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Stride segmentation during free walk movements using multi-dimensional subsequence dynamic time warping on inertial

Jens Barth1,2,3, Cäcilia Oberndorfer4,5, Cristian Pasluosta6,7

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This study introduces a new algorithm for automatically segmenting individual strides from continuous gait data using inertial sensors. The method accurately isolates strides, improving gait analysis for health monitoring.

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Area of Science:

  • Biomedical Engineering
  • Movement Science
  • Gerontology

Background:

  • Gait pattern analysis is vital for assessing individual health.
  • Automatic segmentation of single strides from continuous movement is essential for sensor-based gait analysis.
  • Existing methods may lack accuracy in diverse walking conditions.

Purpose of the Study:

  • To develop and validate an algorithm for automatic single stride segmentation from inertial sensor data.
  • To enhance the accuracy of gait analysis, particularly for elderly individuals and patients with mobility impairments.
  • To establish a robust method for stride segmentation applicable to both standardized tests and daily activities.

Main Methods:

  • Development of a time-invariant template matching algorithm.
  • Utilizing shoe-mounted gyroscopes and accelerometers to collect gait data.
  • Application of a multi-dimensional subsequence Dynamic Time Warping (msDTW) approach for pattern matching.
  • Validation against manually labeled strides from 40m walk tests and free walk sequences.

Main Results:

  • Achieved high F-measure scores: 98% for 40m walk tests and 97% for free walk tests across three groups (elderly controls, Parkinson's disease patients, geriatric patients).
  • Demonstrated significant improvement (up to 15% F-measure) compared to conventional peak detection methods.
  • Confirmed the robustness of the msDTW approach for segmenting strides in various walking scenarios.

Conclusions:

  • The developed msDTW-based algorithm effectively and accurately segments individual strides from inertial sensor data.
  • This method offers a robust platform for precise gait analysis, outperforming traditional techniques.
  • The approach holds potential for individualized stride segmentation during daily living activities, aiding in health monitoring and rehabilitation.