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Purpose: This study aimed at evaluating the effect of level of competition and drill typology on loads during the preseason period in male volleyball players. Methods: Internal (percentage of peak heart rate [HR] and summated HR zone) and external (PlayerLoad per minute, total and high accelerations per minute [tACCmin and hACCmin], decelerations per minute [tDECmin and hDECmin], and jumps per minute [tJUMPmin and hJUMPmin]) loads were monitored across a 5-week preseason period in 12 Division 1 (age: 22.5 [3.9] y; stature: 188 [6.2] cm; body mass: 85 [11.6] kg; training experience: 9.4 [4.2] y) and 12 Division 2 (age: 20.7 [2.9] y; stature: 186 [6.2] cm; body mass: 77.8 [9.6] kg; training experience: 5.6 [2.3] y) male volleyball players. Furthermore, differences in load were assessed for each drill typology (warm-up, conditioning, technical, tactical, and integral). Results: No effects (P >.05) of level of competition on the internal (except for summated HR zone, P =.05) and external loads (except for tJUMPmin, P =.002) were found. Differently, drill typologies showed an effect (P <.001) on all the investigated internal- and external-load measures. The main post hoc results revealed higher (P <.05) percentage of peak HR, summated HR zone, PlayerLoad per minute, and tACCmin in warm-up and conditioning drills, while higher (P <.05) hDECmin and hJUMPmin were found in tactical and integral drills. Conclusions: These results suggest that volleyball coaches use warm-up and conditioning drills when aiming at increasing the internal loads, PlayerLoad per minute, and tACCmin, while tactical and integral drills should be preferred to enhance the number of hDECmin and hJUMPmin. [ABSTRACT FROM AUTHOR]

Purpose: Although running traditionally relies on critical speed (CS) as an indicator of critical intensity, portable inertial measurement units offer a potential solution for estimating running mechanical power to assess critical power (CP) in runners. The purpose of this study was to determine whether CS and CP differ when assessed using the Stryd device, a portable inertial measurement unit, and if 2 running bouts are sufficient to determine CS and CP. Methods: On an outdoor running track, 10 trained runners ( V ˙ O 2 max , 59.0 [4.2] mL·kg−1·min−1) performed 3 running time trials (TT) between 1200 and 4400 m on separate days. CS and CP were derived from 2-parameter hyperbolic speed–time and power–time models, respectively, using 2 (CS2TT and CP2TT) and 3 (CS3TT and CP3TT) TTs. Subsequently, runners performed constant-intensity running for 800 m at their calculated CS3TT and CP3TT. Results: Running at the calculated CS3TT speed (3.88 [0.44] m·s−1) elicited an average Stryd running power (271 [28] W) not different from the calculated CP3TT (270 [28]; P =.940; d = 0.02), with excellent agreement between the 2 values (intraclass correlation coefficient =.980). The CS2TT (3.97 [0.42] m·s−1) was not higher than CS3TT (3.89 [0.44] m·s−1; P =.178; d = 0.46); however, CP2TT (278 [29] W) was greater than CP3TT (P =.041; d = 0.75). Conclusion: The running intensities at CS and CP were similar, supporting the use of running power (Stryd) as a metric of aerobic fitness and exercise prescription, and 2 trials provided a reasonable, albeit higher, estimate of CS and CP. [ABSTRACT FROM AUTHOR]

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]

The PULSE workload monitor (PULSE) is a commercially available, wearable device that assesses upper extremity kinematics and kinetics during overhand pitching with three metrics to improve pitching performance. The purpose of this study was to determine the inter-tester and intra-tester reliability of the PULSE metrics when applied by a lay consumer versus trained researcher. A total of 14 healthy, adult male (age: 44.21 ± 17.54 years) baseball players were fitted with the PULSE under two application conditions, participant application and researcher application of the device. Each participant performed seven pitches of three pitch types, including fastball, curveball, and change-up, per application condition. The protocol was repeated during a second session one week later. Intraclass correlation coefficients (ICC) were determined to examine inter-tester and intra-tester reliability of the PULSE metrics between placement conditions and across sessions. For all pitch types, inter-tester reliability (ICC > 0.758) and intra-tester reliability (participant application: ICC > 0.710; researcher application: ICC > 0.890) were strong, indicating that the device is reliable when placed by a lay consumer. Findings suggest that PULSE may serve as an easily accessible, wearable device for reproducing pitching metrics that can inform consumer training. [ABSTRACT FROM AUTHOR]

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]

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]

