Prosthetic Hand Control via PCA-Decoupled EMG Signals
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Solution Overview
Problem
Current prosthetic hand control systems struggle to accurately estimate and control continuous finger motions due to individual variations in muscle development and operating habits, with limited research on continuous motion estimation for smooth robot motion control.
Innovation Solution
A multi-dimensional surface electromyogram signal control method using a 24-channel array electromyography sensor and finger joint attitude sensors, combined with principal component analysis and a neural network, to decouple electromyography data and predict continuous finger motions, allowing for precise control of prosthetic hand fingers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If discrete gesture recognition methods are used, then pattern recognition is mature and reliable, but continuous motion estimation capability is insufficient and control smoothness is poor
Solution Approach 1:
The system transitions from static discrete gesture classification to dynamic continuous motion estimation. The neural network model processes electromyography signals to predict continuous finger joint angles in real-time, enabling smooth and natural prosthetic hand control that adapts to ongoing muscle activity patterns.
Solution Approach 2:
The invention changes the output parameters from discrete gesture categories to continuous joint angle values. By estimating continuous motion parameters rather than classifying discrete gestures, the system achieves both reliability through neural network processing and productivity through smooth continuous control capability.
2Device complexity
If traditional electromyography signal processing is used, then signal collection is simple, but prediction precision for continuous motion is insufficient
Solution Approach 1:
The system performs preliminary training to establish individualized mapping relationships between electromyography signals and finger joint angles. During this training phase, the neural network learns subject-specific muscle activation patterns, which significantly improves prediction precision when the prosthetic hand is actually used, while keeping the operational complexity low.
Solution Approach 2:
The system uses collected electromyography signals as feedback to continuously predict and adjust finger joint angles. The neural network processes real-time muscle activity signals to generate accurate motion predictions, creating a closed-loop control system that maintains high precision without requiring complex signal processing during operation.
3Adaptability or versatility
If individualized training is performed for each subject, then adaptation to personal muscle characteristics is achieved, but training time and system complexity increase
Solution Approach 1:
The system performs preliminary individualized training to establish subject-specific mapping models before actual use. This upfront investment in training time creates personalized neural network models that adapt to each user's muscle characteristics, thereby reducing the need for complex real-time adjustments and improving long-term system efficiency and accuracy.
Data Source
AI summary
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.