Search

This study investigates the ergonomic assessment of sitting postures and the potential for work-related musculoskeletal disorders (WMSDs) in office environments by comparing traditional physical therapist evaluations with Inertial Measurement Unit (IMU) technology by determining the reliability and accuracy of sitting posture assessment using the rapid upper limb assessment (RULA) method. In this experiment, neck and body angle data is collected from twenty participants while sitting and working. The study aims to capture and compare the neck and trunk posture score based RULA protocol system to evaluate ergonomic risks. The findings revealed a strong correlation between the video-based evaluations by the physical therapist and the data obtained from the SMART IMU system, demonstrating the feasibility of using these combined approaches for ergonomic assessments. The results highlight the effectiveness of integrating traditional ergonomic assessment tools with modern IMU technology in comprehensively analyzing the ergonomic risks associated with prolonged sitting. [ABSTRACT FROM AUTHOR]

Background/Objectives: Sarcopenia and cognitive decline (CD) are prevalent in aging populations, impacting functionality and quality of life. The early detection of these diseases is challenging, often relying on in-person screening, which is difficult to implement regularly. This study aims to develop artificial intelligence algorithms based on gait analysis, integrating sensor and computer vision (CV) data, to detect sarcopenia and CD. Methods: A cross-sectional case-control study was conducted involving 42 individuals aged 60 years or older. Participants were classified as having sarcopenia if they met the criteria established by the European Working Group on Sarcopenia in Older People and as having CD if their score in the Mini-Mental State Examination was ≤24 points. Gait patterns were assessed at usual walking speeds using sensors attached to the feet and lumbar region, and CV data were captured using a camera. Several key variables related to gait dynamics were extracted. Finally, machine learning models were developed using these variables to predict sarcopenia and CD. Results: Models based on sensor data, CV data, and a combination of both technologies achieved high predictive accuracy, particularly for CD. The best model for CD achieved an F1-score of 0.914, with a 95% sensitivity and 92% specificity. The combined technologies model for sarcopenia also demonstrated high performance, yielding an F1-score of 0.748 with a 100% sensitivity and 83% specificity. Conclusions: The study demonstrates that gait analysis through sensor and CV fusion can effectively screen for sarcopenia and CD. The multimodal approach enhances model accuracy, potentially supporting early disease detection and intervention in home settings. [ABSTRACT FROM AUTHOR]

This paper presents a robust and efficient method for validating the accuracy of orientation sensors commonly used in practical applications, leveraging measurements from a commercial robotic manipulator as a high-precision reference. The key concept lies in determining the rotational transformations between the robot's base frame and the sensor's reference, as well as between the TCP (Tool Center Point) frame and the sensor frame, without requiring precise alignment. Key advantages of the proposed method include its independence from the exact measurement of rotations between the reference instrumentation and the sensor, systematic testing capabilities, and the ability to produce repeatable excitation patterns under controlled conditions. This approach enables automated, high-precision, and comparative evaluation of various orientation sensing devices in a reproducible manner. Moreover, it facilitates efficient calibration and analysis of sensor errors, such as drift, noise, and response delays under various motion conditions. The method's effectiveness is demonstrated through experimental validation of an Inertial Navigation System module and the SLAM-IMU fusion capabilities of the HTC VIVE VR headset, highlighting its versatility and reliability in addressing the challenges associated with orientation sensor validation. [ABSTRACT FROM AUTHOR]

The continuous, automated monitoring of sensor-based data for walking capacity and mobility has expanded gait analysis applications beyond controlled laboratory settings to real-world, everyday environments facilitated by the development of portable, cost-efficient wearable sensors. In particular, the integration of Inertial Measurement Units (IMUs) into smart shoes has proven effective for capturing detailed foot movements and spatiotemporal gait characteristics. While IMUs enable accurate foot trajectory estimation through the double integration of acceleration data, challenges such as drift errors necessitate robust correction techniques to ensure reliable performance. This review analyzes current literature on shoe-based systems utilizing IMUs to estimate spatiotemporal gait parameters and foot trajectory characteristics, including foot–ground clearance. We explore the challenges and advancements in achieving accurate 3D foot trajectory estimation using IMUs in smart shoes and the application of advanced techniques like zero-velocity updates and error correction methods. These developments present significant opportunities for achieving reliable and efficient real-time gait assessment in everyday environments. [ABSTRACT FROM AUTHOR]

Vertical jump height from a countermovement jump is a widespread metric to assess the lower limb functionality. Motion capture systems and force platforms are considered gold standards to estimate vertical jump height; however, their use in ecological settings is limited. This study aimed to evaluate the feasibility of low-sampling-rate inertial measurement units as an alternative to the gold standard systems. The validity of three computational methods for IMU-based data—numerical double integration, takeoff velocity, and flight time—was assessed using data from 18 healthy participants who performed five double-leg and ten single-leg countermovement jumps. The data were simultaneously collected from a motion capture system, two force platforms, and an IMU positioned at the L5 level. The comparisons revealed that the numerical double integration method exhibited the highest correlation (0.87) and the lowest bias (2.5 cm) compared to the gold standards and excellent reliability (0.88). Although the takeoff velocity and flight time methods demonstrated comparable performances for double-leg jumps, their accuracy in single-leg jumps was reduced. Overall, the low-sampling-rate IMU with the numerical double integration method seems to be a reliable and feasible alternative for field-based countermovement jump assessment, warranting future investigation across diverse populations and jump modalities. [ABSTRACT FROM AUTHOR]

Assessing objective physical function in patients with cancer is crucial for evaluating their ability to tolerate invasive treatments. Current assessment methods, such as the timed up and go (TUG) test and the short physical performance battery, tend to require additional resources and time, limiting their practicality in routine clinical practice. To address these challenges, we developed a system to assess physical function based on movements observed during clinical consultations and aimed to explore relevant features from inertial measurement unit data collected during those movements. As for the flow of the research, we first collected inertial measurement unit data from 61 patients with cancer while they replicated a series of movements in a consultation room. We then conducted correlation analyses to identify keypoints of focus and developed machine learning models to predict the TUG test outcomes using the extracted features. Regarding results, pelvic velocity variability (PVV) was identified using Lasso regression. A linear regression model using PVV as the input variable achieved a mean absolute error of 1.322 s and a correlation of 0.713 with the measured TUG results during five-fold cross-validation. Higher PVV correlated with shorter TUG test results. These findings provide a foundation for the development of an artificial intelligence-based physical function assessment system that operates without the need for additional resources. [ABSTRACT FROM AUTHOR]

The evaluation of gait biomechanics using portable inertial measurement units (IMUs) offers real-time feedback and has become a crucial tool for detecting gait disorders. However, many of these devices have not yet been fully validated. The aim of this study was to assess the concurrent validity and relative reliability of the RunScribe™ system for measuring spatiotemporal gait parameters during walking. A total of 460 participants (age: 36 ± 13 years; height: 173 ± 9 cm; body mass: 70 ± 13 kg) were asked to walk on a treadmill at 5 km·h−1. Spatiotemporal parameters of step frequency (SF), step length (SL), step time (ST), contact time (CT), swing time (SwT), stride time (StT), stride length (StL) and normalized stride length (StL%) were measured through RunScribe™ and OptoGait™ systems. Bland–Altman analysis indicated small systematic biases and random errors for all variables. Pearson correlation analysis showed strong correlations (0.70–0.94) between systems. The intraclass correlation coefficient supports these results, except for contact time (ICC = 0.64) and swing time (ICC = 0.34). The paired t-test showed small differences in SL, StL and StL% (≤0.25) and large in CT and SwT (1.2 and 2.2, respectively), with no differences for the rest of the variables. This study confirms the accuracy of the RunScribe™ system for assessing spatiotemporal parameters during walking, potentially reducing the barriers to continuous gait monitoring and early detection of gait issues. [ABSTRACT FROM AUTHOR]

