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Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0003-4086-9221
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-4143-2948
2017 (English)In: Gait & Posture, ISSN 0966-6362, E-ISSN 1879-2219, Vol. 51, 84-90 p.Article in journal (Refereed) Published
Abstract [en]

Numerous gait event detection (GED) algorithms have been developed using accelerometers as they allow the possibility of long-term gait analysis in everyday life. However, almost all such existing algorithms have been developed and assessed using data collected in controlled indoor experiments with pre-defined paths and walking speeds. On the contrary, human gait is quite dynamic in the real-world, often involving varying gait speeds, changing surfaces and varying surface inclinations. Though portable wearable systems can be used to conduct experiments directly in the real-world, there is a lack of publicly available gait datasets or studies evaluating the performance of existing GED algorithms in various real-world settings.

This paper presents a new gait database called MAREA (n=20 healthy subjects) that consists of walking and running in indoor and outdoor environments with accelerometers positioned on waist, wrist and both ankles. The study also evaluates the performance of six state-of-the-art accelerometer-based GED algorithms in different real-world scenarios, using the MAREA gait database. The results reveal that the performance of these algorithms is inconsistent and varies with changing environments and gait speeds. All algorithms demonstrated good performance for the scenario of steady walking in a controlled indoor environment with a combined median F1score of 0.98 for Heel-Strikes and 0.94 for Toe-Offs. However, they exhibited significantly decreased performance when evaluated in other lesser controlled scenarios such as walking and running in an outdoor street, with a combined median F1score of 0.82 for Heel-Strikes and 0.53 for Toe-Offs. Moreover, all GED algorithms displayed better performance for detecting Heel-Strikes as compared to Toe-Offs, when evaluated in different scenarios. © 2016 Elsevier B.V.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2017. Vol. 51, 84-90 p.
Keyword [en]
gait events, gait event detection, accelerometer, inertial sensor, gait database, gait dataset, Heel Strike, Toe Off
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-32110DOI: 10.1016/j.gaitpost.2016.09.023PubMedID: 27736735Scopus ID: 2-s2.0-84991511975OAI: oai:DiVA.org:hh-32110DiVA: diva2:999938
Funder
Knowledge Foundation
Note

This study was supported in part by the Knowledge Foundation, Sweden.

Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2016-12-09Bibliographically approved

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CiteExportLink to record
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