Purpose: Whole-body vibration (WBV) has been reported to influence performance improvement. However, its effects may vary based on device parameters and body positioning. This study aims to assess the effects of two frequencies and two positions on jump kinetic variables. Methods: Thirty-four healthy non-athlete females underwent four WBV protocols involving different combinations of medium (30 Hz) and high (50 Hz) frequencies, as well as static and dynamic squat positions. Participants performed three counter-movement drop jumps before, 1 min, and 10 min after each protocol. Jump variables were extracted using acceleration data from an inertial measurement unit attached to participants' waists. Results: Three-way repeated measure ANOVA results revealed a significant position effect on maximum contact power (p = 0.046) and a significant time effect on maximum contact force (p = 0.010), concentric contact impulse (p < 0.001), jump height (p = 0.014), and the reactive strength index (p = 0.007). Bonferroni analysis showed an increase in maximum contact power in the dynamic squat protocols. However, there was a decrease in maximum contact force, jump height, and the reactive strength index, along with an increase in contact impulse in the concentric phase for all frequencies and squat positions. Conclusion: While dynamic squatting increased maximum power during the concentric phase, highlighting the importance of dynamic contractions during vibration, temporary declines in other key jump variables suggest that acute WBV effects in non-athlete subjects might negatively impact overall function. Caution is advised when considering the immediate effects of WBV in this group. [ABSTRACT FROM AUTHOR]

Due to rapid advancements in robotics technology, mobile robots are now utilized across various industries and applications. Understanding the terrain on which a robot operates can greatly aid its navigation and movement adjustments, ultimately minimizing potential hazards and ensuring seamless operation. This study aims to identify the specific terrain on which a mobile robot travel. Data was gathered using an inertial measurement unit (IMU) installed on the robot for experimental testing. The key contributions of this research are twofold: firstly, the implementation and evaluation of various machine learning techniques using the IMU sensor dataset, comparing their performance using metrics like accuracy, precision, recall, and F1-score. Secondly, after assessing the different techniques, the most effective one is chosen for the final system implementation. Following the experimental evaluation of machine learning techniques, it was determined that the light gradient boosting machine (LGBM) classifier outperformed the others. Consequently, LGBM was utilized for the proposed system's implementation, achieving a 91% accuracy in surface classification. The experimental results highlight the efficiency and viability of the proposed system. [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]

Background Objectives Methods Results Conclusions Head tremor poses diagnostic problems, especially when present as an isolated or predominant symptom.To assess how maneuvers activating upper limb postural tremor can help differentiate head tremor in essential tremor (ET) from dystonic tremor (DT) in cervical dystonia.48 patients with head tremor (25 ET, 23 DT), underwent clinical examination and accelerometric evaluation of head and upper limb tremor during routine tremor‐inducing tasks.While accelerometric power and clinical scores of head tremor did not significantly differ between patient groups, task‐induced variations revealed distinctions. ET patients exhibited increased head tremor power and clinical scores during forward outstretched and lateral wing‐beating arm positions, unlike DT patients. Coherence between head and upper limb tremor remained consistent. Tremor stability index showed no significant differences.Task‐induced changes in head tremor could aid in distinguishing between ET and DT. Further research is needed to refine diagnostic approaches for head tremor. [ABSTRACT FROM AUTHOR]

The countermovement vertical jump height, flight time, and jump duration are used to assess athletic performance. Force-plate and motion-capture cameras are used to estimate these parameters, yet, their application is limited to dedicated lab environments. Despite the potential of inertial measurement units (IMU) for estimating the jump height, their accuracy has not been validated. This study investigates the accuracy of our proposed method to estimate the jump height using a sacrum-mounted IMU, during countermovement jumping. Eleven individuals performed four jumps each. To obtain the jump height, we transformed the IMU readouts into anatomical planes, and double-integrated the vertical acceleration after correction for zero velocity and vertical displacement. The accuracy of jump height obtained by IMU was compared to force-plate and motion-capture cameras during jumps without arm swing (mean error (standard deviation) of 0.3(2.2) cm and 1.0(3.0) cm, and correlation coefficient of 0.83 and 0.82, respectively) and during jumps with arm swing (−1.1(2.1) cm and 0.5(1.9) cm, and 0.92 and 0.89). The correlation coefficients were high, and the errors were comparable to the difference between the jump height obtained by force-plate and cameras. Therefore, a sacrum-mounted IMU can be recommended for in-field assessment of countermovement jump with and without arm swing. [ABSTRACT FROM AUTHOR]