Fatigue plays a critical role in sports science, significantly affecting recovery, training effectiveness, and overall athletic performance. Understanding and predicting fatigue is essential to optimize training, prevent overtraining, and minimize the risk of injuries. The aim of this study is to leverage Human Activity Recognition (HAR) through deep learning methods for dimensionality reduction. The use of Adversarial AutoEncoders (AAEs) is explored to assess and visualize fatigue in a two-dimensional latent space, focusing on both semi-supervised and conditional approaches. By transforming complex time-series data into this latent space, the objective is to evaluate motor changes associated with fatigue within the participants' motor control by analyzing shifts in the distribution of data points and providing a visual representation of these effects. It is hypothesized that increased fatigue will cause significant changes in point distribution, which will be analyzed using clustering techniques to identify fatigue-related patterns. The data were collected using a Wii Balance Board and three Inertial Measurement Units, which were placed on the hip and both forearms (distal part, close to the wrist) to capture dynamic and kinematic information. The participants followed a fatigue-inducing protocol that involved repeating sets of 10 repetitions of four different exercises (Squat, Right Lunge, Left Lunge, and Plank Jump) until exhaustion. Our findings indicate that the AAE models are effective in reducing data dimensionality, allowing for the visualization of fatigue's impact within a 2D latent space. The latent space representation provides insights into motor control variations, revealing patterns that can be used to monitor fatigue levels and optimize training or rehabilitation programs. [ABSTRACT FROM AUTHOR]

Locomotive syndrome (LS) refers to a condition where individuals face challenges in performing activities of daily living. Early detection of such deterioration is crucial to reduce the need for nursing care. The Geriatric Locomotive Function Scale (GLFS-25), a 25-question assessment, has been proposed for categorizing individuals into different stages of LS. However, its subjectivity has prompted interest in technology-based quantitative assessments. In this study, we utilized machine learning and an instrumented five-time sit-to-stand test (FTSTS) to assess LS stages. Younger and older participants were recruited, with older individuals classified into LS stages 0–2 based on their GLFS-25 scores. Equipped with a single inertial measurement unit at the pelvis level, participants performed the FTSTS. Using acceleration data, 144 features were extracted, and seven distinct machine learning models were developed using the features. Remarkably, the multilayer perceptron (MLP) model demonstrated superior performance. Following data augmentation and principal component analysis (PCA), the MLP+PCA model achieved an accuracy of 0.9, a precision of 0.92, a recall of 0.9, and an F1 score of 0.91. This underscores the efficacy of the approach for LS assessment. This study lays the foundation for the future development of a remote LS assessment system using commonplace devices like smartphones. [ABSTRACT FROM AUTHOR]

In this paper, the accuracy of inertial sensor orientation relative to the level frame is improved through optimal tuning of a complementary filter by a genetic algorithm. While constant filter gains have been used elsewhere, these may introduce errors under dynamic motions when gyroscopes should be trusted more than accelerometers. Optimal gains are prescribed by a Mamdani fuzzy rule base whose membership functions are found using a genetic algorithm and experimental data. Furthermore, model fitness is not based directly on orientation but the error between estimated and ground truth velocities. This paper has three interrelated novel elements. The main novelty is the indirect tuning method, which is simple, low-cost and requires a single camera and inertial sensor. The method is shown to increase tracking accuracy compared with popular baseline filters. Secondary novel elements are the bespoke genetic algorithm and the time agnostic velocity error metric. The contributions from this work can help improve the localization accuracy of assets and human personnel. This research has a direct impact in command and control by improving situational awareness and the ability to direct assets to safe locations using safer routes. This results in increasing safety in applications such as firefighting and battlespace. [ABSTRACT FROM AUTHOR]

Background: We aimed to assess (1) the awareness of parents regarding the cervical rotation preference of their infant and the agreement of the parent, clinician and objective assessments, and (2) the test–retest reliability for objective (measured) rotation, lateral flexion and combined flexion–rotation. Methods: This was a cross-sectional study including 69 infants aged three to six months with upper cervical spine dysfunction, without general health issues or specific cervical spine impairments. No treatment was applied. The primary outcomes were parent and clinician assessments of cervical spine rotation preference. The secondary outcome was the cervical range of motion measured by inertial measurement units (IMUs) at two different timepoints. Spearman correlation was performed for the parent, clinician and objective assessments. IMU data were dichotomized into the preferred and unpreferred sides, and test–retest reliability was assessed (ICC). Results: The mean age of infants was 145 days ± 29.1 days, birth length 49.40 cm ± 2.7 cm, birth weight 3328 g ± 530.9 g and 24 were female. In total, 33 infants were assessed by their parents as right-preferred, 30 as left-preferred and 6 as having no preference. The clinician assessed 38 infants as right-preferred and 31 as left-preferred. The correlation between parents and the clinician was rs = 0.687 (p < 0.001), the clinician and the IMU rs = 0.408 (p = 0.005) and parents and the IMU rs = 0.301 (p = 0.044). The ICC of cervical range of motion measurements ranged from poor to moderate. Conclusions: Clinicians can use the parents' assessment of cervical spine rotation preference as a foundation for their clinical examination. IMU measurements are difficult in infants, possibly due to their lack of cooperation during measurements. Clinical Trial Registration Number: clinicaltrails.gov (NCT04981782). [ABSTRACT FROM AUTHOR]

Unstable gait increases the risk of falls, posing a significant danger, particularly for frail older adults. The margin of stability (MoS) is a quantitative index that reflects the risk of falling due to postural imbalance in both the anterior-posterior and mediolateral directions during walking. Although MoS is a reliable indicator, its computation typically requires specialized equipment, such as motion capture systems, limiting its application to laboratory settings. To address this limitation, we propose a method for estimating MoS using time-series data from the translational and angular velocities of a single body segment—the pelvis. By applying principal motion analysis to process the multivariate time-series data, we successfully estimated MoS. Our results demonstrate that the estimated MoS in the mediolateral direction achieved an RMSE of 0.88 cm and a correlation coefficient of 0.72 with measured values, while in the anterior-posterior direction, the RMSE was 0.73 cm with a correlation coefficient of 0.87. These values for the mediolateral direction are better than those obtained in previous studies using only the three translational velocity components of the pelvis, whereas the values for the anterior direction are comparable to previous approaches. Our findings suggest that MoS can be reliably estimated using six-axial kinematic data of the pelvis, offering a more accessible method for assessing gait stability. [ABSTRACT FROM AUTHOR]

