Real-Time Hand Gesture Tracking for Human–Computer Interface Based on Multi-Sensor Data Fusion

Abstract

Cerebral palsy is one of the main factors leading to children’s disability. A large number of such children have hand motor dysfunction, such as limited range of motion, abnormal gestures, etc. Our goal is to design a prototype of wearable gesture training equipment for such children. For this purpose, this paper presents the development of a wireless smart glove to facilitate the accurate measurement of finger movement through the integration of multiple IMU sensors. Commercially available gaming gloves are not fitted with sufficient sensors for this particular application and most of them can only provide one degree of freedom for finger flexion and extension. In this paper, a data glove integrated with nine degree of freedom inertial sensors in conjunction with complex multi-sensor data fusion is developed. In our method, a single/dual state measurement update switching extended Kalman filter is proposed to estimate the spatial attitude of each finger segment. Through traversing the kinematic chain, a tree type hand dynamic model based on joint constraints is established to realize the real-time gesture tracking. Furthermore, a visual interface is developed to display the tracking effect of gestures, and the reliability of our algorithm is verified by optical contrast experiments, the repeatability of joint angle is also evaluated by kinematic analysis. In general, all the experimental results demonstrated that our proposed framework can accurately track the 3-D hand motion. This glove will help quantify joint stiffness and monitor patient progression during the rehabilitation training process.