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A Symbol-Based Approach to Gait Analysis From Acceleration Signals: Identification and Detection of Gait Events and a New Measure of Gait Symmetry
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).ORCID iD: 0000-0002-3495-2961
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).ORCID iD: 0000-0002-4143-2948
2010 (English)In: IEEE transactions on information technology in biomedicine, ISSN 1089-7771, E-ISSN 1558-0032, Vol. 14, no 5, p. 1180-1187Article in journal (Refereed) Published
Abstract [en]

Gait analysis can convey important information about one’s physical and cognitive condition. Wearable inertial sensor systems can be used to continuously and unobtrusively assess gait during everyday activities in uncontrolled environments. An important step in the development of such systems is the processing and  analysis of the sensor data. This paper presents a symbol-based method used to detect the phases of gait and convey important dynamic information from accelerometer signals. The addition of expert knowledge substitutes the need for supervised learning techniques, rendering the system easy to interpret and easy to improve incrementally. The proposed method is compared to an approach based on peak-detection. A new symbol-based symmetry index is created and compared to a traditional temporal symmetry index and a symmetry measure based on cross-correlation. The symbol-based symmetry index exemplifies how the proposed method can extract more information from the acceleration signal than previous approaches

Place, publisher, year, edition, pages
New York: IEEE , 2010. Vol. 14, no 5, p. 1180-1187
Keywords [en]
gait analysis, accelerometers, motion language
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hh:diva-5439DOI: 10.1109/TITB.2010.2047402ISI: 000282474800006PubMedID: 20371410Scopus ID: 2-s2.0-77956380313OAI: oai:DiVA.org:hh-5439DiVA, id: diva2:345706
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Note
Copyright © 2010 IEEE. Reprinted from the IEEE IEEE transactions on information technology in biomedicine. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Halmstads's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.Available from: 2010-09-08 Created: 2010-08-26 Last updated: 2017-12-12Bibliographically approved
In thesis
1. A Symbolic Approach to Human Motion Analysis Using Inertial Sensors: Framework and Gait Analysis Study
Open this publication in new window or tab >>A Symbolic Approach to Human Motion Analysis Using Inertial Sensors: Framework and Gait Analysis Study
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Motion analysis deals with determining what and how activities are being performed by a subject, through the use of sensors. The process of answering the what question is commonly known as classification, and answering the how question is here referred to as characterization. Frequently, combinations of inertial sensor such as accelerometers and gyroscopes are used for motion analysis. These sensors are cheap, small, and can easily be incorporated into wearable systems.

The overall goal of this thesis was to improve the processing of inertial sensor data for the characterization of movements. This thesis presents a framework for the development of motion analysis systems that targets movement characterization, and describes an implementation of the framework for gait analysis. One substantial aspect of the framework is symbolization, which transforms the sensor data into strings of symbols. Another aspect of the framework is the inclusion of human expert knowledge, which facilitates the connection between data and human concepts, and clarifies the analysis process to a human expert.

The proposed implementation was compared to state of practice gait analysis systems, and evaluated in a clinical environment. Results showed that expert knowledge can be successfully used to parse symbolic data and identify the different phases of gait. In addition, the symbolic representation enabled the creation of new gait symmetry and gait normality indices. The proposed symmetry index was superior to many others in detecting movement asymmetry in early-to-mid-stage Parkinson's Disease patients. Furthermore, the normality index showed potential in the assessment of patient recovery after hip-replacement surgery. In conclusion, this implementation of the gait analysis system illustrated that the framework can be used as a road map for the development of movement analysis systems.

Place, publisher, year, edition, pages
Halmstad: Halmstad University, 2012. p. 52
Series
Halmstad University Dissertations ; 2
Keywords
symbolization, expert knowledge, gait analysis, inertial sensors
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-17523 (URN)978-91-87045-01-1 (ISBN)
Public defence
2012-04-13, Wigforssalen, Halmstad University, Halmstad, 15:49 (English)
Opponent
Supervisors
Available from: 2012-04-18 Created: 2012-04-17 Last updated: 2016-03-09Bibliographically approved

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Sant'Anna, AnitaWickström, Nicholas

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