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Fatigue manifests as a decline in performance during high-intensity and prolonged exercise. With technological advancements and the increasing adoption of inertial measurement units (IMUs) in sports biomechanics, there is an opportunity to enhance our understanding of running-related fatigue beyond controlled laboratory environments. How have IMUs have been used to assess running biomechanics under fatiguing conditions? Following the PRISMA-ScR guidelines, our literature search covered six databases without date restrictions until September 2024. The Population, Concept, and Context criteria were used: Population (distance runners ranging from novice to competitive), Concept (fatigue induced by running a distance over 400 m), Context (assessment of fatigue using accelerometer, gyroscope, and/or magnetometer wearable devices). Biomechanical outcomes were extracted and synthesised, and interpreted in the context of three main study characteristics (cohort ability, testing environment, and the inclusion of physiological outcomes) to explore their potential role in influencing outcomes. A total of 88 articles were included in the review. There was a high prevalence of treadmill-based studies (n=46, 52%), utilising only 1-2 sensors (n=69, 78%), and cohorts ranged in experience, from sedentary to elite-level runners, and were largely comprised of males (69% of all participants). The majority of biomechanical outcomes assessed showed varying responses to fatigue across studies, likely attributable to individual variability, exercise intensity, and differences in fatigue protocol settings and prescriptions. Spatiotemporal outcomes such as stride time and frequency (n=37, 42 %) and impact accelerations (n=55, 62%) were more widely assessed, with a fatigue response that appeared population and environment specific. There was notable heterogeneity in the IMU-based biomechanical outcomes and methods evaluated in this review. The review findings emphasise the need for standardisation of IMU-based outcomes and fatigue protocols to promote interpretable metrics and facilitate inter-study comparisons. • Studies on running fatigue often focus on male cohorts with few sensors and outcomes. • Fatigue responses vary by individual, environment, and exercise intensity. • Standardized assessment and integrating physiological data could enhance analysis. [ABSTRACT FROM AUTHOR]

Gait kinematics differ between settings and among young and older adults with and without knee osteoarthritis. Out-of-lab data has a variety of walking bout characteristics compared to controlled in-lab settings. The effect of walking bout duration on gait analysis results is unclear, and there is no standardized procedure for segmenting or selecting out-of-lab data for analysis. Do gait kinematics differ by bout duration or setting in young and older adults with and without knee osteoarthritis? Ten young (28.1±3.5 yrs), ten older adults (60.8±3.3 yrs), and ten older adults with knee osteoarthritis (64.1±3.6 yrs) performed a standard in-lab gait analysis followed by a prescribed walking route outside the lab at a comfortable speed with four IMUs. Walking speed, stride length, and sagittal hip, knee, and ankle angular excursion (ROM) were calculated for each identified stride. Out-of-lab strides included straight-line, level walking divided into strides that occurred during long (>60 s) or short (≤60 s) bouts. Gait kinematics were compared between in-lab and both out-of-lab bout durations among groups. Significant main effects of setting or duration were found for walking speed and stride length, but there were no significant differences in hip, knee, or ankle joint ROM. Walking speed and stride length were greater in-lab followed by long and short bout out-of-lab. No significant interaction was observed between group and setting or bout duration for any spatiotemporal variables or joint ROMs. Out-of-lab gait data can be beneficial in identifying gait characteristics that individuals may not encounter in the traditional lab setting. Setting has an impact on walking kinematics, so comparisons of in-lab and free-living gait may be impacted by the duration of walking bouts. A standardized approach for to analyzing out-of-lab gait data is important for comparing studies and populations. • Setting and the duration of walking bouts have an influence on gait kinematics. • Speed and stride length were greater in vs out of lab and for long vs short bouts. • Adults respond similarly to different settings or bout durations regardless of age. [ABSTRACT FROM AUTHOR]

Human behavior patterns involve mutual interactions among psychology, physiology, and stress, which are all associated with gait at different grades. The study aims to reveal the interrelationship among personality, mental workload, and gait patterns by capturing gait variations using inertial sensors. It also assesses individual personality traits and simulates stress to construct a gait classification model. Sixty participants were instructed to perform regular, low, and high mental workload walking on the corridor to simulate a natural setting walking. Meanwhile inertial measurement units (IMUs) were placed on eight body parts. Mental workload was induced using the auditory n-back task, and their Big Five personality traits were evaluated. Gait data from IMUs were categorized into nine classifications of average, low, and high Big Five Inventory scores with three levels of mental workload walking. Subsequently, the segmentation gait data were used as input features for classifications in deep learning models, employing a sliding window long short-term memory network for nine classifications for different personality dimensions. The results indicated average accuracies of nine classifications were 83.6 % for Openness, 84.4 % for Conscientiousness, 82.0 % for Extraversion, 85.2 % for Agreeableness, and 84.5 % for Neuroticism across all IMU placements. Remarkably, gait data from the lower back IMU achieved the highest model performance, with an average accuracy of 92.7 %, in classifying the different levels of personality and mental workload walking. In contrast, the left wrist and chest showed several misclassifications among regular, low, and high mental workload walking across personality traits. Successful classification can help monitor an individual's mental state in real time and analyze personality dimensions, providing feedback and suggestions. The present study demonstrated that gait characteristics can contribute to more profound and personalized health information. • First one reveals gait variation across personality dimensions and mental workload. • 60 individuals with 8 IMUs completed walking tasks under varied mental workloads. • Over 82 % classification accuracies of five personalities across all IMU placements. • Gait data from the lower back IMU achieved the highest 93 % classification accuracy. [ABSTRACT FROM AUTHOR]

Previous research on the accuracy of mobile measurement systems has focused on parameters related to the whole gait cycle. Specifically, bilateral gait characteristics were primarily used as outcome measures. How accurate are unilateral gait characteristics detected using a mobile system at various fixed walking speeds? Gait analysis during treadmill walking at velocities (VEL) of 2.5 (v1), 4.5 (v2) and 6.5 km/h (v3) was performed in a population of 47 healthy young adults, consisting of 27 females (age: 23 ± 2 years, BMI: 21.4 ± 2.2 kg/m²) and 20 males (age: 22 ± 1 years, BMI: 23.3 ± 3.4 kg/m²). Spatiotemporal gait data were simultaneously determined using an instrumented treadmill (gaitway 3D) and a mobile gait analysis system (RehaGait). Besides VEL, bilateral (stride length [SL], cadence [CAD]) and unilateral (contact duration [CON], single [SS] and double support duration [DS]) outcomes were validated. Across the three VEL investigated, the correlations between both measurement systems were almost perfect in SL and CAD (r > 0.97). In addition, SL significantly differed (p < 0.01) with moderate to large effects, whereby the root mean squared error (RMSE) did not exceed 1.8 cm. RMSE in CAD was not higher than 0.33 spm and statistically significant differences were only present at v1 (d = 0.63). DS was the most erroneous unilateral parameter with values for %RMSE ranging from 9% at v1 to 14% at v3. In CON and SS %RMSE was in a magnitude of 2–4% across all VEL. Furthermore, VEL affected measurement accuracy in unilateral outcomes with moderate to large effects (F (2, 45) > 6.0, p < 0.01, η p 2 > 0.11) with consistently higher differences at lower velocities. Based on the results presented the validity of the mobile gait analysis system investigated to detect gait asymmetries must be questioned. • The device investigated provides accurate results for cadence and stride length. • The estimation of walking speed was also accurate. • Unilateral parameters were revealed to be less accurate than bilateral outcomes. • Double support duration detection showed the lowest level of accuracy. • The accuracy in contact time detection improved with walking speed. [ABSTRACT FROM AUTHOR]

Deterioration of physical function in middle-aged adults is a significant challenge that can lead to increased risk of future falls. However, a screening method for the functional decline in middle-aged adults has not been established. To evaluate the relationship between biomechanical parameters assessed by the timed up and go test (TUG) and locomotive syndrome (LS) in middle-aged adults. The inclusion criterion was: under 65 years of age. A total of 97 volunteers (mean age 51.1 years) participated in this study. An LS test was performed, including a 2-step test, a stand-up test, and a 25-question Geriatric Locomotive Function Scale. The TUG was measured using inertial measurement units (IMUs) at comfortable and fast speeds. We then determined the minimum values for anterior-posterior acceleration and angular velocity around the medial-lateral axis, as well as the maximum values of angular velocity around the vertical axis for the upper trunk and sacrum in a TUG phase. Angular velocity around the vertical axis for upper trunk and sacrum were significantly smaller in LS than non-LS in the turn phase of both speed conditions. For the fast speed condition, the minimum anterior-posterior acceleration for sacrum was greater in LS than in the non-LS condition for the stand-to-sit phase. Angular velocity around the vertical axis for turning and anterior-posterior acceleration from sitting were associated with detection of LS. Turning and sitting movements during TUG should be observed using IMU to screen for physical function decline in middle aged adults. • Locomotive syndrome (LS) has slow turning velocity compared to non-LS. • LS show increased antero-posterior acceleration when going from stand to sit. • TUG measurement using inertial measurement unit is useful for detection of LS in middle age. [ABSTRACT FROM AUTHOR]

