title: Gait event detection in laboratory and real life settings: Accuracy of ankle and waist sensor based methods.
abstract: Wearable sensors technology based on inertial measurement units (IMUs) is leading the transition from laboratory-based gait analysis, to daily life gait monitoring. However, the validity of IMU-based methods for the detection of gait events has only been tested in laboratory settings, which may not reproduce real life walking patterns. The aim of this study was to evaluate the accuracy of two algorithms for the detection of gait events and temporal parameters during free-living walking, one based on two shank-worn inertial sensors, and the other based on one waist-worn sensor. The algorithms were applied to gait data of ten healthy subjects walking both indoor and outdoor, and completing protocols that entailed both straight supervised and free walking in an urban environment. The values obtained from the inertial sensors were compared to pressure insoles data. The shank-based method showed very accurate initial contact, stride time and step time estimation (<14ms error). Accuracy of final contact timings and stance time was lower (28-51ms error range). The error of temporal parameter variability estimates was in the range 0.09-0.89%. The waist method failed to detect about 1% of the total steps and performed worse than the shank method, but the temporal parameter estimation was still satisfactory. Both methods showed negligible differences in their accuracy when the different experimental conditions were compared, which suggests their applicability in the analysis of free-living gait.
authors: Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK; INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK. Electronic address: fastorm1@sheffield.ac.uk.
authors: Fabio A
authors: FA
authors: Storm
authors: Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK; INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK; MRC-Arthritis Research UK Centre for Integrated Research into Musculoskeletal Ageing (CIMA), The University of Sheffield, Sheffield, UK.
authors: Christopher J
authors: CJ
authors: Buckley
authors: Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK; INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK.
authors: Claudia
authors: C
authors: Mazzà
mesh_terms: D000328:Adult; D000465:Algorithms; D000842:Ankle; D000843:Ankle Joint; D001696:Biomechanical Phenomena; D005260:Female; D005684:Gait / Q000502:physiology*; D064368:Healthy Volunteers; D006801:Humans; D007753:Laboratories; D008297:Male; D060726:Torso; D016138:Walking / Q000502:physiology
keywords: Algorithm; Free walking; Gait events; Inertial sensor; Temporal parameters