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Motor function assessment using wearable inertial sensors.

Avinash Parnandi1, Eric Wade, Maja Mataric

  • 1Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089, USA. parnandi@usc.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 25, 2010
PubMed
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This study introduces a wearable sensor method to automatically score the Wolf Motor Function Test (WMFT) for stroke survivors. Using an inertial measurement unit (IMU), it assesses motor function recovery more efficiently.

Area of Science:

  • Rehabilitation Medicine
  • Biomedical Engineering
  • Data Science

Background:

  • Stroke recovery requires accurate motor function assessment.
  • The Wolf Motor Function Test (WMFT) is a standard clinical tool for evaluating post-stroke motor abilities.
  • Manual WMFT scoring can be time-consuming and subjective.

Purpose of the Study:

  • To develop an automated system for scoring the WMFT using wearable sensor data.
  • To enhance the objectivity and efficiency of motor function assessment in post-stroke individuals.
  • To leverage inertial measurement unit (IMU) technology for functional ability (FA) scoring.

Main Methods:

  • Utilized a single on-body inertial measurement unit (IMU) for data acquisition.
  • Developed signal processing techniques to extract relevant features from IMU data.

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  • Applied supervised machine learning algorithms to classify and estimate WMFT FA scores for 15 tasks.

    • Main Results:

    • Successfully estimated WMFT FA scores using IMU data.
    • Demonstrated the feasibility of automated motor function assessment via wearable sensors.
    • The approach treats scoring as a classification problem in a multidimensional feature space.

    Conclusions:

    • Wearable IMU sensors offer a promising avenue for objective and automated WMFT scoring.
    • This technology can improve the efficiency and consistency of motor function assessment in stroke rehabilitation.
    • The proposed signal processing and machine learning methods provide a robust framework for sensor-based functional ability evaluation.