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People with mobility impairments are often recommended for gait assessmentstudies to diagnose their condition and to select appropriate physiotherapy toimprove their mobility. These studies are often conducted in clinical or labsettings, where subjects are assessed in a foreign environment, which mayinfluence their motivation, coordination and overall mobility. Alternatively,if the subject's gait could be assessed in their daily-lives, in unconstrainedsettings, a more naturalistic gait assessment could be performed. Kinematicanalysis of a gait pattern on its own may not be sufficient to characterise asubject's mobility. To better diagnose gait deficiencies, analysis of thepatient's muscle activity should be conducted as well. To do so, gait studiesshould collect, synchronously, Electromyography (EMG) and kinematic data. Thismethod introduces a simplified markerless gait event detection pipeline for thesegmentation of EMG signals, via synchronously recorded Inertial MeasurementUnit (IMU) data. In an unconstrained walking experiment, healthy subjects walkthrough a designed course with their kinematic and EMG data recorded. Thiscourse 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, inthe different walking modalities. This gait event detection algorithm can beadapted to different datasets, and was tested in both healthy and Parkinson'sDisease (PD) gait. Results demonstrate the extracted muscle activity levels ina healthy subject's level ground walking, and the extracted HS events of a PDpatient. Adjustments to algorithm parameters are possible (e.g., expectedvelocity, cadence) and can further increase the detection accuracy.

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