Comparing subject-to-subject transfer learning methods in surface electromyogram-based motion recognition with shallow and deep classifiers

Medicine & Life Sciences

• Machine Learning 95%
• Electromyography 85%
• Psychology Recognition 74%
• Psychology Transfer 56%
• Support Vector Machine 29%
• Forearm 11%
• Information Storage and Retrieval 10%
• Volunteers 10%
• Datasets 9%

Engineering & Materials Science

• Transfer learning 100%
• Classifiers 59%
• Tuning 32%
• Neural networks 25%
• Time measurement 23%
• Interfaces (computer) 19%
• Support vector machines 16%
• Deep neural networks 10%
• Network architecture 9%