Simple Summary: Monitoring the behavior of young calves is essential for ensuring their health, welfare, and optimal growth. However, traditional observation methods are time-consuming and labor-intensive. This study aimed to develop a technology-based solution for automatically monitoring multiple behaviors in beef calves raised with their mothers. Collar-mounted sensors, which combined accelerometers and gyroscopes, were used to collect data on calf movements. Machine learning techniques were then applied to analyze the data and classify behaviors related to feeding, posture, and coughing. The results showed that the developed models could accurately identify these behaviors, providing a powerful tool that allows farmers to optimize their calf management strategies and detect potential health issues early on. This technology can help improve the efficiency and sustainability of calf rearing practices, ultimately benefiting both farm productivity and animal welfare. The monitoring of pre-weaned calf behavior is crucial for ensuring health, welfare, and optimal growth. This study aimed to develop and validate a machine learning-based technique for the simultaneous monitoring of multiple behaviors in pre-weaned beef calves within a cow–calf contact (CCC) system using collar-mounted sensors integrating accelerometers and gyroscopes. Three complementary models were developed to classify feeding-related behaviors (natural suckling, feeding, rumination, and others), postural states (lying and standing), and coughing events. Sensor data, including tri-axial acceleration and tri-axial angular velocity, along with video recordings, were collected from 78 beef calves across two farms. The LightGBM algorithm was employed for behavior classification, and model performance was evaluated using a confusion matrix, the area under the receiver operating characteristic curve (AUC-ROC), and Pearson's correlation coefficient (r). Model 1 achieved a high performance in recognizing natural suckling (accuracy: 99.10%; F1 score: 96.88%; AUC-ROC: 0.999; r: 0.997), rumination (accuracy: 97.36%; F1 score: 95.07%; AUC-ROC: 0.995; r: 0.990), and feeding (accuracy: 95.76%; F1 score: 91.89%; AUC-ROC: 0.990; r: 0.987). Model 2 exhibited an excellent classification of lying (accuracy: 97.98%; F1 score: 98.45%; AUC-ROC: 0.989; r: 0.982) and standing (accuracy: 97.98%; F1 score: 97.11%; AUC-ROC: 0.989; r: 0.983). Model 3 achieved a reasonable performance in recognizing coughing events (accuracy: 88.88%; F1 score: 78.61%; AUC-ROC: 0.942; r: 0.969). This study demonstrates the potential of machine learning and collar-mounted sensors for monitoring multiple behaviors in calves, providing a valuable tool for optimizing production management and early disease detection in the CCC system [ABSTRACT FROM AUTHOR]

This study proposes a wearable gait assessment method using inertial measurement units (IMUs) to evaluate gait ability in daily environments. By focusing on the estimation of the margin of stability (MoS), a key kinematic stability parameter, a method using a convolutional neural network, was developed to estimate the MoS from IMU acceleration time-series data. The relationship between MoS and other stability indices, such as the Lyapunov exponent and the multi-site time-series (MSTS) index, using data from five IMU sensors placed on various body parts was also examined. To simulate diverse gait conditions, treadmill speed was varied, and a knee–ankle–foot orthosis was used to restrict left knee extension, inducing gait asymmetry. The model achieved over 90% accuracy in classifying MoS in both forward and lateral directions using three-axis acceleration data from the IMUs. However, the correlation between MoS and the Lyapunov exponent or MSTS index was weak, suggesting that these indices may capture different aspects of gait stability. [ABSTRACT FROM AUTHOR]

This study aimed to determine the characteristics of accelerations and angular velocities obtained by an inertial measurement unit (IMU) attached to a cane between older people with and without physical frailty. Community-dwelling older people walked at a comfortable speed using a cane with a built-in IMU. Physical frailty was assessed using exercise-related items extracted from the Kihon Check List. The efficacy of five machine learning models in distinguishing older people with physical frailty was investigated. This study included 48 older people, of which 24 were frail and 24 were not. Compared with the non-frail participants, the older people with physical frailty had a small root mean square value in the vertical and anteroposterior directions and angular velocity in the anteroposterior direction (p < 0.001, r = 0.36; p < 0.001, r = 0.29; p < 0.001, r = 0.30, respectively) and a large mean power frequency value in the vertical direction (p = 0.042, r = 0.18). The decision tree model could most effectively classify physical frailty, with an accuracy, F1 score, and area under the curve of 78.6%, 91.8%, and 0.81, respectively. The characteristics of IMU-attached cane usage by older adults with physical frailty can be utilized to effectively evaluate and determine physical frailty in their usual environments. [ABSTRACT FROM AUTHOR]

Motion blur is a problem that degrades the visual quality of images for human perception and also challenges computer vision tasks. While existing studies mostly focus on deblurring algorithms to remove uniform blur due to their computational efficiency, such approaches fail when faced with non-uniform blur. In this study, we propose a novel algorithm for motion deblurring that utilizes an adaptive mesh-grid approach to manage non-uniform motion blur with a focus on reducing the computational cost. The proposed method divides the image into a mesh-grid and estimates the blur point spread function (PSF) using an inertial sensor. For each video frame, the size of the grid cells is determined adaptively according to the in-frame spatial variance of blur magnitude which is a proposed metric for the blur non-uniformity in the video frame. The adaptive mesh-size takes smaller values for higher variances, increasing the spatial accuracy of the PSF estimation. Two versions of the adaptive mesh-size algorithm are studied, optimized for either best quality or balanced performance and computation cost. Also, a trade-off parameter is defined for changing the mesh-size according to application requirements. The experiments, using real-life motion data combined with simulated motion blur demonstrate that the proposed adaptive mesh-size algorithm can achieve 5% increase in PSNR quality gain together with a 19% decrease in computation time on the average when compared to the constant mesh-size method. [ABSTRACT FROM AUTHOR]

A global navigation satellite system (GNSS) for remotely piloted aircraft systems (RPASs) positioning is essential, thanks to the worldwide availability and continuity of this technology in the provision of positioning services. This makes the GNSS technology a critical element as malfunctions impacting on the determination of the position, velocity and timing (PVT) solution could determine safety issues. Such an aspect is particularly challenging in urban air mobility (UAM) scenarios, where low satellite visibility, multipath, radio frequency interference and cyber threats can dangerously affect the PVT solution. So, to meet integrity requirements, GNSS receiver measurements are augmented/fused with other aircraft sensors that can supply position and/or velocity information on the aircraft without relying on any other satellite and/or ground infrastructures. In this framework, in this paper, the algorithms of a hybrid navigation unit (HNU) for UAM applications are detailed, implementing a tightly coupled sensor fusion between a dual-frequency multi-constellation GNSS receiver, an inertial measurements unit and the barometric altitude from an air data computer. The implemented navigation algorithm is integrated with autonomous fault detection and exclusion of GPS/Galileo/BeiDou satellites and the estimation of navigation solution integrity/accuracy (i.e., protection level and figures of merit). In-flight tests were performed to validate the HNU functionalities demonstrating its effectiveness in UAM scenarios even in the presence of cyber threats. In detail, the navigation solution, compared with a real-time kinematic GPS receiver used as the reference centimetre-level position sensor, demonstrated good accuracy, with position errors below 15 m horizontally and 10 m vertically under nominal conditions (i.e., urban scenarios characterized by satellite low visibility and multipath). It continued to provide a valid navigation solution even in the presence of off-nominal events, such as spoofing attacks. The cyber threats were correctly detected and excluded by the system through the indication of the valid/not valid satellite measurements. However, the results indicate a need for fine-tuning the EKF to improve the estimation of figures of merit and protection levels associated to the navigation solution during the cyber-attacks. In contrast, solution accuracy and integrity indicators are well estimated in nominal conditions. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping (SLAM) is a critical technology for unmanned driving. Existing SLAM methods have the drawbacks of significant cumulative errors and drift in coal mine roadway environment. In this study, a roadway environment feature-assisted SLAM algorithm integrating inertial measurement unit (IMU) and LiDAR was proposed. IMU observation data was used to predict the motion state of point cloud and motion compensation was applied to reduce point cloud distortion caused by equipment movement. Pose transformation information from LiDAR odometry was obtained through point cloud registration, forming a LiDAR odometry constraint. Point clouds from roadway sidewalls and floor were extracted and fitted to planes, establishing environmental constraints. Using IMU pre-integration constraints, LiDAR odometry constraints, and environmental constraints, the algorithm applied factor graph optimization to achieve tight coupling between LiDAR and IMU, enabling high-precision 3D reconstruction of roadway scenes and accurate localization of autonomous vehicles. Simulation experiments showed that the absolute trajectory root mean square error (RMSE) of the roadway environment feature-assisted IMU-LiDAR integrated SLAM algorithm was 0.1162 m, and the relative trajectory RMSE was 0.0409 m, improving positioning accuracy compared to commonly used algorithms such as LeGO-LOAM and LIO-SAM. Based on the test results in a real environment, the algorithm provides excellent mapping performance with no drift or trailing, demonstrating strong environmental adaptability and robustness.

