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A wearable gait analysis system using inertial sensors Part II: Evaluation in a clinical setting
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.ORCID iD: 0000-0002-3495-2961
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.ORCID iD: 0000-0002-4143-2948
Center for Person-Centered Care, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Department of Orthopedics, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
2012 (English)In: BIOSIGNALS 2012 - Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing, [S. l.]: SciTePress, 2012, p. 5-14Conference paper, Published paper (Refereed)
Abstract [en]

The gold standard for gait analysis, in-lab 3D motion capture, is not routinely used for clinical assessment due to limitations in availability, cost and required training. Inexpensive alternatives to quantitative gait analysis are needed to increase the its adoption. Inertial sensors such as accelerometers and gyroscopes are promising tools for the development of wearable gait analysis (WGA) systems. The present study evaluates the use of a WGA system on hip-arthroplasty patients in a real clinical setting. The system provides information about gait symmetry and normality. Results show that the normality measurements are well correlated with various quantitative and qualitative measures of recovery and health status.

Place, publisher, year, edition, pages
[S. l.]: SciTePress, 2012. p. 5-14
Keyword [en]
gait analysis, inertial sensors, normality, symmetry
National Category
Other Medical Engineering
Identifiers
URN: urn:nbn:se:hh:diva-17518Scopus ID: 2-s2.0-84861976917ISBN: 9789898425898 (print)OAI: oai:DiVA.org:hh-17518DiVA, id: diva2:516207
Conference
International Conference on Bio-inspired Systems and Signal Processing, BIOSIGNALS 2012, Vilamoura, Algarve, Portugal, 1-4 February, 2012
Note

Partially funded by the Promobilia Foundation.

Available from: 2012-04-18 Created: 2012-04-17 Last updated: 2017-04-21Bibliographically 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
Keyword
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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