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A wearable gait analysis system using inertial sensors Part I: Evaluation of measures of gait symmetry and normality against 3D kinematic data
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
Department of Orthopedics, 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, 180-188 p.Conference paper, (Refereed)
Abstract [en]

Gait analysis (GA) is an important tool in the assessment of several physical and cognitive conditions. The lack of simple and economically viable quantitative GA systems has hindered the routine clinical use of GA in many areas. As a result, patients may be receiving sub-optimal treatment. The present study introduces and evaluates measures of gait symmetry and gait normality calculated from inertial sensor data. These indices support the creation of mobile, cheap and easy to use quantitative GA systems. The proposed method was compared to measures of symmetry and normality derived from 3D kinematic data. Results show that the proposed method is well correlated to the kinematic analysis in both symmetry (r=0.84, p<0.0001) and normality (r=0.81, p<0.0001). In addition, the proposed indices can be used to classify normal from abnormal gait.

Place, publisher, year, edition, pages
[S. l.]: SciTePress, 2012. 180-188 p.
Keyword [en]
gait analysis, symmetry, normality, motion capture, inertial sensors
National Category
Other Medical Engineering
Identifiers
URN: urn:nbn:se:hh:diva-17517Scopus ID: 2-s2.0-84861964337ISBN: 9789898425898 (print)OAI: oai:DiVA.org:hh-17517DiVA: diva2:516201
Conference
International Conference on Bio-inspired Systems and Signal Processing, BIOSIGNALS 2012, Vilamoura, Algarve, 1-4 February, 2012
Projects
AccelGait
Note

Partially funded by the PromobiliaFoundation and the Institute of Health and Care Sci-ences, Sahlgrenska Academy, University of Gothen-burg, Sweden.

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. 52 p.
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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