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A new measure of movement symmetry in early Parkinson's disease patients using symbolic processing of inertial sensor data
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.ORCID iD: 0000-0002-3495-2961
Oregon Health and Science Univeristy. (Balance Disorders Laboratory)
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.ORCID iD: 0000-0002-4143-2948
2011 (English)In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 58, no 7, p. 2127-2135Article in journal (Refereed) Published
Abstract [en]

Movement asymmetry is one of the motor symptoms associated with Parkinson's Disease (PD). Therefore, being able to detect and measure movement symmetry is important for monitoring the patient's condition.

The present paper introduces a novel symbol based symmetry index calculated from inertial sensor data. The method is explained, evaluated and compared to six other symmetry measures. These measures were used to determine the symmetry of both upper and lower limbs during walking of 11 early-to-mid-stage PD patients and 15 control subjects. The patients included in the study showed minimal motor abnormalities according to the Unified Parkinson's Disease Rating Scale (UPDRS).

The symmetry indices were used to classify subjects into two different groups corresponding to PD or control. The proposed method presented high sensitivity and specificity with an area under the Receiver Operating Characteristic (ROC) curve of 0.872, 9\% greater than the second best method. The proposed method also showed an excellent Intraclass Correlation Coefficient (ICC) of 0.949, 55\% greater than the second best method. Results suggest that the proposed symmetry index is appropriate for this particular group of patients.

Place, publisher, year, edition, pages
Piscataway, N.J.: IEEE , 2011. Vol. 58, no 7, p. 2127-2135
Keyword [en]
gyroscope, Parkinson's disease, symbolization, symmetry
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:hh:diva-15675DOI: 10.1109/TBME.2011.2149521ISI: 000291890000028PubMedID: 21536527Scopus ID: 2-s2.0-79959570629OAI: oai:DiVA.org:hh-15675DiVA, id: diva2:427611
Note
©2011 IEEE. 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 to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Available from: 2011-09-09 Created: 2011-06-28 Last updated: 2017-12-11Bibliographically 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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