A Novel Active Learning Framework for Cross-Subject Human Activity Recognition from Surface Electromyography

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 18; p. 5949
• Main Authors: Ding, Zhen, Hu, Tao, Li, Yanlong, Li, Longfei, Li, Qi, Jin, Pengyu, Yi, Chunzhi
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 13.09.2024; MDPI
• Subjects: Adaptation; Adult; Algorithms; Analysis; Classification; classifier discrepancy; Control systems; cross-subject issue; Datasets; Deep learning; Electromyography; Electromyography - methods; Human Activities; human activity recognition; Humans; Information management; Machine Learning; Male; Neural networks; Optimization; relation network; Sensors; Supervised Machine Learning; surface electromyography signals; Wearable Electronic Devices; wearable sensors; wearable sensors; classifier discrepancy; relation network; human activity recognition; cross-subject issue; surface electromyography signals
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24185949

Wearable sensor-based human activity recognition (HAR) methods hold considerable promise for upper-level control in exoskeleton systems. However, such methods tend to overlook the critical role of data quality and still encounter challenges in cross-subject adaptation. To address this, we propose an active learning framework that integrates the relation network architecture with data sampling techniques. Initially, target data are used to fine tune two auxiliary classifiers of the pre-trained model, thereby establishing subject-specific classification boundaries. Subsequently, we assess the significance of the target data based on classifier discrepancy and partition the data into sample and template sets. Finally, the sampled data and a category clustering algorithm are employed to tune model parameters and optimize template data distribution, respectively. This approach facilitates the adaptation of the model to the target subject, enhancing both accuracy and generalizability. To evaluate the effectiveness of the proposed adaptation framework, we conducted evaluation experiments on a public dataset and a self-constructed electromyography (EMG) dataset. Experimental results demonstrate that our method outperforms the compared methods across all three statistical metrics. Furthermore, ablation experiments highlight the necessity of data screening. Our work underscores the practical feasibility of implementing user-independent HAR methods in exoskeleton control systems.