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Classifying Changes in Amputee Gait following Physiotherapy Using Machine Learning and Continuous Inertial Sensor

Gabriel Ng1,2, Jan Andrysek1,2

  • 1Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 1A1, Canada.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary

A novel method using gyroscope data from a single wearable sensor accurately classifies gait changes in individuals with lower-limb amputations (LLA) after physical therapy. This technology aids in developing adaptable gait analysis systems for diverse mobility impairments.

Keywords:
gait classificationinertial sensorslower limb amputeesmachine learningrehabilitationtime-series analysis

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Wearable Sensors

Background:

  • Wearable sensors offer objective gait analysis but struggle with diverse populations like lower-limb amputees (LLA).
  • LLA present unique gait deviations and rehabilitation needs, challenging current gait analysis systems.
  • Personalized gait assessment is crucial for effective rehabilitation in LLA.

Purpose of the Study:

  • To develop and validate a novel method for person-specific classification of gait changes in LLA during rehabilitation.
  • To assess the efficacy of using continuous gyroscope data from a single inertial sensor for gait analysis.
  • To explore the adaptability of wearable gait analysis systems for individuals with mobility impairments.

Main Methods:

  • Collected continuous gyroscope data from a single thigh-mounted inertial sensor for five LLA participants before, during, and after gait training.
  • Utilized dynamic time warping (DTW) and Euclidean distance with a nearest neighbor classifier to analyze gyroscope data.
  • Classified pre- and post-training gait patterns based on the collected sensor data.

Main Results:

  • The developed models achieved high accuracy in classifying gait changes: 98.65% (Euclidean) and 98.98% (DTW) for pre-training, and 95.45% (Euclidean) and 94.18% (DTW) for post-training.
  • The system demonstrated effectiveness across participants whose gait significantly improved during the training session.
  • Preliminary evidence suggests continuous angular velocity data from a single gyroscope can assess amputee gait changes.

Conclusions:

  • Continuous angular velocity data from a single gyroscope shows potential for assessing gait modifications in individuals with lower-limb amputations.
  • This approach supports the development of adaptable wearable gait analysis and feedback systems for a wide range of mobility impairments.
  • Further research is warranted to refine and implement this technology in clinical and real-world settings.