Inertial Measurement Units (IMUs) offer a promising alternative to optoelectronic systems to obtain joint lower-limb kinematics during gait. However, the associated methodologies, such as sensor-to-segment (S2S) calibration and multibody optimization, have been developed mainly for, and tested on, asymptomatic subjects. This study proposes to evaluate two personalizations of the methodology used to obtain lower-body kinematics from IMUs with pathological subjects: S2S calibration and multibody optimization. Based on previous studies, two decision trees were developed to select the best (in terms of accuracy and repeatability) S2S methods to be performed by the patient given his/her abilities. The multibody optimization was personalized by limiting the kinematic chain range of motion to the results of the subject's clinical examination. These two propositions were tested on 12 patients with various gait deficits. The patients were equipped with IMUs and reflective markers tracked by an optoelectronic system. They had to perform the postures and movements selected by the decision trees then walk back and forth along a walkway. Gait kinematics obtained from the IMUs directly (referred to as Direct kinematics), and after multibody optimization performed via the OpenSim software using the generic range of motion (referred to as Generic Optimized kinematics), and using the personalized range of motion (referred to as Personalized Optimized kinematics) were compared to those obtained with the Conventional Gait Model through Root Mean Square Errors (RMSE), Correlation Coefficients (CC) and Range of Motion differences (ΔROM). The RMSEs were smaller than 8.1° in the sagittal plane but greater than 7.4° in the transverse plane. The CCs, between 0.71 and 0.99 in the sagittal plane, deteriorate sharply in the frontal and transverse planes where they only measured between 0.15 and 0.68. The ΔROMs were mostly below 8.3°. Optimized kinematics did not improve compared to Direct kinematics. The personalization of the proposed S2S calibration method showed encouraging results, whereas multibody optimization did not impact the resulting joint kinematics. • Decision trees were built to select the most appropriate S2S calibration. • The kinematic chain RoM was limited to the results of the clinical examination. • The results were similar to those obtain with asymptomatic subjects. • Multibody optimization personalization had no noticeable impact on the kinematics. [ABSTRACT FROM AUTHOR]

The miniaturization of MEMS-based inertial measurement units (IMUs) facilitates their widespread use in a growing number of application domains. The fundamental sensor fusion task of orientation estimation is a prerequisite for most further data processing steps in inertial motion tracking, such as position and velocity estimation, joint angle estimation, and 3D visualization. Errors in the estimated orientations severely affect all further processing steps. Recent systematic comparisons of existing algorithms show that out-of-the-box accuracy is often low and that application-specific tuning is required to obtain high accuracy. In the present work, we propose and extensively evaluate a quaternion-based orientation estimation algorithm that is based on a novel approach of filtering the acceleration measurements in an almost-inertial frame and that includes extensions for gyroscope bias estimation and magnetic disturbance rejection, as well as a variant for offline data processing. In contrast to all existing work, we perform an extensive evaluation, using a large collection of publicly available datasets and eight literature methods for comparison. The proposed method consistently outperforms all eight literature methods and achieves an average RMSE of 2.9°, while the errors obtained with literature methods range from 5.3° to 16.7°. This improved accuracy with respect to the state of the art is observed not only in average but also for each of several different motion characteristics, as well as for gyroscope bias estimation. Since the evaluation was performed with one single fixed parametrization across a very diverse dataset collection, we conclude that the proposed method provides unprecedented out-of-the-box performance for a broad range of motions, sensor hardware, and environmental conditions. This gain in orientation estimation accuracy is expected to advance the field of IMU-based motion analysis and provide performance benefits in numerous applications. The provided open-source implementation makes it easy to employ the proposed method. • 6D+9D inertial sensor fusion with bias estimation and magnetic disturbance rejection. • Extensive validation against 8 literature methods on 6 diverse reference datasets. • Results show unprecedented accuracy with 2–5 times lower errors than existing filters. • VQF uses a single generic parametrization across all datasets, tuning is obsolete. • Easy-to-use open-source implementation provided in C++, Python, and Matlab. [Display omitted] [ABSTRACT FROM AUTHOR]

Reverse total shoulder arthroplasty (rTSA) is utilized for a variety of indications, but most commonly for patients with rotator cuff arthropathy. This procedure reduces pain, improves satisfaction, and increases clinically measured range of motion (ROM). However, traditional clinical ROM measurements captured via goniometer may not accurately represent 'real-world' utilization of ROM. In contrast, inertial measurement units (IMUs) are useful for establishing ROM outside the clinical setting. We sought to measure 'real-world' ROM after rTSA using IMUs. A previously validated IMU-based method for continuously capturing shoulder elevation was used to assess 10 individuals receiving rTSA (1M, 82 ± 5 years) and compared to a previously captured 10 healthy individuals (4M, 69 ± 20 years) without shoulder dysfunction. Control subject data were previously collected over 1 week of continuous use. Patients undergoing rTSA donned sensors for 1 week pre-rTSA, 6 weeks at 3 months post-rTSA following clearance to perform active-independent ROM, and 1 week at 1 year and 2 years post-rTSA. Shoulder elevation was computed continuously each day. Daily continuous elevation was broken into 5° angle 'bins' (eg, 0-5°, 5-10°, etc.) and converted to percentage of the total day. IMU-based outcome measures were ROM binned percent (as described previously) and maximum/average elevation each week. Clinical goniometric ROM and patient-reported outcome measures were also captured. No differences existed between patient and healthy control demographics. While patients showed improvement in American Shoulder and Elbow Surgeon (ASES) score, pain score, and goniometric ROM, IMU-based average and maximum elevation were equal between control subjects and patients both pre- and post-rTSA. The percent of time spent above 90° was equal between cohorts pre-rTSA, rose significantly at 3 months post-rTSA, and returned to preoperative levels thereafter. Although pain, satisfaction, and ROM measured clinically may improve following rTSA, real-world utilization of improved ROM was not seen herein. Improvements during the acute rehabilitation phase may be transient, indicating longer or more specific rehabilitation protocols are necessary to see chronic improvements in post-rTSA movement patterns. [ABSTRACT FROM AUTHOR]

In this study, we aimed to classify 7 cow behavior patterns automatically with an inertial measurement unit (IMU) using a fully convolutional network (FCN) algorithm. Behavioral data of 12 cows were collected by attaching an IMU in a waterproof box on the neck behind the head of each cow. Seven behavior patterns were considered: rub scratching (leg), ruminating-lying, lying, feeding, self-licking, rub scratching (neck), and social licking. To simplify the data and compare classification performance with or without magnetometer data, the 9-axis IMU data were reduced using the square root of the sum of squares to develop 2 datasets. Comparing the classification accuracy of the 3 models using a window size of 64 with 6-axis data and a window size of 128 with both 6-axis and 9-axis data, the best overall accuracy (83.75%) was achieved using the FCN model with a window size of 128 (12.8 s) using all IMU data. This model achieved classification accuracies of 83.2, 96.5, 92.8, 98.1, 82.9, 87.2, and 45.2% for ruminating-lying, lying, feeding, rub scratching (leg), self-licking, rub scratching (neck), and social licking, respectively. As a sequence of varied and intensive movement, the classification accuracy of behavior patterns related to skin disease was lower; better classification of these behavior patterns could be achieved with full IMU data and a larger window size. In the future, additional data will take into account different data types, such as audio and video data, to further enhance performance. In addition, an adaptive sliding window size will be used to improve model performance. [Display omitted] [ABSTRACT FROM AUTHOR]

Background: Inertial measurement units are increasing used for monitoring joint motion, but there is a need to demonstrate their suitability during hours-long continuous use, as well as a need for validated methods to count arm cycles and provide descriptions of typical cycles.Research Question: Do IMU sensors and rainflow counting have sufficient accuracy for tracking and cycle counting of hours-long continuous arm motion? If so, what are the cycle rates of normal arm ADL and is there a representative cycle that can serve as a 'gait cycle' for the arm?Methods: IMU sensors continuously tracked a robot performing 8 h of simulated cyclic arm motion. Error in the angle measurements was regressed against time to determine the rate of error and the total accumulated error. Additionally, the cycle count accuracy of rainflow, peak/valley, and Fourier transform counting methods was evaluated.Results: Over 8 h the IMU measurements accumulated a maximum 0.473° of error and the rainflow method counted cycles with less than 1% error. Applying rainflow counting to normal shoulder ADL resulted in an average rate of 533 elevation cycles per day.Tabulating the ADL cycles by mean and range values into a matrix and calculating the centroid, the single best values representing arm elevation cycles were a mean of 22.4° and a range of 21.6°.Significance: IMU sensors can track arm motion for 8 h with little increase in error, though during longer durations error may reach unacceptable levels. For normal arm ADL, the rainflow determined count of arm elevation full-cycles differed from previous estimates based on peak/valley counting. From the rainflow counting, a single cycle representation of cycle mean and range was determined that can be used as a 'gait cycle' for the shoulder. [ABSTRACT FROM AUTHOR]

This paper introduces a new algorithm (Gravity & Magnetic North Vector correction — Double SLERP, or "GMV-D") to estimate the orientation of a MEMS Magnetic/Angular-Rate/Gravity (MARG) sensor module using sensor fusion in the context of a real-time hand tracking application, for human–computer interaction purposes. Integrated MEMS MARG modules are affordable, small, light and consume minimal power. As such, there is interest in using them for monitoring the orientation of various body segments, to which they can be attached (e.g., the finger segments of a gloved hand). However, each of the 3 types of signals they provide has proven insufficient to yield robust orientation estimates, particularly in regions of space where the geomagnetic field is distorted. The significance (main contribution) of the approach we present is the computation of a final orientation estimate that uses all the signals generated by inexpensive (e.g., less than 20 USD, in large quantities) integrated MEMS MARG modules but weighs their contributions with simple, real-time-updatable parameters that prevent erroneous corrections when the pre-conditions for their valid use are not met. This will enable the use of inexpensive integrated MEMS MARG modules for hand tracking applications in human–computer interaction and other areas of work where tracking the orientation of body segments in real-time is important. In each iteration, GMV-D defines an initial orientation estimate from integration of gyroscopic signals ("dead reckoning"), and also calculates accelerometer-based and magnetometer-based corrections. These corrections are defined on the assumptions that the module is near static and affected by an undistorted geomagnetic field. Because these assumptions are seldom fully met simultaneously, the information fusion challenge is to apply each correction only to the extent that its corresponding pre-conditions are met, as inappropriate corrections will introduce significant error in the future orientation estimates. To achieve this, GMV-D develops an accelerometer correction trustworthiness parameter, 0 ≤ α ≤ 1, and a magnetometer correction trustworthiness parameter, 0 ≤ μ ≤ 1, both of which are updated on a sample-by-sample basis and are available at each iteration of the algorithm. The information fusion phase of the algorithm implements the corrections in a two-tiered application of Spherical Linear Interpolation (SLERP) of the quaternions representing the initial dead reckoning estimate and the available corrections, scaled according to their corresponding levels of trustworthiness. GMV-D was evaluated in comparison to 2 other orientation correction approaches (Kalman Filtering and GMV-S) and contrasted with 2 contemporary complementary filter approaches (Madgwick , Mahony). The results confirm that GMV-D displayed better orientation estimation performance when the algorithms operated in an area with known distortion of the geomagnetic field. • Non-Kalman Filter fusion of Magnetic, Angular-Rate and Gravity (MARG) signals. • Fusion achieved by 2 weighted spherical linear interpolations (SLERP) of quaternions. • Accelerometer and magnetometer trustworthiness weights updated every sampling instant. • Weight given to magnetometer information is progressively mapped across space. • Demonstrated more robust performance in magnetically distorted regions of space. [ABSTRACT FROM AUTHOR]

