- The paper introduces a semi-supervised ensemble algorithm to adapt powered hand orthosis control and mitigate intrasession EMG concept drift.
- It combines multimodal sensors including EMG, joint angles, and fingertip pressure to enhance intent inferral with reduced training data.
- Experimental results with stroke subjects validate its superior accuracy and real-world potential in stroke rehabilitation.
Adaptive Semi-Supervised Intent Inferral for a Powered Hand Orthosis in Stroke Rehabilitation
This paper presents a paper on the adaptive control systems for a powered hand orthosis intended for stroke patients. The focus of this research is on mitigating the challenges of concept drift in electromyographic (EMG) signals, which are used as inputs for inferring user intent. Concept drift is particularly problematic in stroke rehabilitation due to abnormal muscle coactivation and interaction issues between the user’s hand and the orthotic device. The novelty in this research lies in the application of semi-supervised learning, a machine learning paradigm that incorporates unlabeled data, to improve intent recognition and reduce the need for extensive user training data.
Methodology and Contributions
The paper introduces a disagreement-based semi-supervised learning algorithm designed to address intrasession concept drift. The algorithm leverages multimodal ipsilateral sensing, combining EMG signals with inputs from motor position sensors, IMUs for finger joint angles, and fingertip pressure sensors. An ensemble of classifiers forms the core of the control system, with each using different feature subsets. The ensemble's outputs undergo aggregation and filtering to make final intent predictions.
Key contributions of the paper include:
- Development of a semi-supervised control algorithm that adapts to intrasession concept drift by using disagreement-based strategies to exploit the ensemble learning approach.
- Demonstration that the semi-supervised controls require less initial training data while maintaining or improving upon the prediction accuracy compared to traditional supervised methods.
- Validation through experimental results with five stroke subjects, showing improved accuracy over baseline methods without the need for extensive labeling effort.
Numerical Results and Experimental Validation
Experiments were conducted with chronic stroke survivors to determine the effectiveness of the proposed system. The semi-supervised approach (DSSM-partial) displayed superior classification accuracy compared to other methods, including supervised EMG with full and partial training datasets and supervised multimodal methods. The semi-supervised method achieved an average accuracy improvement, showing statistical significance when compared against baseline methodologies, thereby confirming its robustness to concept drift.
In practical terms, the paper’s findings were validated through functional tasks involving object manipulation. The real-time adaptation in tasks further supported the proposed framework’s feasibility, highlighting its potential for real-world application in stroke rehabilitation.
Implications and Future Developments
This research advances the control of assistive devices by reducing the dependency on extensive labeled datasets, which often pose a significant burden on users, particularly in clinical rehabilitation scenarios. The theoretical contribution lies in demonstrating the application of semi-supervised learning to a domain with complex and non-stationary signals like EMG.
The implications of this paper are significant for the development of more intuitive and adaptive rehabilitation devices. Potential future directions include extending this framework to manage intersession drift, which is a major challenge in ensuring consistency across different therapy sessions. Investigating the integration of this approach with advanced neural interfaces or exploring reinforcement learning could further enhance adaptability and usability in diverse therapeutic contexts.