Abstract Background Onasemnogene abeparvovec gene replacement therapy (GT) has changed the prognosis of patients with spinal muscular atrophy (SMA) with variable outcome regarding motor development in symptomatic patients. This pilot study evaluates acceptability, validity and clinical relevance of Inertial Measurement Units (IMU) to monitor spontaneous movement recovery in early onset SMA patients after GT. Methods Clinical assessments including CHOPINTEND score (the gold standard motor score for infants with SMA) and IMU measurements were performed before (M0) and repeatedly after GT. Inertial data was recorded during a 25-min spontaneous movement task, the child lying on the back, without (10 min) and with a playset (15 min) wearing IMUs. Two commonly used parameters, norm acceleration 95th centile (||A||_95) and counts per minute (||A||_CPM) were computed for each wrist, elbow and foot sensors. Results 23 SMA-patients were included (mean age at diagnosis 8 months [min 2, max 20], 19 SMA type 1, three type 2 and one presymptomatic) and 104 IMU-measurements were performed, all well accepted by families and 84/104 with a good child participation (evaluated with Brazelton scale). ||A||_95 and ||A||_CPM showed high internal consistency (without versus with a playset) with interclass correlation coefficient for the wrist sensors of 0.88 and 0.85 respectively and for the foot sensors of 0.93 and 0.91 respectively. ||A||_95 and ||A||_CPM were strongly correlated with CHOPINTEND (r for wrist sensors 0.74 and 0.67 respectively and for foot sensors 0.61 and 0.68 respectively, p-values

Abstract Studies reported the existence of instability catch (IC) during trunk flexion in patients with chronic low back pain (CLBP). However, different movement speeds can cause different neuromuscular demands resulting in altered kinematic patterns. In addition, kinematic characterization corresponding to clinical observation of IC is still limited. Therefore, this study aimed to determine (1) the association between movement speed and kinematic parameters representing IC during trunk flexion and (2) the differences in kinematic parameters between individuals with and without CLBP. Fifteen no low back pain (NoLBP) and 15 CLBP individuals were recruited. Inertial measurement units (IMU) were attached to T3, L1, and S2 spinous processes. Participants performed active trunk flexion while IMU data were simultaneously collected. Total trunk, lumbar, and pelvic mean angular velocity (T_MV, L_MV, and P_MV), as well as number of zero-crossings, peak-to-peak, and area of sudden deceleration and acceleration (Num, P2P, and Area), were derived. Pearson’s correlation tests were used to determine the association between T_MV and L_MV, P_MV, Num, P2P, and Area. An ANCOVA was performed to determine the difference in kinematic parameters between groups using movement speed as a covariate. Significant associations (P

The purpose of this paper is to introduce a method of measuring spatiotemporal gait patterns, tibial accelerations, and heart rate that are matched with high resolution geographical terrain features using publicly available data. These methods were demonstrated using data from 218 Marines, who completed loaded outdoor ruck hikes between 5–20 km over varying terrain. Each participant was instrumented with two inertial measurement units (IMUs) and a GPS watch. Custom code synchronized accelerometer and positional data without a priori sensor synchronization, calibrated orientation of the IMUs in the tibial reference frame, detected and separated only periods of walking or running, and computed acceleration and spatiotemporal outcomes. GPS positional data were georeferenced with geographic information system (GIS) maps to extract terrain features such as slope, altitude, and surface conditions. This paper reveals the ease at which similar data can be gathered among relatively large groups of people with minimal setup and automated data processing. The methods described here can be adapted to other populations and similar ground-based activities such as skiing or trail running. [ABSTRACT FROM AUTHOR]

Frozen shoulder (FS) is a common shoulder condition accompanied by shoulder pain and a loss of shoulder range of motion (ROM). The typical clinical assessment tools such as questionnaires and ROM measurement are susceptible to subjectivity and individual bias. To provide an objective evaluation for clinical assessment, this study proposes an inertial measurement unit (IMU)-based identification system to automatically identify shoulder tasks whether performed by healthy subjects or FS patients. Two groups of features (time-domain statistical features and kinematic features), seven machine learning (ML) techniques, and two deep learning (DL) models are applied in the proposed identification system. For the experiments, 24 FS patients and 20 healthy subjects were recruited to perform five daily shoulder tasks with two IMUs attached to the arm and the wrist. The results demonstrate that the proposed system using deep learning presented the best identification performance using all features. The convolutional neural network achieved the best identification accuracy of 88.26%, and the multilayer perceptron obtained the best F1 score of 89.23%. Further analysis revealed that the identification performance based on wrist features had a higher accuracy compared to that based on arm features. The system's performance using time-domain statistical features has better discriminability in terms of identifying FS compared to using kinematic features. We demonstrate that the implementation of the IMU-based identification system using ML is feasible for FS assessment in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Onasemnogene abeparvovec gene replacement therapy (GT) has changed the prognosis of patients with spinal muscular atrophy (SMA) with variable outcome regarding motor development in symptomatic patients. This pilot study evaluates acceptability, validity and clinical relevance of Inertial Measurement Units (IMU) to monitor spontaneous movement recovery in early onset SMA patients after GT. Methods: Clinical assessments including CHOPINTEND score (the gold standard motor score for infants with SMA) and IMU measurements were performed before (M0) and repeatedly after GT. Inertial data was recorded during a 25-min spontaneous movement task, the child lying on the back, without (10 min) and with a playset (15 min) wearing IMUs. Two commonly used parameters, norm acceleration 95th centile (||A||_95) and counts per minute (||A||_CPM) were computed for each wrist, elbow and foot sensors. Results: 23 SMA-patients were included (mean age at diagnosis 8 months [min 2, max 20], 19 SMA type 1, three type 2 and one presymptomatic) and 104 IMU-measurements were performed, all well accepted by families and 84/104 with a good child participation (evaluated with Brazelton scale). ||A||_95 and ||A||_CPM showed high internal consistency (without versus with a playset) with interclass correlation coefficient for the wrist sensors of 0.88 and 0.85 respectively and for the foot sensors of 0.93 and 0.91 respectively. ||A||_95 and ||A||_CPM were strongly correlated with CHOPINTEND (r for wrist sensors 0.74 and 0.67 respectively and for foot sensors 0.61 and 0.68 respectively, p-values < 0.001). ||A||_95 for the foot, the wrist, the elbow sensors and ||A||_CPM for the foot, the wrist, the elbow sensors increased significantly between baseline and the 12 months follow-up visit (respective p-values: 0.004, < 0.001, < 0.001, 0.006, < 0.001, < 0.001). Conclusion: IMUs were well accepted, consistent, concurrently valid, responsive and associated with unaided sitting acquisition especially for the elbow sensors. This study is the first reporting a large set of inertial sensor derived data after GT in SMA patients and paves the way for IMU-based follow-up of SMA patients after treatment. [ABSTRACT FROM AUTHOR]

