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Sensor Signal Processing to Extract Features from Finger Temperature in a Case-Based Stress Classification Scheme
Mälardalens högskola, Akademin för innovation, design och teknik.ORCID-id: 0000-0002-1212-7637
2009 (engelsk)Inngår i: WISP 2009: 6TH IEEE INTERNATIONAL SYMPOSIUM ON INTELLIGENT SIGNAL PROCESSING, PROCEEDINGS, 2009, s. 193-198Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

One of the physiological parameters for quantifying stress levels is the finger temperature that helps the clinician in diagnosis and treatment of stress. However, this pattern of the finger temperature sensor signal is so individual and in practice, it is difficult and tedious even for experienced clinicians to interpret and analyze the signal to classify individual stress levels. So there is an inherent need to develop methods or techniques providing computational solution to utilize this sensor signal in a computer-based system. This paper presents a feature extraction approach based on finger temperature sensor signal. The extracted features are then used to formulate cases in a case-based reasoning system to classify individual sensitivity to stress. The evaluation result shows an encouraging performance to apply the approach in feature extraction from slowly changing sensor signals such as finger temperature signal. 

sted, utgiver, år, opplag, sider
2009. s. 193-198
HSV kategori
Identifikatorer
URN: urn:nbn:se:mdh:diva-8984DOI: 10.1109/WISP.2009.5286562ISI: 000276341800034Scopus ID: 2-s2.0-71249105449ISBN: 978-1-4244-5058-9 (tryckt)OAI: oai:DiVA.org:mdh-8984DiVA, id: diva2:301554
Konferanse
6th IEEE International Symposium on Intelligent Signal Processing Location: Budapest, HUNGARY Date: AUG 26-28, 2009
Tilgjengelig fra: 2010-03-03 Laget: 2010-03-03 Sist oppdatert: 2013-12-03bibliografisk kontrollert
Inngår i avhandling
1. A Personalised Case-Based Stress Diagnosis System Using Physiological Sensor Signals
Åpne denne publikasjonen i ny fane eller vindu >>A Personalised Case-Based Stress Diagnosis System Using Physiological Sensor Signals
2011 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Stress is an increasing problem in our present world. It is recognised that increased exposure to stress may cause serious health problems if undiagnosed and untreated. In stress medicine, clinicians’ measure blood pressure, Electrocardiogram (ECG), finger temperature and respiration rate etc. during a number of exercises to diagnose stress-related disorders. However, in practice, it is difficult and tedious for a clinician to understand, interpret and analyze complex, lengthy sequential sensor signals. There are few experts who are able to diagnose and predict stress-related problems. Therefore, a system that can help clinicians in diagnosing stress is important.

This research work has investigated Artificial Intelligence techniques for developing an intelligent, integrated sensor system to establish diagnosis and treatment plans in the psychophysiological domain. This research uses physiological parameters i.e., finger temperature (FT) and heart rate variability (HRV) for quantifying stress levels.  Large individual variations in physiological parameters are one reason why case-based reasoning is applied as a core technique to facilitate experience reuse by retrieving previous similar cases. Feature extraction methods to represent important features of original signals for case indexing are investigated. Furthermore, fuzzy techniques are also employed and incorporated into the case-based reasoning system to handle vagueness and uncertainty inherently existing in clinicians’ reasoning.

The evaluation of the approach is based on close collaboration with experts and measurements of FT and HRV from ECG data. The approach has been evaluated with clinicians and trial measurements on subjects (24+46 persons). An expert has ranked and estimated the similarity for all the subjects during classification. The result shows that the system reaches a level of performance close to an expert in both the cases. The proposed system could be used as an expert for a less experienced clinician or as a second opinion for an experienced clinician to supplement their decision making tasks in stress diagnosis.

sted, utgiver, år, opplag, sider
Västerås: Mälardalen University, 2011
Serie
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 103
HSV kategori
Forskningsprogram
datavetenskap
Identifikatorer
urn:nbn:se:mdh:diva-12257 (URN)978-91-7485-018-5 (ISBN)
Disputas
2011-06-20, Pi, Mälardalens högskola, Västerås, 13:35 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2011-05-16 Laget: 2011-05-15 Sist oppdatert: 2018-01-12bibliografisk kontrollert

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