Greater understanding of differences in technique between runners may allow more beneficial feedback related to improving performance and decreasing injury risk. The purpose of this study was to develop and test a support vector machine classifier, which could automatically differentiate running technique between experienced and novice participants using only wearable sensor data. Three-dimensional linear accelerations and angular velocities were collected from six wearable sensors secured to current common smart device locations. Cross-validation was used to test the classification accuracy of models trained with a variety of combinations of sensor locations, with participants running at different speeds. Average classification accuracies ranged from 71.3% to 98.4% across the sensor combinations and running speeds tested. Models trained with only a single sensor location still showed effective classification. With the models trained with only upper arm data achieving an average accuracy of 96.4% across all tested running speeds. A post-hoc comparison of biomechanical variables between the two subgroups showed significant differences in upper body biomechanics throughout the stride. Both the methodology used to perform the classifications and the biomechanical differences identified could prove useful when aiming to shift a novice runner's technique towards movement patterns more akin to those with greater experience. [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]

The concurrent validity and between-unit reliability of a foot-mounted inertial measurement unit (F-IMU) was investigated during linear and change of direction running drills. Sixteen individuals performed four repetitions of two drills (maximal acceleration and flying 10 m sprint) and five repetitions of a multi-directional movement protocol. Participants wore two F-IMUs (Playermaker) and 10 retro-reflective markers to allow for comparisons to the criterion system (Qualisys). Validity of the F-IMU derived velocity was assessed via root-mean-square error (RMSE), 95% limits of agreement (LoA) and mean difference with 95% confidence interval (CI). Between-unit reliability was assessed via intraclass correlation (ICC) with 90% CI and 95% LoA. The mean difference for instantaneous velocity for all participants and drills combined was −0.048 ± 0.581 m ∙ s−1, the LoA were from −1.09 to −1.186 m ∙ s−1 and RMSE was 0.583 m ∙ s−1. The ICC ranged from 0.84 to 1, with LoA from −7.412 to 2.924 m ∙ s−1. Differences were dependent on the reference speed, with the greatest absolute difference (−0.66 m ∙ s−1) found at velocities above 7 m ∙ s−1. Between-unit reliability of the F-IMU ranges from good to excellent for all locomotor characteristics. Playermaker has good agreement with 3D motion capture for velocity and good to excellent between-unit reliability. [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]

The overlap between motor and cognitive signs resulting from posterior parietal cortex (PPC) and cerebellar lesions can mask their relative contribution in the sensorimotor integration process. This study aimed to identify distinguishing motor and cognitive features to disentangle PPC and cerebellar involvement in two sensorimotor-related functions: gait and body schema representation. Thirty healthy volunteers were enrolled and randomly assigned to PPC or cerebellar stimulation. Sham stimulation and 1 Hz-repetitive-Transcranial-Magnetic-Stimulation were delivered over P3 or cerebellum before a balance and a walking distance estimation task. Each trial was repeated with eyes open (EO) and closed (EC). Eight inertial measurement units recorded spatiotemporal and kinematic variables of gait. Instability increased in both groups after real stimulation: PPC inhibition resulted in increased instability in EC conditions, as evidenced by increased ellipse area and range of movement in medio-lateral and anterior–posterior (ROMap) directions. Cerebellar inhibition affected both EC (increased ROMap) and EO stability (greater displacement of the center of mass). Inhibitory stimulation (EC vs. EO) affected also gait spatiotemporal variability, with a high variability of ankle and knee angles plus different patterns in the two groups (cerebellar vs parietal). Lastly, PPC group overestimates distances after real stimulation (EC condition) compared to the cerebellar group. Stability, gait variability, and distance estimation parameters may be useful clinical parameters to disentangle cerebellar and PPC sensorimotor integration deficits. Clinical differential diagnosis efficiency can benefit from this methodological approach. [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]

Shoulder injuries are a common problem in handball. One likely cause of such injuries is excessive throwing. However, it is difficult to measure the number of player throws in large cohort studies using existing methods accurately. Therefore, the purpose of this study is to develop and validate a method for identifying overhead throws using a low-cost inertial measurement unit (IMU) worn on the wrist. In a two-stage approach, we developed a threshold-based automatic identification method for overhead throws in a laboratory study using the IMU. Subsequently, we validated the suggested thresholds in a field setting by comparing throws identified by the threshold-method to throws identified by video recordings of handball practices. The best set of threshold values resulted in a per-player median sensitivity of 100% (range: 84–100%) and a median positive predictive value (PPV) of 96% (range: 86–100%) in the development study. In the validation study, the per-player median sensitivity dropped to 78% sensitivity (range: 52–91%), while the per-player median PPV dropped to 79% (range: 47–90%). The proposed method is a promising method for automatically identifying handball throws in a cheap and feasible way. [ABSTRACT FROM AUTHOR]

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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]