Manual wheelchair propulsion represents a repetitive and constraining task, which leads mainly to the development of joint injury in spinal cord-injured people. One of the main reasons is the load sustained by the shoulder joint during the propulsion cycle. Moreover, the load at the shoulder joint is highly correlated with the force and moment acting at the handrim level. The main objective of this study is related to the estimation of handrim reactions forces and moments during wheelchair propulsion using only a single inertial measurement unit per hand. Two approaches are proposed here: Firstly, a method of identification of a non-linear transfer function based on the Hammerstein–Wiener (HW) modeling approach was used. The latter represents a typical multi-input single output in a system engineering modeling approach. Secondly, a specific variant of recurrent neural network called BiLSTM is proposed to predict the time-series data of force and moments at the handrim level. Eleven subjects participated in this study in a linear propulsion protocol, while the forces and moments were measured by a dynamic platform. The two input signals were the linear acceleration as well the angular velocity of the wrist joint. The horizontal, vertical and sagittal moments were estimated by the two approaches. The mean average error (MAE) shows a value of 6.10 N and 4.30 N for the horizontal force for BiLSTM and HW, respectively. The results for the vertical direction show a MAE of 5.91 N and 7.59 N for BiLSTM and HW, respectively. Finally, the MAE for the sagittal moment varies from 0.96 Nm (BiLSTM) to 1.09 Nm for the HW model. The approaches seem similar with respect to the MAE and can be considered accurate knowing that the order of magnitude of the uncertainties of the dynamic platform was reported to be 2.2 N for the horizontal and vertical forces and 2.24 Nm for the sagittal moments. However, it should be noted that HW necessitates the knowledge of the average force and patterns of each subject, whereas the BiLSTM method do not involve the average patterns, which shows its superiority for time-series data prediction. The results provided in this study show the possibility of measuring dynamic forces acting at the handrim level during wheelchair manual propulsion in ecological environments. [ABSTRACT FROM AUTHOR]

Existing methods to obtain position from inertial sensors typically use a combination of multiple sensors and orientation modeling; thus, obtaining position from a single inertial sensor is highly desirable given the decreased setup time and reduced complexity. The dead reckoning method is commonly chosen to obtain position from acceleration; however, when applied to upper limb tracking, the accuracy of position estimates are questionable, which limits feasibility. A new method of obtaining position estimates through the use of zero velocity updates is reported, using a commercial IMU, a push-to-make momentary switch, and a 3D printed object to house the sensors. The generated position estimates can subsequently be converted into sound through sonification to provide audio feedback on reaching movements for rehabilitation applications. An evaluation of the performance of the generated position estimates from a system labeled 'Soniccup' is presented through a comparison with the outputs from a Vicon Nexus system. The results indicate that for reaching movements below one second in duration, the Soniccup produces positional estimates with high similarity to the same movements captured through the Vicon system, corresponding to comparable audio output from the two systems. However, future work to improve the performance of longer-duration movements and reduce the system latency to produce real-time audio feedback is required to improve the acceptability of the system. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Train positioning method based on the global navigation satellite system (GNSS) is a key development direction for the next generation train operation control systems. To enhance the safety and autonomy of train positioning, while fully considering the cost of integrated positioning systems and the complementary performance of heterogeneous sensors, this study employed the BeiDou navigation satellite system (BDS) and inertial measurement unit (IMU) to build an onboard integrated positioning scheme. Addressing the issue of cumulative error divergence in IMU standalone navigation with BDS under conditions where satellite signals were limited, resulting in positioning failure, such as in tunnels, a method aided by grey combination prediction model was proposed to maintain positioning accuracy. An algorithm model was established based on location information from integrated BDS/ IMU system prior to BDS signal failure. When the train entered an environment with limited satellite signals, the grey combination model continuously corrected the IMU's cumulative errors using the predicted train positioning data sequence, ensuring the availability of positioning output throughout the entire train operation. The proposed method was validated on a simulation platform in a laboratory environment. The results showed that the BDS/ IMU combination maintained high positioning accuracy under normal working conditions, with the eastward/ northward position errors less than 2 m and the eastward/ northward velocity errors under 0. 2 m/ s. In scenarios where BDS positioning failed, the grey combination model corrected the IMU's cumulative errors using the predicted data, reducing eastward/ northward position errors by more than 85% and eastward/ northward velocity errors by over 80% compared to IMU standalone navigation. The positioning accuracy meets the train operation control system's requirements for train positioning. [ABSTRACT FROM AUTHOR]

Utilizing reliable and accurate positioning and navigation systems is crucial for saving the lives of rescue personnel and accelerating rescue operations. However, Global Navigation Satellite Systems (GNSSs), such as GPS, may not provide stable signals in dense forests. Therefore, integrating multiple sensors like GPS and Inertial Measurement Units (IMUs) becomes essential to enhance the availability and accuracy of positioning systems. To accurately estimate rescuers' positions, this paper employs the Adaptive Unscented Kalman Filter (AUKF) algorithm with measurement noise variance matrix adaptation, integrating IMU and GPS data alongside barometric altitude measurements for precise three-dimensional positioning in complex environments. The AUKF enhances estimation robustness through the adaptive adjustment of the measurement noise variance matrix, particularly excelling when GPS signals are interrupted. This study conducted tests on two-dimensional and three-dimensional road scenarios in forest environments, confirming that the AUKF-algorithm-based integrated navigation system outperforms the traditional Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Adaptive Extended Kalman Filter (AEKF) in emergency rescue applications. The tests further evaluated the system's navigation performance on rugged roads and during GPS signal interruptions. The results demonstrate that the system achieves higher positioning accuracy on rugged forest roads, notably reducing errors by 18.32% in the north direction, 8.51% in the up direction, and 3.85% in the east direction compared to the EKF. Furthermore, the system exhibits good adaptability during GPS signal interruptions, ensuring continuous and accurate personnel positioning during rescue operations. [ABSTRACT FROM AUTHOR]

Studies reported the existence of instability catch (IC) during trunk flexion in patients with chronic low back pain (CLBP). However, different movement speeds can cause different neuromuscular demands resulting in altered kinematic patterns. In addition, kinematic characterization corresponding to clinical observation of IC is still limited. Therefore, this study aimed to determine (1) the association between movement speed and kinematic parameters representing IC during trunk flexion and (2) the differences in kinematic parameters between individuals with and without CLBP. Fifteen no low back pain (NoLBP) and 15 CLBP individuals were recruited. Inertial measurement units (IMU) were attached to T3, L1, and S2 spinous processes. Participants performed active trunk flexion while IMU data were simultaneously collected. Total trunk, lumbar, and pelvic mean angular velocity (T_MV, L_MV, and P_MV), as well as number of zero-crossings, peak-to-peak, and area of sudden deceleration and acceleration (Num, P2P, and Area), were derived. Pearson's correlation tests were used to determine the association between T_MV and L_MV, P_MV, Num, P2P, and Area. An ANCOVA was performed to determine the difference in kinematic parameters between groups using movement speed as a covariate. Significant associations (P < 0.05) were found between movement speed and other kinematic parameters, except for Area. Results showed that L_MV significantly differed from the P_MV (P = 0.002) in the CLBP group, while a significant between-group difference (P = 0.037) was found in the P_MV. Additionally, significant between-group differences (P < 0.05) in P2P and Area were observed. The associations between movement speed and kinematic parameters suggest that movement speed changes can alter kinematic patterns. Therefore, clinicians may challenge lumbopelvic neuromuscular control by modifying movement speed to elicit greater change in kinematic patterns. In addition, the NoLBP group used shared lumbar and pelvic contributions, while the CLBP group used less pelvic contribution. Finally, P2P and Area appeared to offer the greatest sensitivity to differentiate between the groups. Overall, these findings may enhance the understanding of the mechanism underlying IC in CLBP. [ABSTRACT FROM AUTHOR]

