An Algorithm for Accurate Marker-Based Gait Event Detection in Healthy and Pathological Populations During Complex Motor Tasks

on behalf of the Mobilise-D consortium

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There is growing interest in the quantification of gait as part of complex motor tasks. This requires gait events (GEs) to be detected under conditions different from straight walking. This study aimed to propose and validate a new marker-based GE detection method, which is also suitable for curvilinear walking and step negotiation. The method was first tested against existing algorithms using data from healthy young adults (YA, n = 20) and then assessed in data from 10 individuals from the following five cohorts: older adults, chronic obstructive pulmonary disease, multiple sclerosis, Parkinson’s disease, and proximal femur fracture. The propagation of the errors associated with GE detection on the calculation of stride length, duration, speed, and stance/swing durations was investigated. All participants performed a variety of motor tasks including curvilinear walking and step negotiation, while reference GEs were identified using a validated methodology exploiting pressure insole signals. Sensitivity, positive predictive values (PPV), F1-score, bias, precision, and accuracy were calculated. Absolute agreement [intraclass correlation coefficient ((Formula presented.))] between marker-based and pressure insole stride parameters was also tested. In the YA cohort, the proposed method outperformed the existing ones, with sensitivity, PPV, and F1 scores ≥ 99% for both GEs and conditions, with a virtually null bias (<10 ms). Overall, temporal inaccuracies minimally impacted stride duration, length, and speed (median absolute errors ≤1%). Similar algorithm performances were obtained for all the other five cohorts in GE detection and propagation to the stride parameters, where an excellent absolute agreement with the pressure insoles was also found ((Formula presented.)). In conclusion, the proposed method accurately detects GE from marker data under different walking conditions and for a variety of gait impairments.

Original languageEnglish
Article number868928
JournalFrontiers in Bioengineering and Biotechnology
StatePublished - 2 Jun 2022


FundersFunder number
IMI2 JU853981
United Kingdom Engineering and Physical Sciences Research CouncilEP/K03877X/1, EP/S032940/1
Wellcome Trust Clinical Research Facility
Horizon 2020 Framework Programme820820
European Federation of Pharmaceutical Industries and Associations
National Institute for Health and Care ResearchIS-BRC-1215–20017
Newcastle University
Newcastle upon Tyne Hospitals NHS Foundation Trust
Innovative Medicines Initiative
NIHR Newcastle Biomedical Research Centre
NIHR Leicester Clinical Research Facility


    • gait analysis
    • gait cycle
    • spatio-temporal gait parameters
    • stereophotogrammetry
    • stride duration
    • stride length
    • stride speed


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