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Symbolization of time series: an evaluation of SAX, persist, and ACA
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
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: 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. 2223-2228 p.
Keyword [en]
symbolization
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-17516DOI: 10.1109/CISP.2011.6100559Scopus ID: 2-s2.0-84855591065ISBN: 978-142449306-7 OAI: oai:DiVA.org:hh-17516DiVA: diva2:516193
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
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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