• Single-foot multiconditional constraints, adaptive step size constraints, bipedal maximum heading difference constraints, and millimeter-wave radar-based inter-foot ranging constraints are fused into a bipedal multiconditional constraint algorithm. • Practical experiments were conducted to verify that the algorithm does not over-adjust the heading under smooth paths, as well as to verify that the algorithm under-corrects the heading at sharp turns. • The proposed heading estimation algorithm, which is suitable for very stealthy foot-tethered positioning systems, has great potential for commercial applications in the field of professional pedestrian positioning. Heading estimation is crucial in pedestrian navigation systems based on IMUs, but accuracy often suffers degradation due to error accumulation. This study introduces a novel heading estimation algorithm using dual-foot-mounted IMUs with a millimeter-wave radar to enhance accuracy. The algorithm includes a Single-foot Multiconditional Constraint Algorithm (SMC-A), adaptive step length constraint, and bipedal maximum heading difference constraint. Firstly, the SMC-A utilizes zero speed correction, zero angular rate updates, and self-observing heading error corrections through extended Kalman filtering. Then, the adaptive step length constraint limits pedestrian motion, and the bipedal constraint corrects heading errors. Finally, the Bipedal Multiconditional Constraint Algorithm (BMC-A) incorporates millimeter-wave radar measurements of foot distance. Experiments show heading deviations of 3.74° and 4.03° are obtained for the left and right feet over a 30-minute, 1.45 km trial. The new algorithm improves heading accuracy by 68.19 % and 70.67 % for the left and right feet, respectively, compared to traditional SMC-A. [ABSTRACT FROM AUTHOR]

Individuals with unilateral hip osteoarthritis walk with kinematic and spatiotemporal compensations compared to healthy individuals. Our purpose was to determine associations between gait, pain, and functional performance during the six-minute walk test. Trunk and hip kinematics and spatiotemporal gait outcomes were recorded from individuals with unilateral hip osteoarthritis using inertial sensors (Xsens Technologies). Pain was collected prior to and at the end of the six-minute walk test. Paired t -tests were conducted to evaluate gait between limbs and between the first and final minutes of walking. Correlations were conducted between gait, pain, and six-minute walk test performance. Nineteen participants (8 females, age: 63 ± 5 yrs., BMI: 29.0 ± 4.5 kg/m2) completed the study. Between-limb differences in hip flexion, hip extension, and trunk forward flexion peak angles were observed during the six-minute walk test (P <.05). Participants demonstrated an increase in trunk forward flexion of the osteoarthritis side (t = −2.34, P =.031) and a bilateral decrease in stride length (osteoarthritis limb: t = 2.98, P =.008, non- osteoarthritis limb: t = 3.17, P =.006) from the first to the final minute of walking. Greater pain was associated with greater osteoarthritis limb hip extension (first minute: r = −0.506, P =.027, final minute: r = −0.53, P =.020) and greater hip abduction (r = 0.46, P =.046) during the final minute of walking. Gait compensations increase throughout the six-minute walk test, and pain associates with hip kinematics during the six-minute walk test. Wearable technology may allow for more accurate clinical movement assessments. • Gait compensations increase during a walking task in people with hip osteoarthritis. • Walking gait patterns associate with pain in people with hip osteoarthritis. • Functional performance associates with gait in people with hip osteoarthritis. [ABSTRACT FROM AUTHOR]

The calcaneofibular ligament, a component of the lateral ligament complex of the ankle joint, plays an essential role in ankle-joint stability. To understand the mechanism of sprain-induced calcaneofibular ligament injury, the effect of ankle positions on calcaneofibular ligament tension needs to be ascertained. We propose a convenient method that combines stretchable strain sensors and an inertial measurement unit for simulative tension analysis of the calcaneofibular ligament in formalin-fixed cadavers. The stretchable strain sensor was pre-stretched approximately 1.3 times and, then set along the direction of the calcaneofibular ligament; a capacitance value from the sensor was used as a parameter to reflect the tension generated. Accurate three-axial inertial measurement unit-based monitoring of joint angles was undertaken for ten cadaveric ankles in measurements at 10° intervals from 30° plantarflexion to 20° dorsiflexion, followed by the investigation of additional effects with 10° inversion and eversion. Two-way repeated-measures ANOVA revealed a significant interactive effect for plantar/dorsiflexion × inversion/eversion and main effects for plantar/dorsiflexion and inversion/eversion. Post hoc pairwise analysis confirmed that 20° dorsiflexion or 10° inversion induces tension, whereas 10° eversion causes relaxation. Moreover, a promotional interactive effect by 20° dorsiflexion and 10° inversion and an offsetting effect by 10° eversion to 20° dorsiflexion were revealed. The measured values showed high levels of reliability and reproducibility (intraclass correlation coefficient [1,1] = 0.89). These results appropriately demonstrate the tensile action of calcaneofibular ligament. The novel approach investigated herein potentially opens new avenues for precise ligament-function evaluation. • We proposed a new practical method for functional analysis of cadaveric ligaments. • This easy-to-install method can be applied to formalin-fixed specimens. • Typical, significant calcaneofibular ligament behaviors were detected. • Unknown complicated tensile actions of calcaneofibular ligament were detected. • This strategy potentially opens a new avenue to evaluate precise ligament function. [ABSTRACT FROM AUTHOR]

Handwriting is essential for the development of fine motor skills in children. Handwritten character recognition has the potential to facilitate natural human–machine interactions, aiding in the digitization of handwritten text for educational environments such as smart classrooms. Electromyography (EMG), a widely recognized biosignal, captures complex electrical patterns generated by muscle activity during handwriting movements, offering detailed insights into neuromuscular function. This study proposes an efficient multi-modal handwritten character recognition pipeline integrating physiological (EMG) and Inertial Measurement Unit (IMU) sensors. EMG signals provide valuable information about muscle function and activation patterns, while IMU sensors track motion and orientation associated with handwriting. The proposed system employs feature fusion, combining data from both sensor types. A cooperative game theory-based feature ranking method and a modified deep auto-encoder architecture are utilized for enhanced data representation and feature extraction. A novel dataset comprising 26 isolated handwritten English alphabets written on a whiteboard was collected for experimental validation. The proposed pipeline demonstrates high efficiency, achieving a classification accuracy of 99.01% for the isolated handwritten characters. Additional performance metrics, including the Matthews correlation coefficient (98.77) and Kappa Score (98.97), were assessed to validate the model's effectiveness. The fusion of EMG and IMU data enhances system robustness, offering significant potential for digitizing handwritten notes in smart classrooms and for clinical handwriting analysis, including the diagnosis and monitoring of Alzheimer's disease [ABSTRACT FROM AUTHOR]

An increased focus on automation in the aerospace industry has led to a demand for flexible manipulators that can achieve high trajectory following accuracy for tasks like machining, joining, and painting. These tasks require an accurate, real-time state estimation of the robot's end-effector. Currently, the most accurate methods of estimating the end-effector state involve using high-end position and orientation sensors such as laser trackers and camera-based metrology systems and tend to focus on accurately estimating the end-effector position and orientation. This paper investigates the potential benefits and practical considerations in supplementing the data from these kinds of sensors with acceleration and angular velocity measurements obtained from an inertial measurement unit (IMU) to improve the overall robot end-effector state estimation including its velocity and angular acceleration. The sensor fusion is achieved using a Kalman Filter for motions with no rotation and using a Particle Filter for motions with rotation. Simulation results indicate that up to 95% improvement in velocity estimation accuracy, and up to 45% improvement in angular acceleration estimation accuracy can be achieved by fusing IMU data with laser tracker data. In addition, the paper analyzes the thresholds of sensor accuracy beyond which fusion no longer provides significant benefits. Finally, hardware experiments are conducted using a fusion of robot encoder data with IMU measurements. These experiments indicate up to 40% improvement in velocity estimation accuracy from the sensor fusion. [ABSTRACT FROM AUTHOR]

Working status in walking and personal identity are critical to worker safety and management in the workplace, which could be recognized through gait patterns. However, detecting gait data from images could violate workers' privacy and, most importantly, cause confidentiality problems. The present study proposed utilizing gait data from environmental surface detection to classify walking status and personal identification simultaneously. In addition to general walking, twenty participants were instructed to simulate three walking scenarios: wearing a backpack, pulling a suitcase, and watching a video on the cell phone. Two datasets containing ground reaction forces and center of pressure from a force plate (FP) and acceleration and angular velocity data from inertial measurement units (IMU) embedded in a walking surface were utilized as input features for eighty classifications. These datasets were then processed using the matching network of few-shot learning and sliding-window long short-term memory (SW-LSTM) models. The best accuracy of the few-shot learning model was 98% for four walking scenarios and 99% for an 80-class classification. In comparison, the SW-LSTM model was 92% for the FP features and 99% for the IMU features to identify twenty individuals. The present study demonstrated the IMU environmental device can capture gait features to classify walking status and identify individuals. Additionally, the SW-LSTM model showed potential for instant identification of gait cycles, rather than relying on a few-shot learning method until the entire gait cycle is completed. This practical approach provides an alternative for gait pattern recognition and deep learning model selection in the industry. [ABSTRACT FROM AUTHOR]

