• Corpus ID: 245634948

Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification

@article{Lehmler2021DeepTF,
  title={Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification},
  author={Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},
  journal={ArXiv},
  year={2021},
  volume={abs/2112.15019}
}
Accurate decoding of surface electromyography (sEMG) is pivotal for muscle-to-machine-interfaces (MMI) and their application for e.g. rehabilitation therapy. sEMG signals have high inter-subject variability, due to various factors, including skin thickness, body fat percentage, and electrode placement. Therefore, obtaining high generalization quality of a trained sEMG decoder is quite challenging. Usually, machine learning based sEMG decoders are either trained on subject-specific data, or at… 

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