Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A Symbolic Approach to Human Motion Analysis Using Inertial Sensors: Framework and Gait Analysis Study
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
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 [en]
symbolization, expert knowledge, gait analysis, inertial sensors
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hh:diva-17523ISBN: 978-91-87045-01-1 OAI: oai:DiVA.org:hh-17523DiVA: diva2:516246
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
List of papers
1. A Symbol-Based Approach to Gait Analysis From Acceleration Signals: Identification and Detection of Gait Events and a New Measure of Gait Symmetry
Open this publication in new window or tab >>A Symbol-Based Approach to Gait Analysis From Acceleration Signals: Identification and Detection of Gait Events and a New Measure of Gait Symmetry
2010 (English)In: IEEE transactions on information technology in biomedicine, ISSN 1089-7771, E-ISSN 1558-0032, Vol. 14, no 5, 1180-1187 p.Article 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
Keyword
gait analysis, accelerometers, motion language
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-5439 (URN)10.1109/TITB.2010.2047402 (DOI)000282474800006 ()20371410 (PubMedID)2-s2.0-77956380313 (Scopus ID)
Projects
SELIES
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: 2016-03-09Bibliographically approved
2. A new measure of movement symmetry in early Parkinson's disease patients using symbolic processing of inertial sensor data
Open this publication in new window or tab >>A new measure of movement symmetry in early Parkinson's disease patients using symbolic processing of inertial sensor data
2011 (English)In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 58, no 7, 2127-2135 p.Article 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
Keyword
gyroscope, Parkinson's disease, symbolization, symmetry
National Category
Control Engineering
Identifiers
urn:nbn:se:hh:diva-15675 (URN)10.1109/TBME.2011.2149521 (DOI)000291890000028 ()21536527 (PubMedID)2-s2.0-79959570629 (Scopus ID)
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
3. Symbolization of time series: an evaluation of SAX, persist, and ACA
Open this publication in new window or tab >>Symbolization of time series: an evaluation of SAX, persist, and ACA
2011 (English)In: CISP 2011: Proceedings, the 4th International Congress on Image and Signal Processing, 15-17 October 2011, Shanghai, China / [ed] Peihua Qiu, Piscataway, N.J.: IEEE Press, 2011, 2223-2228 p.Conference paper, Published paper (Refereed)
Abstract [en]

Symbolization of time-series has successfully been used to extract temporal patterns from experimental data. Segmentation is an unavoidable step of the symbolization process, and it may be characterized on two domains: the amplitude and the temporal domain. These two groups of methods present advantages and disadvantages each. Can their performance be estimated a priori based on signal characteristics? This paper evaluates the performance of SAX, Persist and ACA on 47 different time-series, based on signal periodicity. Results show that SAX tends to perform best on random signals whereas ACA may outperform the other methods on highly periodic signals. However, results do not support that a most adequate method may be determined a priory.

Place, publisher, year, edition, pages
Piscataway, N.J.: IEEE Press, 2011
Keyword
symbolization
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-17516 (URN)10.1109/CISP.2011.6100559 (DOI)2-s2.0-84855591065 (Scopus ID)978-142449306-7 (ISBN)
Conference
4th International conference on Image and Signal Processing (CISP)
Available from: 2012-04-18 Created: 2012-04-17 Last updated: 2016-03-09Bibliographically approved
4. A wearable gait analysis system using inertial sensors Part I: Evaluation of measures of gait symmetry and normality against 3D kinematic data
Open this publication in new window or tab >>A wearable gait analysis system using inertial sensors Part I: Evaluation of measures of gait symmetry and normality against 3D kinematic data
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, Published 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
Keyword
gait analysis, symmetry, normality, motion capture, inertial sensors
National Category
Other Medical Engineering
Identifiers
urn:nbn:se:hh:diva-17517 (URN)2-s2.0-84861964337 (Scopus ID)9789898425898 (ISBN)
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
5. A wearable gait analysis system using inertial sensors Part II: Evaluation in a clinical setting
Open this publication in new window or tab >>A wearable gait analysis system using inertial sensors Part II: Evaluation in a clinical setting
2012 (English)In: BIOSIGNALS 2012 - Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing, [S. l.]: SciTePress, 2012, 5-14 p.Conference 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
Keyword
gait analysis, inertial sensors, normality, symmetry
National Category
Other Medical Engineering
Identifiers
urn:nbn:se:hh:diva-17518 (URN)2-s2.0-84861976917 (Scopus ID)9789898425898 (ISBN)
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

Open Access in DiVA

fulltext(2556 kB)