Abstract
The use of wearable sensors allows continuous recordings of physical activity from participants in free-living or at-home clinical studies. The large amount of data collected demands automatic analysis pipelines to extract gait parameters that can be used as clinical endpoints. We introduce a deep learning-based automatic pipeline for wearables that processes tri-axial accelerometry data and extracts gait events—bout segmentation, initial contact (IC), and final contact (FC)—from a single sensor located at either the lower back (near L5), shin or wrist. The gait events detected are posteriorly used for gait parameter estimation, such as step time, length, and symmetry. We report results from a leave-one-subject-out (LOSO) validation on a pilot study dataset of five participants clinically diagnosed with Parkinson’s disease (PD) and six healthy controls (HC). Participants wore sensors at three body locations and walked on a pressure-sensing walkway to obtain reference gait data. Mean absolute errors (MAE) for the IC events ranged from 22.82 to 33.09 milliseconds (msecs) for the lower back sensor while for the shin and wrist sensors, MAE ranges were 28.56–64.66 and 40.19–72.50 msecs, respectively. For the FC-event detection, MAE ranges were 29.06–48.42, 40.19–72.70 and 36.06–60.18 msecs for the lumbar, wrist and shin sensors, respectively. Intraclass correlation coefficients, ICC(2,k), between the estimated parameters and the reference data resulted in good-to-excellent agreement (ICC ≥ 0.84) for the lumbar and shin sensors, excluding the double support time (ICC = 0.37 lumbar and 0.38 shin) and swing time (ICC = 0.55 lumbar and 0.59 shin). The wrist sensor also showed good agreements, but the ICCs were lower overall than for the other two sensors. Our proposed analysis pipeline has the potential to extract up to 100 gait-related parameters, and we expect our contribution will further support developments in the fields of wearable sensors, digital health, and remote monitoring in clinical trials.
Highlights
IntroductionThe analysis of gait is highly informative in the clinical evaluation of patients with diseases involving movement abnormalities
Parkinson’s disease (PD) participants had an average of 5.9 years since clinical diagnosis, and only the most recently diagnosed participant (1.5 years since diagnosis) was not taking dopaminergic medication
We presented an automatic gait analysis pipeline for wearable sensors that use tri-axial accelerometry and extracts gait parameters from a single sensor attached to either the lower back, wrist, or shin
Summary
The analysis of gait is highly informative in the clinical evaluation of patients with diseases involving movement abnormalities. Instrumented and objective methods to study gait abnormalities could help to improve diagnosis, stratify or enrich study populations, track disease progression, and assess the efficacy of new treatments. The gold standard for instrumented gait analysis is to use pressure-sensing platforms or walkways, which consist of an array of sensors that can capture individual foot-strikes while a participant performs a series of directed tasks. Pressure-sensing walkways can capture the initial contact (IC)
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