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Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis
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
2016 (English)In: IEEE transactions on neural systems and rehabilitation engineering, ISSN 1534-4320, E-ISSN 1558-0210, Vol. 24, no 12, p. 1363-1372Article in journal (Refereed) Published
Resource type
Text
Abstract [en]

Detecting gait events is the key to many gait analysis applications that would benefit from continuous monitoring or long-term analysis. Most gait event detection algorithms using wearable sensors that offer a potential for use in daily living have been developed from data collected in controlled indoor experiments. However, for real-word applications, it is essential that the analysis is carried out in humans’ natural environment; that involves different gait speeds, changing walking terrains, varying surface inclinations and regular turns among other factors. Existing domain knowledge in the form of principles or underlying fundamental gait relationships can be utilized to drive and support the data analysis in order to develop robust algorithms that can tackle real-world challenges in gait analysis. This paper presents a novel approach that exhibits how domain knowledge about human gait can be incorporated into time-frequency analysis to detect gait events from longterm accelerometer signals. The accuracy and robustness of the proposed algorithm are validated by experiments done in indoor and outdoor environments with approximately 93,600 gait events in total. The proposed algorithm exhibits consistently high performance scores across all datasets in both, indoor and outdoor environments. © Copyright 2016 IEEE

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Press, 2016. Vol. 24, no 12, p. 1363-1372
Keywords [en]
accelerometer, gait analysis, inertial sensors, morlet, principles of gait, stride parameters, wavelet transform
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-30468DOI: 10.1109/TNSRE.2016.2536278ISI: 000390559600010PubMedID: 26955043Scopus ID: 2-s2.0-85006253692OAI: oai:DiVA.org:hh-30468DiVA, id: diva2:909015
Available from: 2016-03-04 Created: 2016-03-04 Last updated: 2018-03-26Bibliographically approved
In thesis
1. Gait Event Detection in the Real World
Open this publication in new window or tab >>Gait Event Detection in the Real World
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2018. p. 73
Keywords
gait analysis, gait event detection, wearable sensors, accelerometers
National Category
Signal Processing Other Medical Engineering
Identifiers
urn:nbn:se:hh:diva-36525 (URN)978-91-87045-86-8 (ISBN)978-91-87045-87-5 (ISBN)
Public defence
2018-03-14, Wigforssalen, Visionen, Kristian IV:s väg 3, Halmstad, 10:00 (English)
Opponent
Supervisors
Available from: 2018-03-26 Created: 2018-03-26 Last updated: 2018-03-26Bibliographically approved

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Khandelwal, SiddharthaWickström, Nicholas

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