• Lameness is common in dairy cows and causes decreased welfare and production loss. • Visual lameness detection is complicated; reliable automatic methods are needed. • Equine lameness is extensively studied; we used that knowledge to study lame cows. • We identified several kinematic features which may be used to detect lameness in cows. • This knowledge can be used to develop automatic lameness detection systems. Lameness is a common issue on dairy farms, with serious implications for economy and animal welfare. Affected animals may be overlooked until their condition becomes severe. Thus, improved lameness detection methods are needed. In this study, we describe kinematic changes in dairy cows with induced, mild to moderate hindlimb lameness in detail using a "whole-body approach". Thereby, we aimed to identify explicable features to discriminate between lame and non-lame animals for use in future automated surveillance systems. For this purpose, we induced a mild to moderate and fully reversible hindlimb lameness in 16 dairy cows. We obtained 41 straight-line walk measurements (containing > 3 000 stride cycles) using 11 inertial measurement units attached to predefined locations on the cows' upper body and limbs. One baseline and ≥ 1 induction measurement(s) were obtained from each cow. Thirty-one spatial and temporal parameters related to limb movement and inter-limb coordination, upper body vertical displacement symmetry and range of motion (ROMz), as well as pelvic pitch and roll, were calculated on a stride-by-stride basis. For upper body locations, vertical within-stride movement asymmetry was investigated both by calculating within-stride differences between local extrema, and by a signal decomposition approach. For each parameter, the baseline condition was compared with induction condition in linear mixed−effect models, while accounting for stride duration. Significant difference between baseline and induction condition was seen for 23 out of 31 kinematic parameters. Lameness induction was associated with decreased maximum protraction (−5.8%) and retraction (−3.7%) angles of the distal portion of the induced/non-induced limb respectively. Diagonal and lateral dissociation of foot placement (ratio of stride duration) involving the non-induced limb decreased by 8.8 and 4.4%, while diagonal dissociation involving the induced limb increased by 7.7%. Increased within-stride vertical displacement asymmetry of the poll, neck, withers, thoracolumbar junction (back) and tubera sacrale (TS) were seen. This was most notable for the back and poll, where a 40 and 24% increase of the first harmonic amplitude (asymmetric component) and 27 and 14% decrease of the second harmonic amplitude (symmetric component) of vertical displacement were seen. ROMz increased in all these landmarks except for TS. Changes in pelvic roll main components, but not in the range of motion of either pitch or roll angle per stride, were seen. Thus, we identified several kinematic features which may be used in future surveillance systems. Further studies are needed to determine their usefulness in realistic conditions, and to implement methods on farms. [ABSTRACT FROM AUTHOR]

With increased age, walking without tripping requires greater cognitive demand. Therefore, it may be beneficial for training interventions to address and incorporate aspects of cognitive load. The purpose of this study was to compare a semi-immersive virtual reality treadmill training (VRTT) and conventional treadmill training (CTT) on obstacle clearance and trip hazard in older adults. Obstacle clearance parameters were measured with foot-mounted inertial measurement units (IMUs) and a Zeno pressure walkway. All data were processed and analyzed through custom Matlab scripts. Obstacle step height mean decreased (p =.003) in the lead limb following both training interventions. Additional significant changes were found in pre- and post-obstacle distance mean following both training interventions. Furthermore, significant correlations were found between demographic, cognitive, and functional mobility assessments and changes in dependent measures. The findings suggest that both the VRTT and CTT interventions may provide a reduction in trip risk in older adults, although through different methods. [ABSTRACT FROM AUTHOR]

• ·The feasibility of wearable inertial measurement units (IMUs) to assess fall risk of aged workers. • ·Developing machine learning (ML) models using inertial motion data and FGA scores. • ·Walking motion demonstrates potential for utilizing ML to assess fall risk. • ·Wearable IMUs combined with ML can be useful for fall risk assessment of aged workers. Falls are the major cause of fatal and nonfatal incidents among aged workers. Estimating individual fall risk is crucial for occupational protection and public health. This study aimed to develop a fall-risk assessment method based on Machine Learning (ML) and Inertial Measurement Units (IMUs). A total of 28 aged workers (60 to 80 years old) participated in this study, recruited from community sanitation workers and janitors. They were categorized into high and low fall-risk groups based on functional gait assessment. Each participant performed 5 sets of motion experiments in the lab, covering 6 daily motions. We gathered their kinematic data with IMUs for training ML-based fall-risk assessment models. Five classic ML classifiers were optimized using grid search, and a Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) hybrid deep neural network was built to recognize walking motion from kinematic data for input into the fall-risk assessment model. The results demonstrated that ML classifiers trained with walking motion data exhibited superior performance. Cross-validation revealed that the optimized Random Forest classifier, with IMU on the right upper leg, reached 91.3 % accuracy. Additionally, the CNN-LSTM models were evaluated using F1-score, which can balance accuracy and coverage of model performance evaluation. The CNN-LSTM achieved a maximum F1-score of 95.18 %, proving their effectiveness in extracting walking motion data. These findings indicate that the developed IMU-based ML method exhibits excellent performance in assessing fall risk, potentially allowing aged workers to assess their fall risk before clinical diagnosis by embedding it in wearable IMUs. [ABSTRACT FROM AUTHOR]

Purpose: To analyze temporal and kinematic parameters of chronic Low-Back Pain (cLBP) subjects compared to healthy subjects during Timed Up and Go Test (TUG) execution implemented with an Inertial Measurement Unit and to explore the correlations of those parameters with pain and disability.Methods: Observational cross-sectional study. Thirty-one subjects with cLBP [(19 females - 61%), mean age 61 ± 19] were allocated to the case group, and 14 healthy [(10 females - 71%), mean age 62 ± 6] subjects to the control group. Instrumented TUG was administered to both groups. The Roland Morris Disability Questionnaire and Numerical Pain Rating Scale (NPRS) were also administered for disability and pain assessment in the case group.Results: Mean TUG time to completion [12.2 ± 3.5 s for cLBP; 8.1 ± 0.9 s for healthy] and the most of sub-phases duration significantly differed between groups (p < 0.05). As for kinematic parameters, significant differences (p < 0.05) were mainly retrieved in acceleration components during the sit-to-stand and stand-to-sit phase, with the cLBP group showing lower accelerations. Significant correlation [from strong (ρ = 0.75 of time to completion) to moderate (ρ = 0.43 of sit-to-stand)] was observed between RMQD score and all temporal parameters and with most of the kinematic parameters. No correlation with NPRS score was found.Conclusions: Instrumented TUG application into a cLBP population provides valuable information about movement behaviors with a deeper assessment of objective functional impairment and disability in respect of the classical stop-watch outcome of TUG, possibly allowing a better design of the rehabilitative intervention. [ABSTRACT FROM AUTHOR]

This study investigates the use of tibia-mounted inertial measurement units (IMUs) as an alternative to upper trunk-mounted IMUs for assessing lower limb training magnitudes and asymmetries in Badminton players. Cross-Sectional Study. Youth athlete training environment. Thirty-three adolescent Badminton players, grouped based on injury history (non-injured = 19, bilateral = 6, unilateral = 8). Players wore 1 upper trunk-mounted and 2 tibia-mounted IMUs during simulated match-play. Modified vector magnitudes were assessed to identify if the IMUs can discriminate between injury history groups to assess the device location sensitivity, determine to what extent players exhibit movement asymmetry within the sport, and explore if asymmetries exist within groups with injury history. Upper trunk-mounted IMUs could not distinguish between injury history groups. Statistically significant asymmetries were observed in the non-injured group, however these were below the 10% threshold for clinical asymmetry. No significant asymmetries were observed in the bilaterally injured group, while statistically significant asymmetries were observed in the unilaterally injured group, which were above the 10% threshold for clinical asymmetry. These results suggest that direct limb specific IMU measurement offers a method to suitably assess training magnitudes and asymmetry within a sporting performance, rather than isolated non-sport specific testing. • Upper trunk-mounted IMUs could not distinguish athletes based on injury history. • Tibia-mounted IMUs detected significant asymmetries (<10%) in non-injured athletes. • Significant asymmetries (>10%) were detected in unilaterally-injured athletes. • Limb specific IMUs suitable to assess lower limb training magnitudes and asymmetry. [ABSTRACT FROM AUTHOR]

Background: Body-worn inertial measurement unit (IMU) sensors have been widely used in postural stability and balance studies because of their low cost and convenience. In most of these studies, a single IMU sensor is attached to a waist belt near the body's center of mass. Some populations such as pregnant women, however, may find a waist belt challenging in terms of fit and comfort. For this reason it may be useful to identify an alternative location for placement of an IMU and a more comfortable means for attaching the sensor to the body. Research question Does placing an IMU sensor in a pendant worn around the neck permit discrimination between conditions with varying postural stability?Methods: Twenty-six healthy participants performed three standing tasks (double-leg, tandem, and single-leg standing) under eyes-open and eyes-closed vision conditions to preliminarily assess the ability of the pendant sensor to discriminate between balance conditions. Discrimination based upon data from a belt-mounted IMU was assessed in the same trials. Differences in standard deviation of acceleration components, sway area, and jerkiness due to trial condition and sensor were evaluated using analysis of variance followed by post hoc comparisons. These data were also incorporated into receiver-operator characteristic (ROC) curve analysis to assess the effectiveness of each sensor at discriminating between conditions.Results: Stability was found to vary across conditions, but there was no interaction between stability and sensor location (all p ≥ 0.323). ROC curve analysis showed that sensors in both locations were good discriminators between conditions. Significance Placing an IMU in a pendant may be feasible for studying and monitoring postural instability. This approach may be especially valuable when considering populations for which wearing a belt is uncomfortable. [ABSTRACT FROM AUTHOR]