Inertial measurement units (IMU) are increasingly utilized to capture biomechanical measures such as joint kinematics outside traditional biomechanics laboratories. These wearable sensors have been proven to help clinicians and engineers monitor rehabilitation progress, improve prosthesis development, and record human performance in a variety of settings. The Valor IMU aims to offer a portable motion capture alternative to provide reliable and accurate joint kinematics when compared to industry gold standard optical motion capture cameras. However, IMUs can have disturbances in their measurements caused by magnetic fields, drift, and inappropriate calibration routines. Therefore, the purpose of this investigation is to validate the joint angles captured by the Valor IMU in comparison to an optical motion capture system across a variety of movements. Our findings showed mean absolute differences between Valor IMU and Vicon motion capture across all subjects' joint angles. The tasks ranged from 1.81 degrees to 17.46 degrees, the root mean squared errors ranged from 1.89 degrees to 16.62 degrees, and interclass correlation coefficient agreements ranged from 0.57 to 0.99. The results in the current paper further promote the usage of the IMU system outside traditional biomechanical laboratories. Future examinations of this IMU should include smaller, modular IMUs with non-slip Velcro bands and further validation regarding transverse plane joint kinematics such as joint internal/external rotations. [ABSTRACT FROM AUTHOR]

Accurately estimating single-axis rotational angle changes is crucial in many high-tech domains. However, traditional angle measurement techniques are often constrained by sensor limitations and environmental interferences, resulting in significant deficiencies in precision and stability. Moreover, current methodologies typically rely on fixed-axis rotation models, leading to substantial discrepancies between measured and actual angles due to axis misalignment. To address these issues, this paper proposes an innovative method for single-axis rotational angle estimation. It introduces a calibration technique for installation errors between inertial measurement units and the overall measurement system, effectively translating dynamic rotational inertial outputs to system enclosure outputs. Subsequently, the method employs triaxial accelerometers combined with zero-velocity detection technology to estimate the rotation axis position. Finally, it delves into analyzing the relationship between quaternion and axis–angle, aimed at reducing noise interference for precise rotational angle estimation. Based on this proposed methodology, a Low-Cost, a High Accuracy Measurement System (HAMS) integrating sensor fusion was designed and implemented. Experimental results demonstrate static measurement errors below ±0.15° and dynamic measurement errors below ±0.5° within a ±180° range. [ABSTRACT FROM AUTHOR]

Integrating running gait coordination assessment into athlete monitoring systems could provide unique insight into training tolerance and fatigue-related gait alterations. This study investigated the impact of an overload training intervention and recovery on running gait coordination assessed by field-based self-testing. Fifteen trained distance runners were recruited to perform 1-week of light training (baseline), 2 weeks of heavy training (high intensity, duration, and frequency) designed to overload participants, and a 10-day light taper to allow recovery and adaptation. Field-based running assessments using ankle accelerometry and online short recovery and stress scale (SRSS) surveys were completed daily. Running performance was assessed after each training phase using a maximal effort multi-stage running test-to-exhaustion (RTE). Gait coordination was assessed using detrended fluctuation analysis (DFA) of a stride interval time series. Two participants withdrew during baseline training due to changed personal circumstances. Four participants withdrew during heavy training due to injury. The remaining nine participants completed heavy training and were included in the final analysis. Heavy training reduced DFA values (standardised mean difference (SMD) = −1.44 ± 0.90; p = 0.004), recovery (SMD = −1.83 ± 0.82; p less than 0.001), performance (SMD = −0.36 ± 0.32; p = 0.03), and increased stress (SMD = 1.78 ± 0.94; p = 0.001) compared to baseline. DFA values (p = 0.73), recovery (p = 0.77), and stress (p = 0.73) returned to baseline levels after tapering while performance trended towards improvement from baseline (SMD = 0.28 ± 0.37; p = 0.13). Reduced DFA values were associated with reduced performance (r2 = 0.55) and recovery (r2 = 0.55) and increased stress (r2 = 0.62). Field-based testing of running gait coordination is a promising method of monitoring training tolerance in running athletes during overload training. [ABSTRACT FROM AUTHOR]

Background: Precise identification of motion phases in long-track speed skating is critical to characterize and optimize performance. This study aimed to estimate the intra- and inter-rater reliability of movement phase identification using inertial measurement units (IMUs) in long-track speed skating. Methods: We analyzed 15 skaters using IMUs attached to specific body locations during a 500m skate, focusing on the stance phase, and identifying three movement events: Onset, Edge-flip, and Push-off. Reliability was assessed using intraclass correlation coefficients (ICC) and Bland-Altman analysis. Results: Results showed high intra- and inter-rater reliability (ICC [1,1]: 0.86 to 0.99; ICC [2,1]: 0.81 to 0.99) across all events. Absolute error ranged from 0.56 to 6.15 ms and from 0.92 to 26.29 ms for intra- and inter-rater reliability, respectively. Minimally detectable change (MDC) ranged from 17.56 to 62.22 ms and from 33.23 to 131.25 ms for intra- and inter-rater reliability, respectively. Discussion: Despite some additive and proportional errors, the overall error range was within acceptable limits, indicating negligible systematic errors. The measurement error range was small, demonstrating the accuracy of IMUs. IMUs demonstrate high reliability in movement phase identification during speed skating, endorsing their application in sports science for enhanced kinematic studies and training. [ABSTRACT FROM AUTHOR]

The article presents the topic of propeller damage detection on unmanned multirotor drones. Propeller damage is dangerous as it can negatively affect the flight of a drone or lead to hazardous situations. The article proposes a non-invasive method for detecting damage within the drone's hardware, which utilizes existing sensors in the Internal Measuring Unit (IMU) to classify propeller damage. The classification is performed by using the Bayesian Gaussian Mixture Model (BGMM). In the field of drone propeller damage detection, there is a significant issue of data scarcity due to traditional methods often involving invasive and destructive testing, which can lead to the loss of valuable equipment and high costs. Bayesian methods, such as BGMM, are particularly well-suited to address this issue by effectively handling limited data through incorporating prior knowledge and probabilistic reasoning. Moreover, using the IMU for damage detection is highly advantageous as it eliminates the need for additional sensors, reducing overall costs and preventing added weight that could compromise the drone's performance. IMUs do not require specific environmental conditions to function properly, making them more versatile and practical for real-world applications. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) is a crucial task in various applications, including healthcare, fitness, and the military. Deep learning models have revolutionized HAR, however, their computational complexity, particularly those involving BiLSTMs, poses significant challenges for deployment on resource-constrained devices like smartphones. While BiLSTMs effectively capture long-term dependencies by processing inputs bidirectionally, their high parameter count and computational demands hinder practical applications in real-time HAR. This study investigates the approximation of the computationally intensive BiLSTM component in a HAR model by using a combination of alternative model components and data flipping augmentation. The proposed modifications to an existing hybrid model architecture replace the BiLSTM with standard and residual LSTM, along with convolutional networks, supplemented by data flipping augmentation to replicate the context awareness typically provided by BiLSTM networks. The results demonstrate that the residual LSTM (ResLSTM) model achieves superior performance while maintaining a lower computational complexity compared to the traditional BiLSTM model. Specifically, on the UCI-HAR dataset, the ResLSTM model attains an accuracy of 96.34% with 576,702 parameters, outperforming the BiLSTM model's accuracy of 95.22% with 849,534 parameters. On the WISDM dataset, the ResLSTM achieves an accuracy of 97.20% with 192,238 parameters, compared to the BiLSTM's 97.23% accuracy with 283,182 parameters, demonstrating a more efficient architecture with minimal performance trade-off. For the KU-HAR dataset, the ResLSTM model achieves an accuracy of 97.05% with 386,038 parameters, showing comparable performance to the BiLSTM model's 98.63% accuracy with 569,462 parameters, but with significantly fewer parameters. [ABSTRACT FROM AUTHOR]

