The purpose of this study was to compare a heuristic feature identification algorithm with output from the Beta Process Auto Regressive Hidden Markov Model (BP-AR-HMM) utilizing minimally sampled (≤ 100 Hz) human locomotion data for identification of gait events prior to their occurrence. Data were collected from 16 participants (21-64 years) using a single gyroscopic sensor in an inertial measurement unit on the dorsum of the foot, across multiple locomotion modes, including level ground walking and running (across speeds 0.8 m s-1 - 3.0 m s-1), ramps and stairs. Identification of gait events, initial contact (IC) and toe off (TO) with the heuristic algorithm, was 94% across locomotion modes. The features identified prior to initial contact had a lead time of 186.32 ± 86.70 ms, while TO had a lead time of 63.96 ± 46.30 ms. The BP-AR-HMM identified features that indicated an impending IC and TO with 99% accuracy, with a lead time of 59.41 ± 54.41 ms for IC and 90.79 ± 35.51 ms for TO. These approaches are consistent in their identification of gait events and have the potential to be utilized for classification and prediction of locomotion mode.

The advancement of inertial measurement unit (IMU) technology has opened new opportunities for motion analysis, yet its widespread adoption in clinical practice remains constrained by the high costs of proprietary systems, lengthy setup procedures, and the need for specialized expertise. To address these challenges, we present a multi-IMU system designed with streamlined calibration, efficient data processing, and a focus on accessibility for patient-facing applications. Although initially developed for human gait analysis, the modular design of this system enables adaptability across diverse motion tracking scenarios. This work outlines the system’s technical framework, including protocols for data acquisition, derivation of gait variables, and considerations for user-friendly software deployment. We further illustrate its utility by measuring lower-limb gait kinematics in near-real time and providing stride-to-stride biofeedback using a single sensor. These initial results underscore the potential of this system for both laboratory-based gait assessment and rehabilitation interventions in clinical environments and future work will assess validation against traditional optical motion capture methods.

This study validates a clinically accessible approach for quantifying the Upper Extremity Reachable Workspace (UERW) using monocular AI-driven Markerless Motion Capture (MMC). Objective validation of such techniques for clinically oriented tasks is essential to support their adoption in clinical motion analysis. Nine adults without impairments performed the standardized UERW task, reaching targets distributed across a virtual sphere centered on the torso and displayed via VR headset. Movements were simultaneously captured with a marker-based system and eight FLIR cameras; monocular analysis was applied to two videos representing frontal and offset camera configurations. Agreement was assessed by comparing the percentage workspacereached across six of eight workspace octants between the systems. The frontal camera demonstrated strong agreement with the marker-based reference (mean bias: $0.61\pm 0.12$% reachspace per octant), whereas the offset view underestimated workspace reached $-5.66\pm 0.45$%. Depth-related errors in the frontal configuration were confined to posterior octants, whereas the offset view introduced inaccuracies in both contralateral and posterior octants. These findings support the feasibility of a frontal monocular camera for UERW assessment, particularly for anterior workspace evaluation. While posterior accuracy remains limited by depth estimation and anatomical occlusion errors, the overall results demonstrate clinical potential for practical, monocular-camera assessments.

The advancement of inertial measurement unit (IMU) technology has opened new opportunities for motion analysis, yet its widespread adoption in clinical practice remains constrained by the high costs of proprietary systems, lengthy setup procedures, and the need for specialized expertise. To address these challenges, we present a multi-IMU system designed with streamlined calibration, efficient data processing, and a focus on accessibility for patient-facing applications. Although initially developed for human gait analysis, the modular design of this system enables adaptability across diverse motion tracking scenarios. This work outlines the system’s technical framework, including protocols for data acquisition, derivation of gait variables, and considerations for user-friendly software deployment. We further illustrate its utility by measuring lower-limb gait kinematics in near-real time and providing stride-to-stride biofeedback using a single sensor. These initial results underscores the potential of this system for both laboratory-based gait assessment and rehabilitation interventions in clinical environments and future work will assess validation against traditional optical motion capture methods.

Background: Despite evidence that passive prosthesis mechanical properties can directly affect user experience, prosthetists have access to minimal information regarding the mechanical interactions between a prosthetic foot and proximal modular componentry. Objectives: This study quantified the stance phase mechanical behavior of a transtibial prosthetic system through the addition of passive modular componentry to a dynamic response (DR) foot. Study Design: Repeated measures, mechanical characterization. Methods: Maximum displacement and energy return were measured with a materials test machine simulating initial, mid, and terminal stances. Twelve conditions were tested: a DR foot in combination with a hydraulic ankle at 2 resistance settings and 3 different shock-absorbing pylons (SAPs). The roll-over shape of the DR foot with and without hydraulic ankle was measured using a test rig. Results: Adding modular passive components altered displacement and energy return, displaying independent and interaction effects. Generally, the hydraulic ankle and SAP reduced energy return (up to 18%) but decreased (up to 51%) and increased (up to 88%) displacement, respectively, while the combined properties were more complex. Roll-over shape radii decreased with increasing load for the foot alone but exhibited a nonlinear response with the addition of the ankle. Conclusions: Inclusion of modular components in a transtibial prosthetic system can have complex mechanical interactions that independently affect the system's response to load. It is important for clinicians to be aware of the cumulative effects of these interactions to inform the tuning of transtibial prosthesis mechanical behavior. Combinations of hydraulic ankles and SAPs can help clinicians adjust the prosthesis to achieve a balance between user comfort and energy return.