Background: There is a range of magneto-inertial measurement unit (MIMU) systems commercially available, however sensor specifications and fusion methods vary considerably between manufacturers. Such variability can influence the concurrent validity of MIMUs relative to reference standard measurement devices. Different MIMUs have been compared during static or low-velocity conditions, with higher-velocity movements assessed in robotic-based studies. However, there is a need for the concurrent validity of higher-velocity movements to be established in human-based studies.Research Question: This study aimed to assess the concurrent validity of two commercial MIMU systems (Noraxon and Xsens), relative to a 'gold-standard' retro-reflective motion capture system, when measuring trunk angles during uni-planar range of motion (ROM) and cricket bowling, which involves high-speed, multi-planar movements.Methods: For this criterion-based validity study, both MIMU systems incorporated comparable sensor specifications and employed Kalman filter sensor fusion algorithms. The MIMU based angles were compared with angles derived from concurrently captured three-dimensional retro-reflective data for 10 fast-medium bowlers. Statistical parametric mapping and root mean squared differences (RMSD) were computed for both MIMU systems.Results: One-dimensional statistical parametric mapping showed no significant differences for angles from both MIMU systems when compared with retro-reflective based angle outputs. The MIMU systems produced ROM RMSDs between 1.4 ± 1.0° and 2.6 ± 1.5°. One system displayed RMSDs between 4.6 ± 1.4° and 7.4 ± 1.9° during bowling, indicating functionally relevant differences to retro-reflective derived angles. There were some small but statistically significant differences in RMSDs between the MIMU systems.Significance: MIMU-based angle accuracy is poorer during high-speed, multi-planar movement than uni-planar tasks. Comparable MIMU systems can produce varying measurements during ROM and bowling tasks. It is likely that varying sample rates and sensor fusion algorithm parameters contributed to the differences. [ABSTRACT FROM AUTHOR]

Because nerve injury of muscles around the shoulder can be easily disguised by "trick movements" of the trunk, shoulder dysfunction following brachial plexus injury is difficult to quantify with conventional clinical tools. Thus, to evaluate brachial plexus injury and quantify its biomechanical consequences, we used inertial measurement units, which offer the sensitivity required to measure the trunk's subtle movements. We calculated 6 kinematic scores using inertial measurement units placed on the upper arms and the trunk during 9 functional tasks. We used both statistical and machine learning techniques to compare the bilateral asymmetry of the kinematic scores of 15 affected and 15 able-bodied individuals (controls). Asymmetry indexes from several kinematic scores of the upper arm and trunk showed a significant difference (P <.05) between the affected and control groups. A bagged ensemble of decision trees trained with trunk and upper arm kinematic scores correctly classified all controls. All but 2 patients were also correctly classified. Upper arm scores showed correlation coefficients ranging from 0.55-0.76 with conventional clinical scores. The proposed wearable technology is a sensitive and reliable tool for objective outcome evaluation of brachial plexus injury and its biomechanical consequences. It may be useful in clinical research and practice, especially in large cohorts with multiple follow-ups. [ABSTRACT FROM AUTHOR]

Visual-inertial odometry (VIO) is a vital technique used in robotics, augmented reality, and autonomous vehicles. It combines visual and inertial measurements to accurately estimate position and orientation. Existing VIO methods assume a fixed noise covariance for the inertial uncertainty. However, accurately determining in real-time the noise variance of the inertial sensors presents a significant challenge as the uncertainty changes throughout the operation leading to suboptimal performance and reduced accuracy. To circumvent this, we propose VIO-DualProNet, a novel approach that utilizes deep learning methods to dynamically estimate the inertial measurement unit (IMU) noise uncertainty in real-time. By designing and training a deep neural network to predict inertial noise uncertainty using only inertial sensor measurements, and integrating it into the VINS-Mono algorithm, we demonstrate a substantial improvement in accuracy and robustness. Our system outperformed constant covariance methods in 9 out of 11 test sequences, with an average improvement of 25% compared to the baseline and a 12.5% improvement over the best constant covariance combination. [ABSTRACT FROM AUTHOR]

Unmanned surface vehicles (USVs) are increasingly appealing for gathering metocean data, including directional sea spectra. This paper presents new developments towards estimating the response amplitude operators (RAOs) of surface vehicles equipped with inertial sensors. The novel approach undertakes the data-driven estimation of vehicle models of the wave-induced heave, roll, and pitch motion dynamics, as required to perform subsequent seakeeping computations. Specifically, a genetic algorithm executes the calibration of available closed-form RAOs for a simplified geometry. The algorithm makes a population of model-fitting parameters evolve towards minimising discrepancies between the predicted and measured response spectra in stationary operational conditions. Trust in the model is eventually increased by screening and merging the best-fitting solutions. Resulting response predictions using high-resolution spectral wave data for the AutoNaut USV demonstrate satisfactory accuracy and robustness in heave and pitch but a worse fidelity in roll, thereby motivating follow-up studies to improve the estimation of roll RAOs. • Inertial motion measurements are collected on board an autonomous marine vehicle. • Directional wave spectra are obtained by dynamical downscaling of hindcast data. • Parameters of the vehicle's seakeeping model are estimated using an elitist genetic algorithm. • Outliers of parameter values are identified and screened out, increasing model robustness. • Resulting predictions of the vehicle's wave-induced heave and pitch responses are accurate. [ABSTRACT FROM AUTHOR]

Precise position recognition systems are actively used in various automotive technology fields such as autonomous vehicles, intelligent transportation systems, and vehicle driving safety systems. In line with this demand, this paper proposes a new vehicle position estimation algorithm based on sensor fusion between low-cost standalone global positioning system (GPS) and inertial measurement unit (IMU) sensors. In order to estimate accurate vehicle position information using two complementary sensor types, adaptive unscented Kalman filter (AUKF), an optimal state estimation algorithm, is applied to the vehicle kinematic model. Since this AUKF includes an adaptive covariance matrix whose value changes under GPS outage conditions, it has high estimation robustness even if the accuracy of the GPS measurement signal is low. Through comparison of estimation errors with both extended Kalman filter (EKF) and UKF, which are widely used state estimation algorithms, it can be confirmed how improved the estimation performance of the proposed AUKF algorithm in real-vehicle experiments is. The given test course includes roads of various shapes as well as GPS outage sections, so it is suitable for evaluating vehicle position estimation performance. [ABSTRACT FROM AUTHOR]

Recognizing postures in construction activities is crucial for preventing long-term work-related illnesses, such as musculoskeletal disorders. While Inertial Measurement Units (IMUs) are commonly used to track workers' movements, their use in posture recognition creates complex networks of sensing nodes and large amounts of data. This leads to high memory consumption and challenges real-time posture recognition efficiency. To address this, the study introduces a novel feature processing method that combines multiple body joint data into plane normal vector quaternions, along with the original joint data. This approach achieves impressive recognition accuracy similar to the original dataset while reducing the feature count by nearly 50%. Validated with a test of six typical postures, the proposed method suggests a new research potential of using equivalent planar vectors for more effective, reliable, and flexible solutions for detecting and diagnosing work-related activities. • An efficient skeleton modeling metric to compress motion data. • A novel feature processing method can convert multiple body joints into plane normal vector quaternions. • The feature count can be reduced by more than 50%. • Adding primitive joints can promote recognition accuracy without dimension expansion. • The marginal accuracy improment reduces with more primitive joints. [ABSTRACT FROM AUTHOR]

• Novel algorithm for internal and external parameters calibration of mecanum wheel omnidirectional mobile platform. • Parameter calibration based on principal component analysis and nonlinear optimization. • The calibration method is theoretically applicable to all types of holonomic robot mobile platforms. Mobile robot positioning, mapping, and navigation systems generally employ an inertial measurement unit to obtain the acceleration and angular velocity of the robot. However, errors in the internal and external parameters of an inertial measurement unit arising from defective calibration directly affect the accuracy of robot positioning and pose estimation. While this issue has been addressed by the mature internal parameters calibration method available for inertial measurement unit, external reference calibration method between the inertial measurement unit and the chassis of a mobile robot are lacking. This study addresses this issue by proposing a novel algorithm for internal parameter calibration of mecanum wheel omnidirectional mobile platform and external parameter calibration of mecanum chassis- inertial measurement unit based on principal component analysis and nonlinear optimization, which is designed for robots equipped with cameras, inertial measurement unit, mecanum wheels, and wheel speed odometers, and functions under the premise of accurate calibrations for the internal parameters of the inertial measurement unit and the internal and external parameters of the camera. All of the internal and external parameters calibrations are conducted using the robot's existing equipment without the need for additional calibration aids. The feasibility of the method is verified by its application to a mecanum wheel omnidirectional mobile platform. The proposed calibration method is thereby demonstrated to guarantee the accuracy of robot pose estimation. [ABSTRACT FROM AUTHOR]

Background: People with from Parkinson's disease (PD) and freezing of gait (FoG) have more frequent falls compared to those who do not freeze but there is no consensus on which, specific objective measures of postural instability are worse in freezers (PD + FoG) than non-freezers (PD-FoG).Research Question: Are functional limits of stability (fLoS) or postural sway during stance measured with wearable inertial sensors different between PD + FoG versus PD-FoG, as well as between PD versus healthy control subjects (HC)?Methods: Sixty-four PD subjects with FoG (MDS-UPDRS Part III: 45.9 ± 12.5) and 80 PD subjects without FoG (MDS-UPDRS Part III: 36.2 ± 10.9) were tested Off medication and compared with 79 HC. Balance was quantified with inertial sensors worn on the lumbar spine while performing the following balance tasks: 1) fLoS as defined by the maximum displacement in the forward and backward directions and 2) postural sway area while standing with eyes open on a firm and foam surface. An ANOVA, controlling for disease duration, compared postural control between groups.Results: PD + FoG had significantly smaller fLoS compared to PD-FoG (p =  0.004) and to healthy controls (p <  0.001). However, PD-FoG showed similar fLoS compared to healthy controls (p =  0.48). Both PD+FoG and PD-FoG showed larger postural sway on a foam surface compared to healthy controls (p =  0.001) but there was no significant difference in postural sway between PD+FoG and PD-FoG.Significance: People with PD and FoG showed task-specific, postural impairments with smaller fLoS compared to non-freezers, even when controlling for disease duration. However, individuals with PD with or without FoG had similar difficulties standing quietly on an unreliable surface compared to healthy controls. Wearable inertial sensors can reveal worse fLoS in freezers than non-freezers that may contribute to FoG and help explain their more frequent falls. [ABSTRACT FROM AUTHOR]

