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Gait Event Detection in the Real World
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
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
Keyword [en]
gait analysis, gait event detection, wearable sensors, accelerometers
National Category
Signal Processing Other Medical Engineering
Identifiers
URN: urn:nbn:se:hh:diva-36525ISBN: 978-91-87045-86-8 (print)ISBN: 978-91-87045-87-5 (electronic)OAI: oai:DiVA.org:hh-36525DiVA, id: diva2:1193297
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
List of papers
1. Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis
Open this publication in new window or tab >>Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis
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
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
Keyword
accelerometer, gait analysis, inertial sensors, morlet, principles of gait, stride parameters, wavelet transform
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-30468 (URN)10.1109/TNSRE.2016.2536278 (DOI)000390559600010 ()26955043 (PubMedID)2-s2.0-85006253692 (Scopus ID)
Available from: 2016-03-04 Created: 2016-03-04 Last updated: 2018-03-26Bibliographically approved
2. Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database
Open this publication in new window or tab >>Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database
2017 (English)In: Gait & Posture, ISSN 0966-6362, E-ISSN 1879-2219, Vol. 51, p. 84-90Article 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
Keyword
gait events, gait event detection, accelerometer, inertial sensor, gait database, gait dataset, Heel Strike, Toe Off
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-32110 (URN)10.1016/j.gaitpost.2016.09.023 (DOI)000390463000015 ()27736735 (PubMedID)2-s2.0-84991511975 (Scopus ID)
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: 2018-03-26Bibliographically approved
3. Novel methodology for estimating Initial Contact events from accelerometers positioned at different body locations
Open this publication in new window or tab >>Novel methodology for estimating Initial Contact events from accelerometers positioned at different body locations
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
Keyword
Gait event, Inertial sensor, sensor placement, wavelet transform, domain knowledge, gait database
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-34639 (URN)10.1016/j.gaitpost.2017.07.030 (DOI)2-s2.0-85026637369 (Scopus ID)
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

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Khandelwal, Siddhartha

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