Multi-sensor fusion prosthetic hand grasping force feedback control method

Abstract

The invention provides a multi-sensor fusion prosthetic hand grasping force feedback control method. The method comprises a multi-sensor fusion and feedback method, a grasping force prediction method and a PD-based CMAC manipulator grasping control method, wherein the multi-sensor fusion and feedback method comprises the following steps: acquiring surface electromyogram signals of muscles, acquiring temperature, contact force and sliding state of an object grabbed by a prosthetic hand, acquiring joint acceleration and angular velocity corresponding to a grasp action, and acquiring actual grasping strength of the grasp action of the prosthetic hand; the grasping force prediction method is used for predicting the grasping force of the current grasping action; and according to the multi-sensor fusion and feedback method, data is fused to generate prosthetic end sensing information of the current grasping action. A user can adjust the actual grasping force and grasping action in time, an object is accurately and stably grasped, besides, the real-time temperature and contact force of the grasped object are detected in the grasping process, and touch information is fed back to the user through a vibration and display module.

Description

technical field [0001] The invention relates to the technical field of prosthetic limbs, in particular to a multi-sensor fusion hand grasping force feedback control method for prosthetic limbs. Background technique [0002] At present, the prosthetic hand technology is mainly to realize the specific pattern recognition of the hand, which only has opening and closing control, lacks force control, and cannot stably grasp objects with different rigidities and different weights. There is a correlation between the surface electromyographic signal (sEMG) and muscle contraction force, and the sEMG of the relevant muscles can be used to characterize the change of the grip force of the prosthetic hand, which can effectively reflect the user's grip intention. [0003] Only using EMG signals to predict the grasping force of the prosthetic hand, due to the lack of perception of the environment, it is impossible to perceive the stable state of the grasped object during the grasping proce...

AI Technical Summary

Problems solved by technology

[0003] Only use EMG signals to predict the grasping force of the prosthetic hand. Due to the lack of perception of the environment, it is impossible to perceive the stable state of the grasped object during the grasping process, and it is difficult to adaptively adjust the grasping force to ensure the stable grasp of the object.

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