• Portable systems can give reliable measures of range of motion in the upper limb. • Sensor placement needs to be carefully standardized to give repeatable measurements. • Sensor placement on the scapula had the greatest impact on the assessed motion. • Single-plane range of motion measurements are best suited for clinical use. Motion analysis using inertial measurement units (IMU) has emerged as an alternative to optical motion capture. However, the validity and reliability of upper limb measurements varies significantly between studies. The objective of this study was to determine how sensor placement affects kinematic output in the assessment of motion of the arm, shoulder, and scapula. IMUs were placed proximally/distally on arms, and medially/laterally on the scapula, in a group of eleven healthy participants, while performing nine different motion tasks. Linear regressions and mixed models analysed how these different sensor placements affected the estimated joint motion by establishing the linear relationship between sensors placed on the same body segment. The placement of sensors affected the measured kinematic output considerably, most prominent affect was seen for sensor placement on scapula during flexion and abduction, and on forearm during pronation/supination. The slope of the linear regression lines was 2.5 during flexion, 2.7 during abduction, and 1.8 for forearm pronation/supination. The results of this study suggest that the forearm sensor should be placed on the dorsal side of the forearm, at the distal end; the upper arm sensor should be placed laterally, on the distal part of the arm; and the sensor on the scapula should be placed cranially, along the spine of scapula. [ABSTRACT FROM AUTHOR]

The purpose of this study was to use at-home, portable, continuous monitoring technologies to record arm motion and activity preoperatively and postoperatively after reverse total shoulder arthroplasty (RTSA). Thirty-three patients indicated for RTSA were monitored preoperatively and 3 and 12 months postoperatively. Inertial measurement units were placed on the sternum and upper arm of the operative limb, recording humeral motion relative to the torso for the duration of a waking day. Elevation events per hour (EE/h) > 90°, time spent at >90°, and activity intensity were calculated and compared between time points. Patient-reported outcome measures were also collected at all time points. At 3 (P =.040) and 12 (P =.010) months after RTSA, patients demonstrated a significantly greater number of EE/h > 90° compared with preoperatively. There were no significant differences (P ≥.242) in the amount of time spent at different elevation angles at any time point or in arm activity intensity. Overall, 95% of the day was spent at elevation angles < 60°, and 90% of the day was spent in a low- or moderate-intensity state. Pearson correlations demonstrated relationships between forward elevation and the number of EE/h (r = 0.395, P =.001) and the number of EE/h > 90° (r = 0.493, P <.001). After RTSA, patients significantly increase the frequency of arm elevation to higher angles. However, we found no differences in the amount of time spent at different elevation angles. Overall, after RTSA, >95% of the day was spent at elevation angles < 60° and <1% of the day was spent at >90° of elevation. [ABSTRACT FROM AUTHOR]

Background: Despite advances in laboratory-based supervised fall risk assessment methods (FRAs), falls still remain a major public health problem. This can be due to the alteration of behavior in laboratory due to the awareness of being observed (i.e., Hawthorne effect), the multifactorial complex etiology of falls, and our limited understanding of human behaviour in natural environments, or in the' wild'. To address these imitations, a growing body of literature has focused on free-living wearable-sensor-based FRAs. The objective of this narrative literature review is to discuss papers investigating natural data collected by wearable sensors for a duration of at least 24 h to identify fall-prone older adults.Methods: Databases (Scopus, PubMed and Google Scholar) were searched for studies based on a rigorous search strategy.Results: Twenty-four journal papers were selected, in which inertial sensors were the only wearable system employed for FRA in the wild. Gait was the most-investigated activity; but sitting, standing, lying, transitions and gait events, such as turns and missteps, were also explored. A multitude of free-living fall predictors (FLFPs), e.g., the quantity of daily steps, were extracted from activity bouts and events. FLFPs were further categorized into discrete domains (e.g., pace, complexity) defined by conceptual or data-driven models. Heterogeneity was found within the reviewed studies, which includes variance in: terminology (e.g., quantity vs macro), hyperparameters to define/estimate FLFPs, models and domains, and data processing approaches (e.g., the cut-off thresholds to define an ambulatory bout). These inconsistencies led to different results for similar FLFPs, limiting the ability to interpret and compare the evidence.Conclusion: Free-living FRA is a promising avenue for fall prevention. Achieving a harmonized model is necessary to systematically address the inconsistencies in the field and identify FLFPs with the highest predictive values for falls to eventually address intervention programs and fall prevention. [ABSTRACT FROM AUTHOR]

To use clinically available inertial measurement units to quantify the control of linear accelerations at the head and trunk during gait in different sensory conditions in individuals with unilateral vestibular loss. Observational study. Outpatient research laboratory. Individuals (n=13; mean age, 47.6±13.7y; 69% women) 6 weeks after vestibular schwannoma resection surgery and vestibular healthy participants (n=16; mean age, 29.7±5.9y; 56% women). Not applicable. Walking speed normalized, root mean square values of cranial-caudal, medial-lateral, and anterior-posterior directed linear accelerations at the head and the trunk while walking in 2 visual sensory conditions (eyes open and eyes closed). Linear mixed models for each root mean square value were fit on the effects of group, condition, and group by condition. The group by condition effect was used to examine the primary hypothesis that individuals with vestibular loss would experience greater change in triplanar root mean square values at the head and trunk from the eyes open to eyes closed condition compared with the vestibular healthy group. The group by condition effect was found to be significant at the head in the cranial-caudal (β=0.39; P =.002), medial-lateral (β=0.41; P <.001), and anterior-posterior (β=0.43; P <.001) directions. The group by condition effect was also significant in the cranial-caudal (β =0.39; P =.002), medial-lateral (β =0.39; P <.001), and anterior-posterior (β =0.23; P =.002) directions at the trunk. Participants who underwent vestibular schwannoma resection were more impaired in their ability to control accelerations at the head and trunk without visual sensory information than vestibular healthy participants. These impairments were detectable using clinically available inertial measurement units. • Control of the head and trunk are impaired after unilateral vestibular loss. • After unilateral vestibular loss, individuals increase reliance on visual information. • Clinical inertial measurement units detect deficits in acceleration control. [ABSTRACT FROM AUTHOR]

Background: Up to one-third of individuals with a recent spinal cord injury (SCI) and most of the individuals with an incomplete lesion are able to regain partial balance and walking ability after the first-year post-injury. However, most individuals experience injurious falls while standing and frequent losses of balance post-rehabilitation, which can result in physical injuries and a fear of falling.Research Question: Control of balance during quiet standing depends on the integration of sensory information. Since SCI causes sensory and motor impairments, understanding the underlying mechanisms of how postural control is regulated is of significant importance for targeted and guided rehabilitation post-SCI.Methods: We characterized the impact of a variety of challenging conditions on the standing balance for eight participants with incomplete SCI with walking ability compared to twelve age-matched able-bodied individuals using a waist-mounted inertial measurement unit (IMU). We compared balance biomarkers derived from IMUs' readouts under conditions that challenged balance by affecting somatosensory (i.e., standing on hard vs. foam surfaces) and visual (i.e., eyes open vs. closed) inputs. We performed a three-way ANOVA or a Kruskal-Wallis test to characterize changes in postural control post-SCI based on reliance on somatosensory and visual information using balance biomarkers.Results: We observed a reduced stability performance, an increased control demand, and a less effective active correction post-SCI in all standing conditions. Due to impaired somatosensory feedback, individuals with incomplete SCI showed a higher and lower reliance on visual and somatosensory information, respectively, for maintaining balance (p < 0.05).Significance: Using a single waist-mounted IMU, the proposed method could characterize standing balance in individuals with incomplete SCI compared to able-bodied participants. Having high clinical utility and sufficient resolution with discriminatory ability, our method could be used in the future to objectively evaluate the effectiveness of rehabilitative interventions on the balance performance of individuals with SCI. [ABSTRACT FROM AUTHOR]

Background: Inertial measurement units (IMUs) are promising tools for collecting human movement data. Model-based filtering approaches (e.g. Extended Kalman Filter) have been proposed to estimate joint angles from IMUs data but little is known about the potential of data-driven approaches.Research Question: Can deep learning models accurately predict lower limb joint angles from IMU data during gait?Methods: Lower-limb kinematic data were simultaneously measured with a marker-based motion capture system and running leggings with 5 integrated IMUs measuring acceleration and angular velocity at the pelvis, thighs and tibias. Data acquisition was performed on 27 participants (26.5 (3.9) years, 1.75 (0.07) m, 68.3 (10.0) kg) while walking at 4 and 6 km/h and running at 8, 10, 12 and 14 km/h on a treadmill. The model input consists of raw IMU data, while the output estimates the joint angles of the lower body. The model was trained with a nested k-fold cross-validation and tested considering a user-independent approach. Mean error (ME), mean absolute error (MAE) and Pearson correlation coefficient (r) were computed between the ground truth and predicted joint angles.Results: MAE for the DOFs ranged from 2.2(0.9) to 5.1(2.7)° with an average of 3.6(2.1)°. r ranged from 0.67(0.23) to 0.99(0.01) with moderate correlation (0.4≤r<0.7) was found for the hip right rotation and lumbar extension, strong correlation (0.7≤r<0.9) was found for the hip left rotation and ankle right/left inversion while all other DOFs showed very strong correlation (r≥0.9).Significance: The proposed model can reliably predict joint kinematics for walking, running and gait transitions without specific knowledge about the body characteristics of the wearer, or the position and orientation of the IMU relative to the attached segment. These results have been validated with treadmill gait, and have not yet been confirmed for gait in other settings. [ABSTRACT FROM AUTHOR]

