Design and Manufacture of Data Gloves for Rehabilitation Training and Gesture Recognition Based on Flexible Sensors

This work takes the production and usage scenarios of the data glove as the research object and studies the method of applying the flexible sensor to the data glove. Many studies are also devoted to exploring the transplantation of flexible sensors to data gloves. However, this type of research still lacks the display of specific application scenarios such as gesture recognition or hand rehabilitation training. A small amount of experimental data and theoretical analysis are difficult to promote the development of flexible sensors and flexible data gloves design schemes. Therefore, this study uses the self-made flexible sensor of the research group as the core sensing unit to produce a flexible data glove to monitor the bending changes of the knuckles and then use it for simple gesture recognition and rehabilitation training.

A Noble Classification Framework for Data Glove Classification of a Large Number of Hand Movements

The recognition of hand movements is an important method for human-computer interaction (HCI) technology, and it is widely used in virtual reality and other HCI areas. While many valuable efforts have been made, efficient ways to capture over 20 types of hand movements with high accuracy by one data glove are still lacking. This paper addresses a new classification framework for 52 hand movements. This classification framework includes the following two parts: the movement detection algorithm and the movement classification algorithm. The fine K-nearest neighbor (Fine KNN) is the core of the movement detection algorithm. The movement classification algorithm is composed of downsampling in data preparation and a new deep learning network named the DBDF network. Bidirectional Long Short-Term Memory (BiLSTM) is the main part of the DBDF network. The results of experiments using the Ninapro DB1 dataset demonstrate that our work can classify more types of hand movements than related algorithms with a precision of 93.15%.

MagIK: a Hand Tracking Magnetic Positioning System based on a Kinematic Model of the Hand

In this paper we present a hand tracking system based on magnetic positioning. A single magnetic node is mounted on each fingertip, and two magnetic nodes on the back side of the hand. A fixed array of receiving coils is used to detect the magnetic field, from which it is possible to infer position and orientation of each magnetic node. A kinematic model of the whole hand has been developed. Starting from the positioning data of each magnetic node, the kinematic model can be used to calculate position and flexion angle of each finger joint, plus the position and orientation of the hand in space. Relying on magnetic fields, the hand tracking system can work also in nonline-of-sight conditions. The gesture reconstruction is validated by comparing it with a commercial hand tracking system based on a depth camera. The system requires a small amount of electronics to be mounted on the hand. This would allow building a light and comfortable data glove that could be used for several purposes: human-machine interface, sign language recognition, diagnostics, and rehabilitation.

MagIK: a Hand Tracking Magnetic Positioning System based on a Kinematic Model of the Hand

In this paper we present a hand tracking system based on magnetic positioning. A single magnetic node is mounted on each fingertip, and two magnetic nodes on the back side of the hand. A fixed array of receiving coils is used to detect the magnetic field, from which it is possible to infer position and orientation of each magnetic node. A kinematic model of the whole hand has been developed. Starting from the positioning data of each magnetic node, the kinematic model can be used to calculate position and flexion angle of each finger joint, plus the position and orientation of the hand in space. Relying on magnetic fields, the hand tracking system can work also in nonline-of-sight conditions. The gesture reconstruction is validated by comparing it with a commercial hand tracking system based on a depth camera. The system requires a small amount of electronics to be mounted on the hand. This would allow building a light and comfortable data glove that could be used for several purposes: human-machine interface, sign language recognition, diagnostics, and rehabilitation.

Development of a Low-Cost Wearable Data Glove for Capturing Finger Joint Angles

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.