Markerless motion capture of hand and object tracking for rehabilitation - Lim, Guan Ming (Nanyang Technological University, 2024)
Capturing hand motion has a myriad of applications ranging from entertainment to healthcare. Current approaches found in hand rehabilitation often involve the placement of a goniometer, wearable sensors, or markers which can be inefficient, hinder natural hand movements, or require time-consuming setup and data post-processing. A promising alternative is markerless motion capture, which seeks to estimate hand poses directly from images. However, existing methods face challenges related to real-time performance, accuracy, and robustness to hand-object interaction. Therefore, this thesis aims to enhance the efficiency and accuracy of markerless motion capture of hand and object tracking for rehabilitation.