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Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
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A Multi-Sensor Matched Filter Approach to Robust Segmentation of Assisted Gait.

Satinder Gill1, Nitin Seth2, Erik Scheme3,4

  • 1Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada. satinder.gill@unb.ca.

Sensors (Basel, Switzerland)
|September 12, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new multi-sensor algorithm for accurately segmenting gait events from data collected by assistive devices like canes. This method improves the analysis of mobility and stability for individuals with walking impairments.

Keywords:
assistive devicecanegait analysisinertial measurement unit (IMU)loading informationmulti-sensorstride segmentation

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Gait Analysis

Background:

  • Assistive devices (ADs) like canes are crucial for individuals with mobility impairments.
  • Instrumenting ADs offers non-invasive monitoring of gait and reliance on the device.
  • Accurate sensor data processing and gait segmentation are essential for analyzing AD usage.

Purpose of the Study:

  • To develop and validate a highly accurate, multi-sensor-based gait segmentation algorithm.
  • To create a robust algorithm capable of handling diverse walking conditions and terrains.
  • To improve the extraction of relevant gait information from sensor data collected by canes.

Main Methods:

  • A novel multi-sensor matched filter (MSMF) algorithm was developed.
  • The algorithm combines matched filtering based on loading information with angular rate reversal and peak detection.
  • The MSMF algorithm was tested using a hybrid sensorized cane on various terrains with 30 healthy participants.

Main Results:

  • The proposed MSMF algorithm demonstrated high accuracy and reliability in segmenting gait events.
  • Performance was compared against variations of the gyroscope peak detection (GPD) algorithm.
  • The algorithm proved robust across different walking conditions and terrains.

Conclusions:

  • The developed multi-sensor gait segmentation algorithm offers a significant advancement for analyzing mobility data.
  • This technology can enhance the monitoring of individuals using assistive devices.
  • Accurate gait event segmentation is a critical step towards better understanding and supporting mobility impairments.