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Developing and validating an accelerometer-based algorithm with machine learning to classify physical activity after

Helene Honoré1,2, Rikke Gade3, Jørgen Feldbæk Nielsen1,2

  • 1Hammel Neurorehabilitation Centre & University Research Clinic (HNURC), Hammel, Denmark.

Brain Injury
|February 18, 2021
PubMed
Summary

Researchers developed an algorithm using accelerometers to classify physical activities in individuals with acquired brain injury (ABI). This technology accurately distinguishes between various movements, aiding in rehabilitation and daily monitoring.

Keywords:
Algorithms [G17.035]Brain Injuries [C10.228.140.199]Monitoring, Ambulatory [E01.370.520.500)Neurological Rehabilitation [E02.760.169.063.500.477]Validation Study [V03.950]

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Science
  • Wearable Technology

Background:

  • Acquired brain injury (ABI) often impairs physical activity and mobility.
  • Objective assessment of physical activity is crucial for rehabilitation and monitoring in ABI populations.
  • Current methods for activity monitoring in ABI may be limited in real-world applicability.

Purpose of the Study:

  • To develop and validate an accelerometer-based algorithm for classifying physical activities in individuals with ABI.
  • To assess the algorithm's performance in a controlled laboratory setting simulating home environments.
  • To establish the criterion validity of the machine learning algorithm against a gold standard reference.

Main Methods:

  • A development and validation study involving healthy participants and individuals with ABI.
  • Utilized accelerometers and thermal video cameras for activity measurement.
  • Developed and cross-validated a machine learning algorithm using a training sample.
  • Established criterion validity by classifying a protocol of transfers and ambulating activities.

Main Results:

  • The algorithm demonstrated good precision in classifying transfers and ambulating activities in individuals with ABI.
  • Achieved a weighted sensitivity of 89.3% and a weighted positive predictive value of 89.7%.
  • Successfully differentiated between lying and sitting activities.

Conclusions:

  • An accelerometer-based algorithm for classifying physical activities in ABI populations has been developed and validated.
  • The algorithm shows promising accuracy for distinguishing specific movements.
  • Further validation in real-world home settings with continuous monitoring is recommended.