Overlap between marine megafauna and maritime activities is a topic of global concern. Basking sharks (Cetorhinus maximus; CM) are listed as Globally Endangered under the IUCN, though reported sightings appear to be increasing in Ireland. While such trends in the region are welcome, increasing spatiotemporal overlap between CM and numerous water users poses an increased risk of boat strikes to the animals. To demonstrate the risk and impact of boat strikes on marine megafauna, we present camera-enabled animal-borne inertial measurement unit (IMU) data from a non-lethal boat strike on a CM within a proposed National Marine Park in Ireland. We tagged a ~7-m female CM in County Kerry, Ireland, which was struck by a boat ~6 h after tag deployment. Comparison of pre-strike data with 4 h of video and ~7.5 h of IMU data following the boat strike provides critical insight into the animal’s response. While the CM reacted momentarily with an increase in activity and swam to the seafloor, it quickly reduced its overall activity (i.e., overall dynamic body acceleration, tailbeat cycles, tailbeat amplitude, and vertical velocity) for the remainder of the deployment. Notably, the animal also ceased feeding for the duration of the video and headed towards deep offshore waters, which is in stark contrast to the pre-strike period where the animal was consistently observed feeding along the surface in shallow coastal water. This work provides insight into a CM’s response to acute injury and highlights the need for appropriate protections to mitigate risks for marine megafauna. [ABSTRACT FROM AUTHOR]

A method for working six-legged robots to recover from an overturned state on a flat slope is proposed. The robot rotates around its roll axis, which is parallel to the slope, from an overturned state to a normal state. During this process, the robot supports its body using six legs and maintains as much static balance as possible. This enables a stable overturn recovery. Before recovery, the robot may rotate in the overturned state around its yaw axis, which is vertical to the slope, until the recovery direction becomes lateral to the slope. This reduces the risk of tumbling down the slope. Consequently, the robot can recover from the overturned state on the flat slope of 40° when it recovers almost in the lateral direction. [ABSTRACT FROM AUTHOR]

The labor shortage and rising costs in the greenhouse industry have driven the development of automation, with the core of autonomous operations being positioning and navigation technology. However, precise positioning in complex greenhouse environments and narrow aisles poses challenges to localization technologies. This study proposes a multi-sensor fusion positioning and navigation robot based on ultra-wideband (UWB), an inertial measurement unit (IMU), odometry (ODOM), and a laser rangefinder (RF). The system introduces a confidence optimization algorithm based on weakening non-line-of-sight (NLOS) for UWB positioning, obtaining calibrated UWB positioning results, which are then used as a baseline to correct the positioning errors generated by the IMU and ODOM. The extended Kalman filter (EKF) algorithm is employed to fuse multi-sensor data. To validate the feasibility of the system, experiments were conducted in a Chinese solar greenhouse. The results show that the proposed NLOS confidence optimization algorithm significantly improves UWB positioning accuracy by 60.05%. At a speed of 0.1 m/s, the root mean square error (RMSE) for lateral deviation is 0.038 m and for course deviation is 4.030°. This study provides a new approach for greenhouse positioning and navigation technology, achieving precise positioning and navigation in complex commercial greenhouse environments and narrow aisles, thereby laying a foundation for the intelligent development of greenhouses. [ABSTRACT FROM AUTHOR]

Building Information Modeling (BIM) has recently been widely applied in the Architecture, Engineering, and Construction Industry (AEC). BIM graphical information can provide a more intuitive display of the building and its contents. However, during the Operation and Maintenance (O&M) stage of the building lifecycle, changes may occur in the building's contents and cause inaccuracies in the BIM model, which could lead to inappropriate decisions. This study aims to address this issue by proposing a novel approach to creating 3D point clouds for updating as-built BIM models. The proposed approach is based on Pedestrian Dead Reckoning (PDR) for an Inertial Measurement Unit (IMU) integrated with a Mobile Laser Scanner (MLS) to create room-based 3D point clouds. Unlike conventional methods previously undertaken where a Terrestrial Laser Scanner (TLS) is used, the proposed approach utilizes low-cost MLS in combination with IMU to replace the TLS for indoor scanning. The approach eliminates the process of selecting scanning points and leveling of the TLS, enabling a more efficient and cost-effective creation of the point clouds. Scanning of three buildings with varying sizes and shapes was conducted. The results indicated that the proposed approach created room-based 3D point clouds with centimeter-level accuracy; it also proved to be more efficient than the TLS in updating the BIM models. [ABSTRACT FROM AUTHOR]

Functional electrical stimulation (FES) is a rehabilitation and assistive technique used for stroke survivors. FES systems mainly consist of sensors, a control algorithm, and a stimulation unit. However, there is a critical need to reassess sensing and control techniques in FES systems to enhance their efficiency. This SLR was carried out following the PRISMA 2020 statement. Four databases (PubMed, Scopus, Web of Science, Wiley Online Library) from 2010 to 2024 were searched using terms related to sensing and control strategies in FES systems. A total of 322 articles were chosen in the first stage, while only 60 of them remained after the final filtering stage. This systematic review mainly focused on sensor techniques and control strategies to deliver FES. The most commonly used sensors reported were inertial measurement units (IMUs), 45% (27); biopotential electrodes, 36.7% (22); vision-based systems, 18.3% (11); and switches, 18.3% (11). The control strategy most reported is closed-loop; however, most of the current commercial FES systems employ open-loop strategies due to their simplicity. Three main factors were identified that should be considered when choosing a sensor for gait-oriented FES systems: wearability, accuracy, and affordability. We believe that the combination of computer vision systems with artificial intelligence-based control algorithms can contribute to the development of minimally invasive and personalized FES systems for the gait rehabilitation of patients with FDS. [ABSTRACT FROM AUTHOR]

Background: This scoping review summarizes the tasks and outcomes in dynamic and functional balance assessments of individuals with chronic ankle instability, focusing on the physiological and biomechanical characteristics. Method: A comprehensive literature search was conducted in PubMed, Scopus, Web of Science, and MEDLINE databases in September 2023 and revised in April 2024. Studies evaluating dynamic and functional balance in chronic ankle instability using clinical tests, as well as biomechanical and physiological outcomes, were included. Results: Out of 536 publications, 31 met the screening criteria. A history of ankle sprain was the main focus of the inclusion criteria (28 articles, 90%). The star excursion balance test, emphasizing maximum reach distance, was the most common quantitative task (12 articles, 66%). Physiological data mainly came from electromyography studies (7 articles, 23%), while biomechanical variables were often assessed through center of pressure studies using force plates (17 articles, 55%). Conclusions: The preferred quantitative clinical assessment was the star excursion balance test, focusing on normalized reach outcomes. Qualitative functional balance assessments emphasize landing activities and center of pressure displacement. Electromyography is commonly used to analyze the tibialis anterior and peroneus longus muscles. However, there is a lack of qualitative data on dynamic balance control, including morphological characteristics and the center of mass adaptation. [ABSTRACT FROM AUTHOR]

In this paper, the accuracy of inertial sensor orientation relative to the level frame is improved through optimal tuning of a complementary filter by a genetic algorithm. While constant filter gains have been used elsewhere, these may introduce errors under dynamic motions when gyroscopes should be trusted more than accelerometers. Optimal gains are prescribed by a Mamdani fuzzy rule base whose membership functions are found using a genetic algorithm and experimental data. Furthermore, model fitness is not based directly on orientation but the error between estimated and ground truth velocities. This paper has three interrelated novel elements. The main novelty is the indirect tuning method, which is simple, low-cost and requires a single camera and inertial sensor. The method is shown to increase tracking accuracy compared with popular baseline filters. Secondary novel elements are the bespoke genetic algorithm and the time agnostic velocity error metric. The contributions from this work can help improve the localization accuracy of assets and human personnel. This research has a direct impact in command and control by improving situational awareness and the ability to direct assets to safe locations using safer routes. This results in increasing safety in applications such as firefighting and battlespace.

