  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.