• Validated wearable sensors against optical motion capture during sports movements. • System agreement was insensitive to activity dynamics (i.e., slow or fast). • ICC values were higher for the sagittal plane than the transverse or frontal planes. Inertial Measurement Units (IMUs), an alternative to 3D optical motion capture, are growing in popularity to assess sports-related movements. This study validated an IMU system against a "gold-standard" optical motion capture system during common sports movements. Forty-nine healthy adults performed six movements common to a variety of sports applications (cutting, running, jumping, single leg squats, and cross-over twist) while simultaneously outfitted with standard, retroreflective markers and a wireless IMU system. Bias, RMSE, precision, and maximum absolute error (MAE) were calculated to compare the two systems at the lower extremity joints and the trunk in all planes of movement and for all activities. The MAE difference between fast and slow activities for the sagittal, transverse, and frontal planes were 11.62°, 7.41°, and 5.82°, respectively. For bias, the IMU system tended to report larger angles than the optical motion capture system in the transverse and frontal planes and smaller angles in the sagittal plane. Average intraclass correlation coefficients for the sagittal, transverse, and frontal planes were 0.81±0.17, 0.38±0.19, and 0.22±0.37, respectively. When calculating a global bias across all three planes, the IMU system reported nearly identical angles (< 3.5° difference) to the optical motion capture system. The global precision across all planes was 2–6.5°, and the global RMSE was 7–10.5°. However, the global MAE was 11–23°. Overall, and with suggestions for methodological improvement to further reduce measurement errors, these results support current applications and also indicate the need for continued validation and improvement of IMU systems. [ABSTRACT FROM AUTHOR]

Wearable inertial measurement units (IMUs) enable gait analysis in the clinic, but require calibrations that may affect subsequent gait measurements. This study assessed concurrent validity and within-session reliability of gait kinematics measured by a frequently calibrated IMU-based system. Calibration pose accuracy and intra-rater repeatability, and IMU orientation tracking accuracy, were additionally quantified. Calibration poses and gait were recorded in 15 women using IMUs and optical motion capture (OMC) (reference standard) simultaneously. Participants performed six consecutive trials: each comprising a calibration pose and a walk. IMU tracking was assessed separately (once-off) using technical static and dynamic tests. Differences of > 5° constituted clinical significance. Concurrent validity for gait revealed clinically significant between-system differences for sagittal angles (root-mean-square error [RMSE] 6.7°–15.0°; bias −9.3°–3.0°) and hip rotation (RMSE 7.9°; bias −4.2°). After removing modelling offsets, differences for all angles (except hip rotation) were < 5°. Gait curves correlated highly between systems (r > 0.8), except hip rotation, pelvic tilt and -obliquity. Within-session reliability of IMU-measured gait angles was clinically acceptable (standard error of measurement [SEM] < 5°). Calibration poses were repeatable (SEM 0.3°–2.2°). Pose accuracy revealed mean absolute differences (MAD) < 5° for all angles except sagittal ankle, hip and pelvis. IMU tracking accuracy demonstrated RMSE ≤ 2.0°. A frequently calibrated IMU system provides reliable gait measurements; comparing highly to OMC after removing modelling differences. Calibration poses can be implemented accurately for most angles and consistently. IMU-measured gait data are clinically useful and comparable within participants, but should not be compared to OMC-measured data. [ABSTRACT FROM AUTHOR]

Background: Functional ambulation limitations are features of lumbar spinal stenosis (LSS) and knee osteoarthritis (OA). With numerous validated walking assessment protocols and a vast number of spatiotemporal gait parameters available from sensor-based assessment, there is a critical need for selection of appropriate test protocols and variables for research and clinical applications.Research Question: In patients with knee OA and LSS, what are the best sensor-derived gait parameters and the most suitable clinical walking test to discriminate between these patient populations and controls?Methods: We collected foot-mounted inertial measurement unit (IMU) data during three walking tests (fast-paced walk test-FPWT, 6-min walk test- 6MWT, self-paced walk test - SPWT) for subjects with LSS, knee OA and matched controls (N = 10 for each group). Spatiotemporal gait characteristics were extracted and pairwise compared (Omega partial squared - ωp2) between patients and controls.Results: We found that normal paced walking tests (6MWT, SPWT) are better suited for distinguishing gait characteristics between patients and controls. Among the sensor-based gait parameters, stance and double support phase timing were identified as the best gait characteristics for the OA population discrimination, whereas foot flat ratio, gait speed, stride length and cadence were identified as the best gait characteristics for the LSS population discrimination.Significance: These findings provide guidance on the selection of sensor-derived gait parameters and clinical walking tests to detect alterations in mobility for people with LSS and knee OA. [ABSTRACT FROM AUTHOR]

Background: A waist-mounted sensor is an attractive option for detecting initial and end of foot contacts during gait in a clinical setting without disturbing the subject's natural gait.Research Question: To examine the current state of the field regarding waist-mounted sensor algorithms for gait event detection during locomotion in adults.Methods: A scoping review design was used to search peer-reviewed literature or conference proceedings published through October 2018 for algorithms for gait event detection. We analyzed data from the studies in a descriptive manner.Results: In total, 588 potentially relevant articles were selected, of which 14 (171 participants, mean age: 44.0 years) met the inclusion criteria. We identified 15 algorithms developed using biomechanical theories including the inverted pendulum model that represents gait during level walking. Most algorithms estimated gait events using triaxial acceleration data with an absolute error of approximately 50-100 ms in healthy adults. However, there was a large amount of inter-trial heterogeneity, and only a few algorithms were validated in patients with neurological diseases. Lower gait speed reduced the accuracy of gait event estimation.Significance: There was no algorithm that showed outstanding performance in the estimation of gait events during level walking using the waist-mounted sensor. More comparisons of all available algorithms with an established reference standard for one data-set are needed to identify the best algorithms. As patients with pathological conditions display altered trunk acceleration and slower gait speeds, the development of an algorithm that does not rely on particular signal characteristics and is robust for a wide range of gait speeds is needed before a specific algorithm can be recommended as a valid strategy for clinical practice. [ABSTRACT FROM AUTHOR]

• Developing a low cost, portable, easy to use digital technology for sophisticated assessment of gait to improve diagnosis and management of abnormalities at home or in clinics. • A tool to visualize gait that consists of five approaches. ○ Real-time dial visualization. ○ Visualization of individual leg time variation. ○ Visualization of both legs asymmetry. ○ Boxplot visualization. ○ Evaluation considering all features. Gait abnormalities are common in clinical practice and there is a global imperative to improve technologies that facilitate their detection, evaluation, monitoring and management. Real time evaluation using digital technology supports the development of digital healthcare. Currently gait assessment relies on visual observation of structured clinical tests such as the "Timed Get up and Go Test." Gold standard methods such as "Qualisys Motion Capture System" require sophisticated equipment in gait laboratories. These are not widely available due to expense, analysis time and requirement of trained technicians. Developing low cost, portable, easy to use digital technology is important to enable sophisticated assessment of gait at home or in clinics. Common measures for quantification of gait include symmetry angle, ratio and index. These measurements may be difficult to interpret by users as stand-alone values. To facilitate the evaluation and interpretation of locomotive information, a tool to visualize gait in real-time is proposed. The proposed tool consists of five approaches (1: Real-time dial visualization, 2: Visualization of individual leg time variation, 3: Visualization of both legs asymmetry, 4: Boxplot visualization, and 5: Evaluation considering all features). Results show that wearable Inertial Measurement Unit (IMU) can be used for extraction of objective gait features. This system opens possibilities for home-based assessment of gait without the requirement and expense of an elaborate laboratory setup and supports the development of digital healthcare. [ABSTRACT FROM AUTHOR]

• Foot-drop refers to the inability to perform complete ankle dorsiflexion during gait after a stroke. • The data treatment algorithms from inertial measurement units are able to accurately capture kinematic and spatio-temporal gait parameters for post-stroke patients with foot-drop syndrome. • Kinematic and gait parameters obtained from inertial measurement units reflect patient performance. Functional electrical stimulation (FES) can be used for compensation of foot-drop for post-stroke individuals by pre-programmed fixed stimulation; however, this stimulation seems no more effective than mechanical ankle foot orthoses. We evaluated the metrological quality of inertial sensors for movement reconstruction as compared with the gold-standard motion capturing system, to couple FES with inertial sensors to improve dorsiflexion on the paretic side, by using an adaptive stimulation taking into account individuals' performance post-stroke. Adults with ischemic or hemorrhagic stroke presenting foot-drop and able to walk 10 m, were included from May 2016 to June 2017. Those with passive ankle dorsiflexion < 0° with the knee stretched were excluded. Synchronous gait was analyzed with the VICON© system as the gold standard and inertial measurement units (IMUs) worn by participants. The main outcome was the dorsiflexion angle at the heel strike and mid-swing phase obtained from IMUs and the VICON system. Secondary outcomes were: stride length, walking speed, maximal ankle dorsiflexion velocity and fatigue detection. We included 26 participants [18 males; mean age 58 (range 45–84) years]. During heel strike, the dorsiflexion angle measurements demonstrated a root mean square error (RMSE) of 5.5°; a mean average error (MAE) of 3.9°; Bland-Altman bias of − 0.1° with limits of agreement − 10.9° to + 10.7° and good intra-class correlation coefficient (ICC) at 0.87 between the 2 techniques. During the mid-swing phase, the RMSE was 5.6; MAE 3.7°; Bland-Altman bias − 0.9° with limits of agreement − 11.7° to + 9.8° and ICC 0.88. Good agreement was demonstrated for secondary outcomes and fatigue detection. IMU-based reconstruction algorithms were effective in measuring ankle dorsiflexion with small biases and good ICCs in adults with ischemic or hemorrhagic stroke presenting foot-drop. The precision obtained is sufficient to observe the fatigue influence on the dorsiflexion and therefore to use IMUs to adapt FES. [ABSTRACT FROM AUTHOR]

• The hybrid approaches presented a similar performance, overpowering the FCM algorithm. • The SOM + KM is recommended for demanding less computational resources than the SOM + FCM. • Cadence stands out as the most relevant gait aspect to cluster age-related groups. Age-associated changes in walking parameters are relevant to recognize functional capacity and physical performance. However, the sensible nuances of slightly different gait patterns are hardly noticeable by inexperienced observers. Due to the complexity of this evaluation, we aimed at verifying the efficiency of applied hybrid-adaptive algorithms to cluster groups with similar gait patterns. Based on self-organizing maps (SOM), k-means clustering (KM), and fuzzy c-means (FCM), we compared the hybrid algorithms to a conventional FCM approach to cluster accordingly age-related groups. Additionally, we performed a relevance analysis to identify the principal gait characteristics. Our experiments, based on inertial-sensors data, comprised a sample of 180 healthy subjects, divided into age-related groups. The outcomes suggest that our methods outperformed the FCM algorithm, demonstrating a high accuracy (88%) and consistent sensitivity also to distinguish groups that presented a significant difference (p <.05) only in one of the six observed gait features. The applied algorithms showed a compatible performance, but the SOM + KM required less computation cost and, therefore, was more efficient. Furthermore, the results indicate the overall importance of cadence, as a measurement of physical performance, especially when clustering subjects by their age. Such output provides valuable information to healthcare professionals, concerning the subject's physical performance related to his age, supporting and guiding the physical evaluation. [ABSTRACT FROM AUTHOR]

