Abstract
Capturing finger joint angle information has important applications in human–computer interaction and hand function evaluation. In this paper, a novel wearable data glove is proposed for capturing finger joint angles. A sensing unit based on a grating strip and an optical detector is specially designed for finger joint angle measurement. To measure the angles of finger joints, 14 sensing units are arranged on the back of the glove. There is a sensing unit on the back of each of the middle phalange, proximal phalange, and metacarpal of each finger, except for the thumb. For the thumb, two sensing units are distributed on the back of the proximal phalange and metacarpal, respectively. Sensing unit response tests and calibration experiments are conducted to evaluate the feasibility of using the designed sensing unit for finger joint measurement. Experimental results of calibration show that the comprehensive precision of measuring the joint angle of a wooden finger model is 1.67%. Grasping tests and static digital gesture recognition experiments are conducted to evaluate the performance of the designed glove. We achieve a recognition accuracy of 99% by using the designed glove and a generalized regression neural network (GRNN). These preliminary experimental results indicate that the designed data glove is effective in capturing finger joint angles.
Highlights
Published: 30 June 2021Human hands are closely related to our life
This paper presents a data glove for capturing hand finger joint angles. 14 sensing units based on a flexible grating strip are placed on the back of the glove to get the angles of distal interphalangeal (DIP) joints, proximal interphalangeal (PIP) joints, metacarpophalangeal (MCP) joints of all fingers
The results show that the 14 finger joint angles can be captured by the designed data glove
Summary
Human hands are closely related to our life. It is one of the most frequently used body parts for human interaction with the outside world. Basic hand motions include grasping, pinching, and stretching. The combination of these motions helps to complete a large number of tasks in everyday life. At present, monitoring and tracking of hand motions and application of these motions to human-computer interaction, rehabilitation training, sign language recognition, and teleoperation have attracted considerable attention from researchers. Wearable data gloves have attracted extensive attention because they are convenient to wear, free from environmental influences and do not restrict the user’s hand motions
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