Search Results (4)

Accurate and scalable gait assessment is essential for clinical and research applications, including fall risk evaluation, rehabilitation monitoring, and early detection of neurodegenerative diseases. While electronic walkways remain the clinical gold standard, their high cost and limited portability restrict widespread use. Wearable inertial measurement units (IMUs) and markerless depth cameras have emerged as promising alternatives; however, prior studies have typically assessed these systems under tightly controlled conditions, with single participants in view, limited marker sets, and without direct cross-technology comparisons. This study addresses these gaps by simultaneously evaluating three sensing technologies—APDM wearable IMUs (tested in two separate configurations: foot-mounted and lumbar-mounted) and the Azure Kinect depth camera—against ProtoKinetics Zeno™ Walkway Gait Analysis System in a realistic clinical environment where multiple individuals were present in the camera’s field of view. Gait data from 20 older adults (mean age $70.06\pm 9.45$ years) performing Single-Task and Dual-Task walking trials were synchronously captured using custom hardware for precise temporal alignment. Eleven gait markers spanning macro, micro-temporal, micro-spatial, and spatiotemporal domains were compared using mean absolute error (MAE), Pearson correlation (r), and Bland–Altman analysis. Foot-mounted IMUs demonstrated the highest accuracy (MAE $=0.00$–$6.12$, $r=0.92$–$1.00$), followed closely by the Azure Kinect (MAE $=0.01$–$6.07$, $r=0.68$–0.98). Lumbar-mounted IMUs showed consistently lower agreement with the reference system. These findings provide the first comprehensive comparison of wearable and depth-sensing technologies with a clinical gold standard under real-world conditions and across an extensive set of gait markers. The results establish a foundation for deploying scalable, low-cost gait assessment systems in diverse healthcare contexts, supporting early detection, mobility monitoring, and rehabilitation outcomes across multiple patient populations. Full article

Background/Objectives: Measuring the physical functioning of older hip fracture patients using wearables is desirable, with physical activity monitoring offering a promising approach. However, it is first important to assess physical activity in healthy older adults. This study quantifies physical functioning with physical activity parameters and assesses those parameters in community-dwelling older adults. The results are compared with the results from one case participant 2 months post-hip fracture surgery. Methods: Twenty-four community-dwelling older adults (aged ≥ 80) participated. The acts of moving around the house, toileting, getting in/out of bed, and preparing meals was quantified by total time, time spent sitting, standing, and walking, number of transfers, and intensity of physical activity. MOX and APDM sensors measured the intensity of physical activity, with the tasks performed in a living lab while video-recorded. The case participant’s total time and intensity of physical activity were measured for walking to a door and getting in/out of bed. Results: Preparing meals showed the longest total time and time spent standing/walking, while moving around the house and getting in/out of bed had the highest intensity of physical activity. Only getting in/out of bed required sitting. The physical activity parameters varied among participants, with very active participants completing tasks faster. The case participant had longer total times and lower intensities of physical activity two months post-surgery compared to before the fracture. Conclusions: This study provides initial insights into the physical activity levels of community-dwelling older adults. It represents the beginning of more efficient and continuous monitoring of physical functioning. Full article

Literature suggests that anxiety affects gait and balance among young adults. However, previous studies using machine learning (ML) have only used gait to identify individuals who report feeling anxious. Therefore, the purpose of this study was to identify individuals who report feeling anxious at that time using a combination of gait and quiet balance ML. Using a cross-sectional design, participants (n = 88) completed the Profile of Mood Survey-Short Form (POMS-SF) to measure current feelings of anxiety and were then asked to complete a modified Clinical Test for Sensory Interaction in Balance (mCTSIB) and a two-minute walk around a 6 m track while wearing nine APDM mobility sensors. Results from our study finds that Random Forest classifiers had the highest median accuracy rate (75%) and the five top features for identifying anxious individuals were all gait parameters (turn angles, variance in neck, lumbar rotation, lumbar movement in the sagittal plane, and arm movement). Post-hoc analyses suggest that individuals who reported feeling anxious also walked using gait patterns most similar to older individuals who are fearful of falling. Additionally, we find that individuals who are anxious also had less postural stability when they had visual input; however, these individuals had less movement during postural sway when visual input was removed. Full article

The objective of this study was to use machine learning to identify feelings of energy and fatigue using single-task walking gait. Participants (n = 126) were recruited from a university community and completed a single protocol where current feelings of energy and fatigue were measured using the Profile of Moods Survey–Short Form approximately 2 min prior to participants completing a two-minute walk around a 6 m track wearing APDM mobility monitors. Gait parameters for upper and lower extremity, neck, lumbar and trunk movement were collected. Gradient boosting classifiers were the most accurate classifiers for both feelings of energy (74.3%) and fatigue (74.2%) and Random Forest Regressors were the most accurate regressors for both energy (0.005) and fatigue (0.007). ANCOVA analyses of gait parameters comparing individuals who were high or low energy or fatigue suggest that individuals who are low energy have significantly greater errors in walking gait compared to those who are high energy. Individuals who are high fatigue have more symmetrical gait patterns and have trouble turning when compared to their low fatigue counterparts. Furthermore, these findings support the need to assess energy and fatigue as two distinct unipolar moods as the signals used by the algorithms were unique to each mood. Full article