Gait impairment is a hallmark of multiple sclerosis (MS). InertiaLocoGraphy, the quantification of gait with inertial measurement units (IMUs), has been found useful to detect early changes in gait in MS. Still, the potential use of IMUs as a reliable biomarker of disease severity in MS remains unknown. This systematic review and meta-analysis of observational studies aimed to describe IMU protocols used to assess gait in MS patients and calculate the effect sizes of IMU features associated with disease severity scale measures. We searched MEDLINE, Cochrane Central, EMBASE and grey literature to identify articles published before May 2, 2018 that measured gait in MS patients by using IMUs and correlated IMU parameters with disease severity scale measures. We excluded from the meta-analysis articles that did not provide enough data to evaluate the association between IMU parameters and disease severity scale measures. The study was registered with the International Prospective Register of Systematic Reviews on May 2, 2018 (Registration: CRD42018092651) and the protocol was published in Systematic Reviews on January 8, 2019. We included 36 articles in the systematic review and pooled 12 for the meta-analysis. The risk of bias was moderate, with only 2 articles (none included in the meta-analysis) showing a bias score < 50%. Among protocols tested, 2 were predominant (the Timed Up and Go test and 6-min walk test). Speed, step length and step time with IMUs were significantly correlated with the Expanded Disability Status Scale (EDSS) score, and speed and step length were significantly correlated with the Multiple Sclerosis Walking Scale-12 score. IMU measurement has the potential to increase the sensitivity of clinical and performance tests to identify evolution in gait alteration in MS. Kinematic parameters easily accessible with IMUs, such as speed, step length and step duration, can help follow up disease severity in MS individuals with low to medium EDSS score (1.0–4.5). [ABSTRACT FROM AUTHOR]

• Inertial measurement units are reasonably accurate for standing balance evaluation. • Accelerometers are accurate and recommended for standing balance evaluation. • Use of accelerometers eliminates the need for complex sensor fusion algorithms. • Use of accelerometers reduces power-consumption for I-o-T clinical research. • Our methodology enables in-field evaluation of standing balance. Observational balance tests (e.g., Berg Balance Scale) are used to evaluate fall-risk. However, they tend to be subjective, and their reliability and sensitivity can be limited. The use of in-lab equipment for objective balance evaluation has not been common in clinical practice, due to the requirement of an equipped lab space. While inertial measurement units (IMUs) enable objective out-of-lab balance assessment, their accuracy has not been validated. This study aims to investigate the accuracy of IMUs against in-lab equipment for characterizing standing balance. Ten non-disabled individuals participated in a two-minute standing test on a force-plate. Four approaches were used for estimating inter-segmental moments and center of pressure (COP) position in a four-segment model: (1) camera-based bottom-up approach; (2) camera-based top-down approach; (3) IMU-based (accelerometer) top-down approach; and (4) IMU-based (accelerometer and gyroscope) top-down approach. Approaches 2 to 4 resulted in high accuracy compared to the reference, Approach 1. The root-mean-square errors in estimating the segments' orientation, ground reaction forces, COP position, and joint moments were smaller than 0.3°, 0.2 N/kg, 1.5 mm, and 0.016N·m/kg, respectively. Since no significant differences were observed between the accuracy of Approaches 3 and 4, only accelerometer recordings are needed and could be recommended for monitoring standing balance. [ABSTRACT FROM AUTHOR]

This study aimed to (1) figure out the difference in motion smoothness between a nonpathologic shoulder and the other with a rotator cuff tear by measuring the angular velocity using an inertial measurement unit (IMU) sensor and (2) propose the parameters to describe the difference because there is no literature on this topic. We enrolled patients with rotator cuff disease diagnosed by magnetic resonance imaging. The intact shoulder of participants was compared with the shoulder with rotator cuff tear by using IMU on the basis of the parameters that establish motion smoothness: the number of peaks, the peak velocity–to–mean velocity ratio (PV/MV), and the number of sign reversals. In addition, subgroup analysis was performed with respect to tear size (small to medium vs. large to massive). In addition, correlations with the American Shoulder and Elbow Surgeons score and symptom duration (months) were evaluated. Among 24 patients (15 males, 9 females), all 3 parameters for the motion quality of patients with a rotator cuff tear exhibited significant difference. The parameters showed a significant difference between the small to medium tear group and the large to massive tear group. A negative correlation was found between symptom duration and parameters of motion quality, except for PV/MV. Motion quality assessment using IMU showed a marked difference in the shoulder with a rotator cuff tear compared with the intact shoulder. Hence, IMU-based parameters for dynamic motion quality could be considered as an option for assessing the function of the shoulder. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis (OA) is one of the main causes of disability and its frequent hip and knee joint localization requires surgical joint replacement treatment. Patients after total hip (THA) or knee (TKA) arthroplasty often show gait abnormalities, whose comprehension is crucial in order to plan an appropriate rehabilitative treatment. Wearable sensor devices can be a valid tool for gait assessment in clinical practice, being relatively inexpensive and easy to use.Research Question: Does the use of crutches influence the ability of a single inertial measurement unit (IMU), placed on the lower trunk, to correctly record the spatial-temporal gait parameters in patients after recent THA or TKA?Methods: 20 patients walking with crutches after recent THA or TKA and 10 healthy subjects were recruited. Each participant was recorded simultaneously with an IMU and with an optoelectronic motion capture system during 5 consecutive walking tests.Results: Intraclass correlation index of spatial-temporal parameters recorded with the IMU showed moderate to excellent reliability results both for healthy subjects (ICC range 0.626-0.897) and for patients (ICC range 0.596-0.951). In terms of concurrent validity, Pearson's r coefficient of healthy subjects, showed strong to very strong levels of correlations for some spatial-temporal parameters (speed, mean cadence, left and right stride length and stride duration) (r range 0.646-0.977) and very week to moderately week levels of correlation for gait cycle phases (swing, stance, single support and double support) (r range 0.390-0.633). Patients' data analysis showed similar results for general spatial-temporal parameters (r range 0.704-0.986) and slightly lower values for gait cycle phases (r range 0.077-0.464).Significance: We can consider the single IMU as a reliable tool for the detection of some spatial-temporal gait parameters. Crutches seem to interfere with the detection of the gait cycle phases. [ABSTRACT FROM AUTHOR]

Background: Wearable sensors have allowed researchers to instrument tests of gait-related mobility, including the widely used timed 'up-and-go' test (TUG). Currently, there is a lack of instrumented test data on whether children with moderate to severe traumatic brain injury (TBI) perform differently on the TUG compared to typically developed (TD) controls during a cognitive-motor task.Research Question: The aim was to explore the effects of a cognitive-motor task on TUG subcomponents among children with TBI compared to TD children.Methods: This observational cross-sectional study included 12 children with moderate to severe TBI (6 males and 6 females, age 10.5 ± 1.5 years of age) and 10 age and sex-matched TD controls (5 males and 5 females, 10.4 ± 1.3 years of age). Each participant completed 6 trials of the TUG wearing a single inertial measurement unit sensor at a self-selected walking pace while listening to an array of 10 randomly presented single digits during each TUG trial.Results: Total time to complete the TUG was not significantly different between groups. The cognitive-motor task led to significantly lower mean turn and peak turn angular velocity values during the turn-around-the-cone and turn-before-sitting TUG subcomponents in children with TBI compared to the TD controls (p ≤ 0.05). Additionally, the cognitive-motor task led to significantly lower values for maximum torso flexion to extension angle, peak flexion and extension angular velocity and peak vertical acceleration for the sit-to-stand subcomponent (p < 0.05). Peak flexion angular velocity during the stand-to-sit subcomponent was lower for the TBI group compared to the children with TD (p < 0.05).Significance: The study provides new insights into the performance of complex gait-related mobility tasks in the context of an instrumented TUG among children with moderate to severe TBI. Our results highlight the potential benefits of outfitting pediatric inpatients with an IMU while completing the TUG. [ABSTRACT FROM AUTHOR]

In general, muscle activity can be directly measured using Electromyography (EMG) or calculated with musculoskeletal models. However, both methods are not suitable for non-technical users and unstructured environments. It is desired to establish more portable and easy-to-use muscle activity estimation methods. Deep learning (DL) models combined with inertial measurement units (IMUs) have shown great potential to estimate muscle activity. However, it frequently occurs in clinical scenarios that a very small amount of data is available and leads to limited performance of the DL models, while the augmentation techniques to efficiently expand a small sample size for DL model training are rarely used. The primary aim of the present study was to develop a novel DL model to estimate the EMG envelope during gait using IMUs with high accuracy. A secondary aim was to develop a novel model-based data augmentation method to improve the performance of the estimation model with small-scale dataset. Therefore, in the present study, a time convolutional network-based generative adversarial network, namely MuscleGAN, was proposed for data augmentation. Moreover, a subject-independent regression DL model was developed to estimate EMG envelope. Results suggested that the proposed two-stage method has better generalization and estimation performance than the commonly used existing methods. Pearson correlation coefficient and normalized root-mean-square errors derived from the proposed method reached up to 0.72 and 0.13, respectively. It was indicated that the MuscleGAN indeed improved the estimation accuracy of lower limb EMG envelope from 70% to 72%. Thus, even using only two IMUs and a very small-scale dataset, the proposed model is still capable of accurately estimating lower limb EMG envelope, demonstrating considerable potential for its application in clinical and daily life scenarios. [ABSTRACT FROM AUTHOR]