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Evaluating generalization of arm movement identification using machine learning: From structured to semi-structured

Sahel Akbari1, Herwin L D Horemans2, Johannes B J Bussmann2

  • 1Dept. Rehabilitation Medicine, Erasmus MC University Medical Center, The Netherlands; Dept. Cognitive Robotics, Faculty of Mechanical Engineering, Delft University of Technology, The Netherlands.

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Summary

Machine learning models for arm movement identification show strong generalization from lab to home settings. This advance supports developing effective wearable technology for stroke rehabilitation at home.

Keywords:
Activities of daily lifeArm movement identificationHome-based rehabilitationInertial measurement unitsMachine learning

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Machine Learning in Healthcare

Background:

  • Home-based rehabilitation is crucial for stroke survivors' motor recovery and daily activities.
  • Wearable technology and machine learning offer potential for advanced home-based arm rehabilitation.
  • Current machine learning models often lack testing across diverse environments, limiting real-world application.

Purpose of the Study:

  • To evaluate the generalization ability of machine learning models for arm movement identification.
  • To compare model performance across structured (lab) and semi-structured (kitchen) environments.
  • To assess the impact of sensor configuration (multi-IMU vs. single wrist IMU) on generalization.

Main Methods:

  • Investigated two machine learning models: Random Forest and a hybrid deep learning model.
  • Trained models in a structured lab environment and tested in a semi-structured kitchen environment.
  • Compared performance using four arm-mounted IMUs versus a single wrist-mounted IMU.

Main Results:

  • Both models demonstrated good generalization from lab to kitchen environments.
  • The four-IMU configuration yielded higher accuracy than the single wrist IMU.
  • The Random Forest model showed a smaller accuracy decrease with the single wrist IMU compared to the hybrid model.

Conclusions:

  • Arm movement identification algorithms generalize well across different environments, even with minimal sensors.
  • These findings support the potential of wearable technology for practical home-based stroke rehabilitation.
  • Further development can leverage these algorithms for more accessible and effective remote rehabilitation solutions.