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Novel methodology for estimating Initial Contact events from accelerometers positioned at different body locations
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), Centre for Research on Embedded Systems (CERES).ORCID iD: 0000-0002-4143-2948
2018 (English)In: Gait & Posture, ISSN 0966-6362, E-ISSN 1879-2219, Vol. 59, p. 278-285Article in journal (Refereed) Published
Abstract [en]

Identifying Initial Contact events (ICE) is essential in gait analysis as they segment the walking pattern into gait cycles and facilitate the computation of other gait parameters. As such, numerous algorithms have been developed to identify ICE by placing the accelerometer at a specific body location. Simultaneously, many researchers have studied the effects of device positioning for participant or patient compliance, which is an important factor to consider especially for long-term studies in real-life settings. With the adoption of accelerometery for long-term gait analysis in daily living, current and future applications will require robust algorithms that can either autonomously adapt to changes in sensor positioning or can detect ICE from multiple sensors locations.

This study presents a novel methodology that is capable of estimating ICE from accelerometers placed at different body locations. The proposed methodology, called DK-TiFA, is based on utilizing domain knowledge about the fundamental spectral relationships present between the movement of different body parts during gait to drive the time-frequency analysis of the acceleration signal. In order to assess the performance, DK-TiFA is benchmarked on four large publicly available gait databases, consisting of a total of 613 subjects and 7 unique body locations, namely, ankle, thigh, center waist, side waist, chest, upper arm and wrist. The DK-TiFA methodology is demonstrated to achieve high accuracy and robustness for estimating ICE from data consisting of different accelerometer specifications, varying gait speeds and different environments. © 2017 Elsevier B.V.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2018. Vol. 59, p. 278-285
Keywords [en]
Gait event, Inertial sensor, sensor placement, wavelet transform, domain knowledge, gait database
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-34639DOI: 10.1016/j.gaitpost.2017.07.030Scopus ID: 2-s2.0-85026637369OAI: oai:DiVA.org:hh-34639DiVA, id: diva2:1128049
Funder
Knowledge Foundation
Note

Funding: The Knowledge Foundation, Sweden and Promobilia Foundation, Sweden

Available from: 2017-07-21 Created: 2017-07-21 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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