Rapid-Motion-Track: Markerless tracking of fast human motion with deep learning

Abstract

Human movement patterns reflect central nervous system function. Small deficits in repetitive fast movements, such as slightly slowed finger-tapping or mildly irregular rhythm of stepping, are often an early sign of a neurological disorder. Accessible tools that precisely measure the individual components of fast movements would thus enhance disease detection, monitoring and research. Deep learning-based computer vision methods applied to digital video-recordings hold promise but current state-of-the-art tools, including DeepLabCut (DLC) and other advanced models, fail to accurately track the fastest range of human movements, primarily due to image blur. To solve this, we developed a new end-to-end, Rapid-Motion-Track (RMT) computer vision tool. This study aimed to evaluate the accuracy of RMT compared to DLC and other advanced computer vision tools. 220 finger-tapping tests were performed at frequencies between 0.5Hz and 6Hz and recorded simultaneously with a standard 30 frames/sec 2D laptop camera and a high-speed 250 frames/sec 3D motion tracking system (ground-truth). Bland-Altman plots and paired Welch's t-test were used to quantify the validity of movement features extracted by computer vision methods with the ground-truth. The movement features extracted by RMT (e.g. frequency, speed, variance) exhibited high concurrent validity across all tapping-frequencies. RMT outperformed other computer vision methods for very fast movements >4Hz. RMT also robustly tracked other fast motions including sit-to-stand, head-turning, foot-tapping, and leg agility. This new tool provides an accurate method to precisely and automatically measure even the fastest and finest human movements. It holds potential of wide reach as digital cameras are ubiquitous in homes, clinics and research centres.