Post-stroke hand gesture recognition via one-shot transfer learning using prototypical networks.

Journal: Journal of neuroengineering and rehabilitation
PMID: 38867287

Abstract

BACKGROUND: In-home rehabilitation systems are a promising, potential alternative to conventional therapy for stroke survivors. Unfortunately, physiological differences between participants and sensor displacement in wearable sensors pose a significant challenge to classifier performance, particularly for people with stroke who may encounter difficulties repeatedly performing trials. This makes it challenging to create reliable in-home rehabilitation systems that can accurately classify gestures.

Authors

  • Hussein Sarwat
    Mechatronics Engineering Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt.
  • Amr Alkhashab
    Robot Offline Programming, Visual Components, Vänrikinkuja, Espoo, 02600, Finland.
  • Xinyu Song
    Beijing Institute of Radiation Medicine, Beijing, 100850, China.
  • Shuo Jiang
    The State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
  • Jie Jia
    Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Peter B Shull
    The State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

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