Action recognition in tennis plays a crucial role for athletes and coaches, aiding in understanding and evaluating the players' skill levels to formulate more effective training plans and tactical strategies. To enhance the recognition and grading of tennis player actions, this study introduces the use of inertial measurement units and flexible resistive sensors for data collection. An improved Support Vector Machine is employed for data classification to achieve efficient action recognition. The results demonstrated that the proposed classification algorithm achieved an average accuracy of 95.35 % in recognizing actions of elite athletes, with the highest accuracy (96.38 %) observed in forehand strokes. In the case of sub-elite athletes, the algorithm achieved an impressive average accuracy of 97.67 %. For amateur enthusiasts, the algorithm exhibited an average accuracy of 94.08 %. Furthermore, elite athletes exhibited larger peak values in the three-axis acceleration waveform during ball striking. Specifically, the absolute peak value of acceleration in the Y-axis for elite athletes reached 78 m/s², representing an increase of 39 m/s² and 8 m/s² compared to the other two levels of athletes, respectively. Additionally, on the X and Z axes, elite athletes' acceleration peak values reached 59 m/s² and 78 m/s², significantly higher than those of sub-elite athletes and amateur enthusiasts. Moreover, the acceleration curves of elite athletes demonstrated a higher overall regularity. These findings indicate that the proposed action recognition method has a significant impact on recognition and evaluation, providing valuable insights for action recognition and assessment across various domains and advancing the application of artificial intelligence technology in the field of sports.

Motion blur is a problem that degrades the visual quality of images for human perception and also challenges computer vision tasks. While existing studies mostly focus on deblurring algorithms to remove uniform blur due to their computational efficiency, such approaches fail when faced with non-uniform blur. In this study, we propose a novel algorithm for motion deblurring that utilizes an adaptive mesh-grid approach to manage non-uniform motion blur with a focus on reducing the computational cost. The proposed method divides the image into a mesh-grid and estimates the blur point spread function (PSF) using an inertial sensor. For each video frame, the size of the grid cells is determined adaptively according to the in-frame spatial variance of blur magnitude which is a proposed metric for the blur non-uniformity in the video frame. The adaptive mesh-size takes smaller values for higher variances, increasing the spatial accuracy of the PSF estimation. Two versions of the adaptive mesh-size algorithm are studied, optimized for either best quality or balanced performance and computation cost. Also, a trade-off parameter is defined for changing the mesh-size according to application requirements. The experiments, using real-life motion data combined with simulated motion blur demonstrate that the proposed adaptive mesh-size algorithm can achieve 5% increase in PSNR quality gain together with a 19% decrease in computation time on the average when compared to the constant mesh-size method.

Wijittra Liengswangwong,1 Natcha Lertviboonluk,1 Chaiyaporn Yuksen,1 Parunchaya Jamkrajang,2 Weerawat Limroongreungrat,2 Atipong Mongkolpichayaruk,2 Chetsadakon Jenpanitpong,1 Sorawich Watcharakitpaisan,1 Chantarat Palee,1 Picharee Reechaipichitkool,1 Suchada Thaipasong1 1Department of Emergency Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand; 2College of Sports Science and Technology, Mahidol University, Nakhon Pathom, ThailandCorrespondence: Chaiyaporn Yuksen, Department of Emergency Medicine, Faculty of medicine, Ramathibodi Hospital, Mahidol University, 270 Rama VI Road, Thung Phaya Thai, Bangkok, Ratchathewi, 10400, Thailand, Email chaipool0634@hotmail.comBackground: The assessment of cervical spine motion is critical for out-of-hospital patients who suffer traumatic spinal cord injuries, given the profound implications such injuries have on individual well-being and broader public health concerns. 3D Optoelectronic systems (BTS SmartDX) are standard devices for motion measurement, but their price, complexity, and size prevent them from being used outside of designated laboratories. This study was designed to evaluate the accuracy and reliability of an inertial measurement unit (IMU) in gauging cervical spine motion among healthy volunteers, using a 3D optoelectronic motion capture system as a reference.Methods: Twelve healthy volunteers participated in the study. They underwent lifting, transferring, and tilting simulations using a long spinal board, a Sked stretcher, and a vacuum mattress. During these simulations, cervical spine angular movements—including flexion-extension, axial rotation, and lateral flexion—were concurrently measured using the IMU and an optoelectronic device. We employed the Wilcoxon signed-rank test and the Bland-Altman plot to assess reliability and validity.Results: A single statistically significant difference was observed between the two devices in the flexion-extension plane. The mean differences across all angular planes ranged from − 1.129° to 1.053°, with the most pronounced difference noted in the lateral flexion plane. Ninety-five percent of the angular motion disparities ascertained by the SmartDX and IMU were less than 7.873° for the lateral flexion plane, 11.143° for the flexion-extension plane, and 25.382° for the axial rotation plane.Conclusion: The IMU device exhibited robust validity when assessing the angular motion of the cervical spine in the axial rotation plane and demonstrated commendable validity in both the lateral flexion and flexion-extension planes.Keywords: reliability, validity, inertial measurement unit, SmartDX, cervical spine angular motion

Abstract Background The two most commonly instrumented gait analysis tools used are Optical Motion Capture systems (OMC) and Inertial Measurement Units (IMU). To date, OMC based gait analysis is considered the gold-standard. Still, it is space-, cost-, and time-intense. On the other hand IMU systems are more cost- and time effective but simulate the whole foot as a single segment. To get a more detailed model of the foot and ankle, a new 2-segment foot model using IMU was developed, comparable to the multi-segment foot models assessed by OMC. Research question Can an IMU based 2-segment foot model be developed to provide a more detailed representation of the foot and ankle kinematics? Methods To establish a 2-segment foot model, in addition to the previous 1-segment foot model an IMU sensor was added to the calcaneus. This allowed the differentiation between the hindfoot and forefoot kinematics. 30 healthy individuals (mean age 27 ± 7 years) were recruited to create a norm data set of a healthy cohort. Moreover, the kinematic data of the 2-segment foot model were compared to those of the traditional 1-segment foot model using statistical parametric mapping. Results The 2-segment foot model proved to be applicable. Furthermore, it allowed for a more detailed representation of the foot and ankle joints, similar to other multi-segment foot model. The healthy cohort’s norm data set showed a homogeneous motion pattern for gait. Conclusion The 2-segment foot model allows for an extension of IMU-based gait analysis. Futures studies must prove the reliability and validity of the 2-segment foot model in healthy and pathologic situations. Level of evidence Level II.

Compass errors can be regarded as a deviation of the vessel from the expected heading. Gyrocompass errors are randomly oscillating in nature, and it is difficult to describe the behaviour of a gyrocompass sufficiently accurately using mathematical relationships. Fibre-optic gyroscopes have no mechanical components, so the variability in their indications has a different nature; the computational processes and inertial sensors used cause certain types of errors. Thus far, compass studies have focused on presenting absolute errors in the time domain. However, compasses exhibit specific characteristics in the frequency domain that affect the amplitude of their deviation. This leads to the issue of identifying the oscillatory spectrum of errors in the operation of such compasses, and how this spectrum is impacted by the dynamic movement of the vessel. We attempt to assess this phenomenon by means of measurements taken on board the training and research vessel M/S NAWIGATOR XXI. The application of a fast Fourier transform allows for calculation of the absolute compass errors in the frequency domain, meaning that the frequency of occurrence of errors can be observed as noise against the background of the useful signal. Our results confirm the value of applying a finite impulse response filter, which is used to filter out noise in the form of absolute compass errors from the useful signal background. The convolution function proposed here considerably extends the possibilities for analysing the signal spectrum in the frequency domain when testing for the accuracy of compass device indications, and enables the elimination of random errors with a low frequency of occurrence..