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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
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0003-4086-9221
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-4143-2948
2017 (Engelska)Ingår i: Gait & Posture, ISSN 0966-6362, E-ISSN 1879-2219, Vol. 51, s. 84-90Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
Amsterdam: Elsevier, 2017. Vol. 51, s. 84-90
Nyckelord [en]
gait events, gait event detection, accelerometer, inertial sensor, gait database, gait dataset, Heel Strike, Toe Off
Nationell ämneskategori
Signalbehandling
Identifikatorer
URN: urn:nbn:se:hh:diva-32110DOI: 10.1016/j.gaitpost.2016.09.023ISI: 000390463000015PubMedID: 27736735Scopus ID: 2-s2.0-84991511975OAI: oai:DiVA.org:hh-32110DiVA, id: diva2:999938
Forskningsfinansiär
KK-stiftelsen
Anmärkning

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

Tillgänglig från: 2016-09-30 Skapad: 2016-09-30 Senast uppdaterad: 2018-03-26Bibliografiskt granskad
Ingår i avhandling
1. Gait Event Detection in the Real World
Öppna denna publikation i ny flik eller fönster >>Gait Event Detection in the Real World
2018 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Healthy gait requires a balance between various neuro-physiological systems and is considered an important indicator of a subject's physical and cognitive health status. As such, health-related applications would immensely benefit by performing long-term or continuous monitoring of subjects' gait in their natural environment and everyday lives. In contrast to stationary sensors such as motion capture systems and force plates, inertial sensors provide a good alternative for such gait analysis applications as they are miniature, cheap, mobile and can be easily integrated into wearable systems.

This thesis focuses on improving overall gait analysis using inertial sensors by providing a methodology for detecting gait events in real-world settings. Although the experimental protocols for such analysis have been restricted to only highly-controlled lab-like indoor settings; this thesis presents a new gait database that consists of data from gait activities carried out in both, indoor and outdoor environments. The thesis shows how domain knowledge about gait could be formulated and utilized to develop methods that are robust and can tackle real-world challenges. It also shows how the proposed approach can be generalized to estimate gait events from multiple body locations. Another aspect of this thesis is to demonstrate that the traditionally used temporal error metrics are not enough for presenting the overall performance of gait event detection methods. The thesis introduces how non-parametric tests can be used to complement them and provide a better overview.

The results of comparing the proposed methodology to state-of-the-art methods showed that the approach of incorporating domain knowledge into the time-frequency analysis of the signal was robust across different real-world scenarios and outperformed other methods, especially for the scenario involving variable gait speeds in outdoor settings. The methodology was also benchmarked on publicly available gait databases yielding good performance for estimating events from different body locations. To conclude, this thesis presents a road map for the development of gait analysis systems in real-world settings.

Ort, förlag, år, upplaga, sidor
Halmstad: Halmstad University Press, 2018. s. 73
Nyckelord
gait analysis, gait event detection, wearable sensors, accelerometers
Nationell ämneskategori
Signalbehandling Annan medicinteknik
Identifikatorer
urn:nbn:se:hh:diva-36525 (URN)978-91-87045-86-8 (ISBN)978-91-87045-87-5 (ISBN)
Disputation
2018-03-14, Wigforssalen, Visionen, Kristian IV:s väg 3, Halmstad, 10:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2018-03-26 Skapad: 2018-03-26 Senast uppdaterad: 2018-03-26Bibliografiskt granskad

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