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Comparative Learning for Cross-Subject Finger Movement Recognition in Three Arm Postures via Data Glove
Summary
This study introduces CLAPISA, a new AI framework for recognizing hand movements in physical therapy. It achieves high accuracy across different users, improving remote rehabilitation effectiveness.
Area of Science:
- Rehabilitation Engineering
- Machine Learning
- Human-Computer Interaction
Background:
- Effective home-based rehabilitation requires reliable recognition of therapeutic hand and finger movements.
- Patient variability (hand size, flexibility, speed) hinders data-glove model generalization.
- Current models struggle with inter-subject differences, limiting unsupervised therapy.
Purpose of the Study:
- To develop a subject-invariant framework for cross-subject gesture recognition in rehabilitation.
- To improve the accuracy and reliability of hand movement recognition for remote physical therapy.
- To address inter-subject variability in hand gesture data.
Main Methods:
- A contrastive-learning framework (CLAPISA) using a Siamese network within a CNN-LSTM pipeline.
- Training with a 1:2 positive-to-negative pairing strategy and an optimized margin of 1.0.
- Utilized a bending-sensor dataset from twenty young adults for evaluation.
Main Results:
- CLAPISA achieved an average accuracy of 96.71% using leave-one-subject-out cross-validation.
- Outperformed five baseline models in cross-subject gesture recognition.
- Reduced errors for challenging subjects by up to 12.3%.
Conclusions:
- CLAPISA demonstrates effective subject-invariant hand gesture recognition for rehabilitation.
- The framework shows promise for application in diverse populations, including the elderly and neurologically impaired.
- Further research will focus on validating CLAPISA with these specific cohorts.
