An optimized real-time hands gesture recognition based interface for individuals with upper-level spinal cord injuries

摘要

This paper presents a hand gesture-based interface to facilitate interaction with individuals with upper-level spinal cord injuries, and offers an alternative way to perform "hands-on'' laboratory tasks. The presented system consists of four modules: hand detection, tracking, trajectory recognition, and actuated device control. A 3D particle filter framework based on color and depth information is proposed to provide a more efficient solution to the independent face and hands tracking problem. More specifically, an interaction model utilizing spatial and motion information was integrated into the particle filter framework to tackle the "false merge'' and "false labeling'' problem through hand interaction and occlusion. To obtain an optimal parameter set for the interaction model, a neighborhood search algorithm was employed. An accuracy of 98.81 % was achieved by applying the optimal parameter set to the tracking module of the system. Once the hands were tracked successfully, the acquired gesture trajectories were compared with motion models. The dynamic time warping method was used for signals' time alignment, and they were classified by a CONDENSATION algorithm with a recognition accuracy of 97.5 %. In a validation experiment, the decoded gestures were passed as commands to a mobile service robot and a robotic arm to perform simulated laboratory tasks. Control policies using the gestural control were studied and optimal policies were selected to achieve optimal performance. The computational cost of each system module demonstrated a real-time performance.