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Related Experiment Video

Updated: Sep 18, 2025

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults
08:56

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Published on: November 7, 2014

14.0K

Comparability of Methods for Remotely Assessing Gait Quality.

Natasha Hassija1,2, Edward Hill3, Helen Dawes3,4

  • 1Center for Outcomes Research and Evaluation (CORE), Research Institute of McGill University Health Center (MUHC), Montreal, QC H4A 3S5, Canada.

Sensors (Basel, Switzerland)
|June 27, 2025
PubMed
Summary

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Discriminating Between Fallers and Non-Fallers Using Kinematic Data from the Heel2Toe™ Wearable Sensor.

Sensors (Basel, Switzerland)·2026

Remote gait analysis using wearable sensors and pose estimation shows promise for Parkinson's disease (PD) assessment. These digital tools can detect subtle gait changes at home, complementing traditional observation methods.

Area of Science:

  • Neurology
  • Biomedical Engineering
  • Digital Health

Background:

  • Remote gait analysis offers efficient, cost-effective, and personalized real-time assessments for individuals with Parkinson's disease (PD).
  • Traditional gait assessment methods can be limited in accessibility and real-time feedback capabilities.

Purpose of the Study:

  • To compare the gait quality metrics of three remote gait assessment methods in individuals with PD: observation, wearable sensors, and pose estimation.
  • To provide evidence on the comparability of these technologies for remote PD gait monitoring.

Main Methods:

  • A cross-sectional, multiple case series study involving 20 participants with PD remotely assessing gait.
  • Participants performed a modified Timed Up and Go (TUG) test using the Heel2ToeTM wearable sensor.
Keywords:
Parkinson’s diseasedigital technologygait analysisobservational analysispose estimationremote assessmentwearable sensor

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Related Experiment Videos

Last Updated: Sep 18, 2025

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Published on: November 7, 2014

14.0K
Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
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  • Videos were analyzed using a PD-specific observational checklist and the MediaPipe Pose Landmarker task estimation library.

    • Main Results:

    • Observational ratings agreed with the Heel2ToeTM wearable on detecting heel strike 64% of the time and push-off 28.5% of the time.
    • Significant differences in gait parameter ranks were found between methods, except for push-off when comparing MediaPipe pose estimation to observational checklist ratings (p = 0.498).

    Conclusions:

    • A combination of digital technologies, including wearable sensors and pose estimation, can detect subtle gait impairments in PD.
    • These advanced methods offer potential for more comprehensive and sensitive remote gait analysis compared to human observation alone.