A large-area grid-type epidermal electronics system for closed-loop human-computer interaction

Abstract

The invention belongs to the field of epidermal electronics, and discloses a large-area grid type epidermal electronic system for closed-loop man-machine interaction. The large-area grid type epidermal electronic system comprises epidermal myoelectricity electrode arrays, a joint rotation angle sensor array, an electric touch sensation sensor array, connecting structures and a circuit compensation structure, wherein the epidermal myoelectricity electrode arrays are used for collecting myoelectricity signals of the internal muscles and the external muscles of all fingers and the muscles of a wrist; the joint rotation angle sensor array is used for collecting the rotation angles of finger joints and the wrist; the electric touch sensation sensor array is used for stimulating fingertips through an electric current and a voltage; the circuit compensation structure is used for eliminating excess myoelectricity signals introduced by each epidermal myoelectricity electrode connecting structure, so that the accuracy of obtained signals of man-machine interaction equipment is guaranteed. Each sensor of the system is very light, thin and small, has stretchability and conforms with the mechanical property of skin, and at the same time, the problem that heat dissipation and perspiration can not normally proceed due to large-area covering is solved.

Description

technical field [0001] The invention belongs to the field of skin electronics, and more specifically relates to a large-area grid type skin electronics system for closed-loop human-computer interaction. Background technique [0002] Human-computer interaction systems have broad application prospects in deep sea navigation, deep space exploration, medical surgery, and industrial production. Human gestures are rich and diverse, which can realize the flexible grasping of many objects. Myoelectric signals were first used in the field of control in 1945, and then many researchers invested in it, expecting to realize human-computer interaction through human gestures. However, there are a large number of muscles that control gestures, with different shapes and sizes, and superimposed on each other. At the same time, weak myoelectric signals spread in the form of diffusion in the arm, which poses a huge challenge to how to accurately and real-time extract gestures. [0003] The cu...

AI Technical Summary

Problems solved by technology

However, there are a large number of muscles that control gestures, with different shapes and sizes, and superimposed on each other. At the same time, weak myoelectric signals spread in a diffuse manner in the arm, which poses a huge challenge to how to accurately and real-time extract gestures.

[0003] The current human-computer interaction system based on electromyographic signals has the following disadvantages: 1) only collects the electromyographic signals at the arm, and ignores the electromyographic signals at the palm (human forearm surface electromyographic signal acquisition and pattern recognition system, CN102426651B); Electrodes use conductive gel electrodes or other hard electrodes, which do not match the mechanical properties of the skin, have limited coverage, low spatial resolution, are prone to crosstalk, and are not convenient to carry (a wearable human-computer interaction gesture recognition Control device, CN106055114A); 3) adopt open-loop control, it is difficult to realize accurate grasping (myoelectric signal acquisition system, CN204683598U); 4) adopt pattern recognition control, algorithm is complicated, the gesture of recognition is limited (myoelectric signal pattern recognition method, CN105139038A; and a myoelectric signal gesture recognition method based on deep learning and feature images, CN105654037A)

Images