Multi-dimensional surface electromyogram signal prosthetic hand control method based on principal component analysis
US10959863B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 23, 2018 |
| Grant date | Mar 30, 2021 |
| Priority date | — |
| Expiry date | Oct 21, 2038 |
Classification
- Technology area (CPC A)Human Necessities
- CPC primaryA61F2002/704
- WIPO fieldMedical technology
- WIPO sectorInstruments
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.