Capturing complex hand movements and object interactions using machine learning-powered stretchable smart textile gloves

Accurate real-time tracking of dexterous hand movements has numerous applications in human-computer interaction, the metaverse, robotics and tele-health. Capturing realistic hand movements is challenging because of the large number of articulations and degrees of freedom. Here we report accurate and dynamic tracking of articulated hand and finger movements using stretchable, washable smart gloves with embedded helical sensor yarns and inertial measurement units. The sensor yarns have a high dynamic range, responding to strains as low as 0.005% and as high as 155%, and show stability during extensive use and washing cycles. We use multi-stage machine learning to report average joint-angle estimation root mean square errors of 1.21 degrees and 1.45 degrees for intra- and inter-participant cross-validation, respectively, matching the accuracy of costly motion-capture cameras without occlusion or field-of-view limitations. We report a data augmentation technique that enhances robustness to noise and variations of sensors. We demonstrate accurate tracking of dexterous hand movements during object interactions, opening new avenues of applications, including accurate typing on a mock paper keyboard, recognition of complex dynamic and static gestures adapted from American Sign Language, and object identification. Accurate real-time tracking of dexterous hand movements and interactions has applications in human-computer interaction, the metaverse, robotics and tele-health. Capturing realistic hand movements is challenging due to the large number of articulations and degrees of freedom. Tashakori and colleagues report accurate and dynamic tracking of articulated hand and finger movements using machine-learning powered stretchable, washable smart gloves.