Abstract
New artificial intelligence- (AI) based marker-less motion capture models provide a basis for quantitative movement analysis within healthcare and eldercare institutions, increasing clinician access to quantitative movement data and improving decision making. This research modelled, simulated, designed, and implemented a novel marker-less AI motion-analysis approach for institutional hallways, a Smart Hallway. Computer simulations were used to develop a system configuration with four ceiling-mounted cameras. After implementing camera synchronization and calibration methods, OpenPose was used to generate body keypoints for each frame. OpenPose BODY25 generated 2D keypoints, and 3D keypoints were calculated and postprocessed to extract outcome measures. The system was validated by comparing ground-truth body-segment length measurements to calculated body-segment lengths and ground-truth foot events to foot events detected using the system. Body-segment length measurements were within 1.56 (SD = 2.77) cm and foot-event detection was within four frames (67 ms), with an absolute error of three frames (50 ms) from ground-truth foot event labels. This Smart Hallway delivers stride parameters, limb angles, and limb measurements to aid in clinical decision making, providing relevant information without user intervention for data extraction, thereby increasing access to high-quality gait analysis for healthcare and eldercare institutions.
Highlights
Motion analysis provides information and insights into the quality of movement for rehabilitation, performance analysis of professional athletes, and animation for video games or computer-generated imagery in movies
Stride analysis and gait information are used in clinical decision making to optimally care for patients
Based on preliminary research [16], the open-source OpenPose BODY25 model was used for all body keypoint inferences
Summary
Motion analysis provides information and insights into the quality of movement for rehabilitation, performance analysis of professional athletes, and animation for video games or computer-generated imagery in movies. Of particular interest is improving the quality of information provided to healthcare professionals. Stride analysis and gait information are used in clinical decision making to optimally care for patients. Human motion analyses can aid in understanding rehabilitation progress [1], fall risk [1,2], progression of neurodegenerative diseases [3], and classifying gait patterns [4,5,6]. Equipment, access, space, and human-resource requirements limit quantitative movement assessment within healthcare and eldercare environments. A Smart Hallway implementation could automatically record movement as a person walks through a hallway within an institution so that a therapist or physician can review walking parameters before their appointment
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