EMG Intention Estimation for Robot Hand Finger Control
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Solution Overview
Problem
Current intention estimation methods based on supervised learning face challenges in accurately recognizing separate finger motions and estimating motion intentions, especially for individuals with amputations, requiring additional hardware like load cells or force sensors, which are incompatible and degrade the usability and portability of robot hand systems.
Innovation Solution
A robot hand system employing unsupervised learning through kernel principal component analysis (kPCA) with kernel functions applied to biometric signals from EMG sensors, enabling accurate estimation of motion intentions without the need for additional hardware, allowing for simultaneous and proportional control of robot fingers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning with classification scheme is used for intention estimation, then the system can recognize previously specified classes, but it fails to accurately recognize separate finger motions and estimate motion intentions for hand amputees
Solution Approach 1:
The patent inverts the conventional supervised learning approach by using unsupervised learning to discover motion patterns without pre-defined class labels. Instead of requiring the system to recognize previously specified classes, the algorithm automatically clusters EMG signal patterns into distinct motion groups, enabling accurate recognition of separate finger motions and adaptation to hand amputees without retraining with labeled data.
Solution Approach 2:
The patent changes the fundamental parameter of the learning approach from supervised (with labels) to unsupervised (without labels). This parameter change allows the system to discover intrinsic motion patterns in EMG signals through clustering algorithms, improving both intention estimation accuracy and adaptability to different user conditions including hand amputees.
2Measurement precision
If additional hardware such as load cell or force sensor is added to improve motion recognition accuracy, then the estimation accuracy improves, but compatibility and usability for hand amputees deteriorates
Solution Approach 1:
The patent extracts the motion recognition capability from the EMG signal processing itself, eliminating the need for additional force sensing hardware. By applying unsupervised learning algorithms directly to the EMG signals from standard sensors, the system achieves accurate motion recognition without requiring load cells or force sensors that hand amputees cannot operate.
Solution Approach 2:
The patent replaces the mechanical force sensing system with a signal processing system based on unsupervised learning. Instead of using mechanical load cells or force sensors that require physical interaction, the system substitutes a computational approach that analyzes EMG signal patterns to infer motion intentions, making the system usable for hand amputees.
3Measurement precision
If complex algorithms are executed for motion intention estimation, then the estimation accuracy improves, but the system requires online analysis which degrades usability and portability
Solution Approach 1:
The patent performs preliminary action by pre-computing the clustering model during an offline training phase using recorded EMG data. The unsupervised learning algorithm discovers motion patterns and creates a clustering model in advance, which can then be applied in real-time with minimal computational overhead. This preliminary processing improves estimation accuracy while reducing the complexity of online analysis requirements.
Data Source
AI summary
This patent proposal document provides a complete robot hand control scheme using myoelectric intention estimation of the human being using the kernel Principal Component Analysis Algorithm (kPCA). The robot hand system includes a biometric EMG sensor system, a robot hand including with multiple fingers, a controller connected with the biometric EMG sensor system, and a robot hand. The controller acquires the biometric EMG signal by means of a biometric sensor system, estimates myoelectric motion intention by applying the kernel principal component analysis (kPCA) algorithm using a kernel function, and delivers a control command corresponding to the estimated motion intention of the user to the robot hand.