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Symbolization of time series: an evaluation of SAX, persist, and ACA
Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Laboratoriet för intelligenta system.ORCID-id: 0000-0002-3495-2961
Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Laboratoriet för intelligenta system.ORCID-id: 0000-0002-4143-2948
2011 (engelsk)Inngår i: 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, s. 2223-2228Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Piscataway, N.J.: IEEE Press, 2011. s. 2223-2228
Emneord [en]
symbolization
HSV kategori
Identifikatorer
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, id: diva2:516193
Konferanse
4th International conference on Image and Signal Processing (CISP)
Tilgjengelig fra: 2012-04-18 Laget: 2012-04-17 Sist oppdatert: 2016-03-09bibliografisk kontrollert
Inngår i avhandling
1. A Symbolic Approach to Human Motion Analysis Using Inertial Sensors: Framework and Gait Analysis Study
Åpne denne publikasjonen i ny fane eller vindu >>A Symbolic Approach to Human Motion Analysis Using Inertial Sensors: Framework and Gait Analysis Study
2012 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Halmstad: Halmstad University, 2012. s. 52
Serie
Halmstad University Dissertations ; 2
Emneord
symbolization, expert knowledge, gait analysis, inertial sensors
HSV kategori
Identifikatorer
urn:nbn:se:hh:diva-17523 (URN)978-91-87045-01-1 (ISBN)
Disputas
2012-04-13, Wigforssalen, Halmstad University, Halmstad, 15:49 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2012-04-18 Laget: 2012-04-17 Sist oppdatert: 2016-03-09bibliografisk kontrollert

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