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Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
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Simplified Markerless Stride Detection Pipeline (sMaSDP) for Surface EMG Segmentation.

Rafael Castro Aguiar1, Edward Jero Sam Jeeva Raj2, Samit Chakrabarty1

  • 1School of Biomedical Sciences, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, UK.

Sensors (Basel, Switzerland)
|May 13, 2023
PubMed
Summary

This study introduces a simplified method using wearable sensors to analyze gait and muscle activity during daily life activities. The approach accurately detects gait events, aiding in diagnosing mobility impairments outside clinical settings.

Keywords:
EMGIMUgait detection algorithmsmuscle activitysegmentation

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Science
  • Wearable Technology

Background:

  • Gait assessment is crucial for diagnosing mobility impairments but often occurs in artificial clinical settings.
  • Naturalistic gait analysis in Activities of Daily Life (ADL) offers more accurate insights.
  • Synchronous recording of Electromyography (EMG) and kinematics is needed for comprehensive gait analysis.

Purpose of the Study:

  • To introduce sMaSDP, a simplified markerless gait event detection pipeline for segmenting EMG signals using Inertial Measurement Unit (IMU) data.
  • To provide a tutorial for beginners on gait event detection and EMG segmentation in unconstrained gait studies.
  • To enable accurate gait analysis in naturalistic settings for improved diagnosis and physiotherapy.

Main Methods:

  • Developed a simplified, markerless gait event detection pipeline (sMaSDP) using IMU data for EMG signal segmentation.
  • Collected synchronized kinematic and EMG data from 10 healthy subjects across five walking modalities in an unconstrained environment.
  • Segmented and filtered data to create an algorithm for detecting heel-strike events with a single IMU and isolating gait cycle EMG activity.

Main Results:

  • Successfully detected heel-strike events and isolated EMG activity within gait cycles using the sMaSDP pipeline.
  • Validated the sMaSDP methodology on both healthy gait data and gait data from Parkinson's Disease (PD) patients.
  • Demonstrated the algorithm's applicability across different datasets and its potential for analyzing gait in various conditions.

Conclusions:

  • The sMaSDP protocol offers a simple, effective method for gait event detection and EMG segmentation in unconstrained ADL settings.
  • Adjustable algorithm parameters enhance detection accuracy for diverse gait characteristics.
  • The emphasis on wearable IMU and EMG technologies makes sMaSDP ideal for real-world gait studies and clinical applications.