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1.发明授权公开(公告)号：US12269161B2
公开(公告)日：2025-04-08
申请号：US17628753
申请日：2020-06-03
Applicant: Southeast University
Inventor： Aiguo Song , Xuhui Hu , Zhikai Wei , Huijun Li , Baoguo Xu , Hong Zeng
Abstract: Provided are a multi-degree-of-freedom myoelectric artificial hand control system and a method for using same. The system comprises a robotic hand, a robotic wrist (2), a stump receiving cavity (1) and a data processor (3), wherein the robotic hand and the stump receiving cavity (1) are respectively mounted on two ends of the robotic wrist (2); a multi-channel myoelectric array electrode oversleeve, a control unit circuit board, and a battery are connected in the stump receiving cavity (1); and the other end of the control unit circuit board is connected to the robotic hand and the robotic wrist (2). The method for using the system comprises the following steps: (S1) a user wearing a multi-channel myoelectric array electrode oversleeve, and connecting a battery and a control unit circuit board; (S2) the user completing a gesture, collecting a surface electromyography signal and then uploading same to a data processor (3); (S3) the data processor (3) receiving the surface electromyography signal and inputting same into a neural network algorithm to generate a gesture prediction model; and (S4) the user controlling the multi-degree-of-freedom movement of the robotic wrist (2) and the robotic hand. By means of the system, continuous gestures and the gesture strength thereof can be identified, and multi-degree-of-freedom gestures can be made.
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公开(公告)号：US10959863B2
公开(公告)日：2021-03-30
申请号：US16475680
申请日：2018-05-23
Applicant: SOUTHEAST UNIVERSITY
Inventor： Aiguo Song , Xuhui Hu , Hong Zeng , Baoguo Xu , Huijun Li
Abstract: The present invention discloses a multi-dimensional surface electromyogram signal prosthetic hand control method based on principal component analysis. The method comprises the following steps. Wear an armlet provided with a 24-channel array electromyography sensor to a front arm of a subject, and respectively wear five finger joint attitude sensors at a distal phalanx of a thumb and at middle phalanxes of remaining fingers of the subject. Perform independent bending and stretching training on the five fingers of the subject, and meanwhile, collect data of an array electromyography sensor and data of the finger joint attitude sensors. Decouple the data of the array electromyography sensor by principal component analysis to form a finger motion training set. Perform data fitting on the finger motion training set by a neural network method, and construct a finger continuous motion prediction model. Predict a current bending angle of the finger through the finger continuous motion model.
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