Simplified markerless stride detection pipeline (sMaSDP) for surface EMG segmentation

TL;DR

This paper presents a simplified, markerless pipeline for detecting gait events and segmenting EMG signals using a single IMU, enabling naturalistic gait analysis in unconstrained environments for both healthy and Parkinson's Disease subjects.

Contribution

The study introduces a novel, adaptable algorithm for markerless gait event detection and EMG segmentation using only one IMU, suitable for real-world gait assessments.

Findings

Successfully detected heel-strike events in healthy and PD subjects.

Effectively segmented EMG activity across different walking modalities.

Algorithm parameters can be tuned for improved accuracy.

Abstract

People with mobility impairments are often recommended for gait assessment studies to diagnose their condition and to select appropriate physiotherapy to improve their mobility. These studies are often conducted in clinical or lab settings, where subjects are assessed in a foreign environment, which may influence their motivation, coordination and overall mobility. Alternatively, if the subject's gait could be assessed in their daily-lives, in unconstrained settings, a more naturalistic gait assessment could be performed. Kinematic analysis of a gait pattern on its own may not be sufficient to characterise a subject's mobility. To better diagnose gait deficiencies, analysis of the patient's muscle activity should be conducted as well. To do so, gait studies should collect, synchronously, Electromyography (EMG) and kinematic data. This method introduces a simplified markerless gait event detection pipeline for the segmentation of EMG signals, via synchronously recorded Inertial Measurement Unit (IMU) data. In an unconstrained walking experiment, healthy subjects walk through a designed course with their kinematic and EMG data recorded. This course comprises 5 different walking modalities (level walking, ramp up/down, staircase up/down), mimicking everyday walking. Through timepoint matching, segmentation and filtering, we generate an algorithm that detects heel-strike (HS) events using a single IMU, and isolates EMG activity of gait cycles, in the different walking modalities. This gait event detection algorithm can be adapted to different datasets, and was tested in both healthy and Parkinson's Disease (PD) gait. Results demonstrate the extracted muscle activity levels in a healthy subject's level ground walking, and the extracted HS events of a PD patient. Adjustments to algorithm parameters are possible (e.g., expected velocity, cadence) and can further increase the detection accuracy.