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Activity recognitio...
Activity recognition using wearable physiological measurements : Selection of features from a comprehensive literature study
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- Mohino-Herranz, Inma (författare)
- Department of Signal Theory and Communications, University of Alcala, 28805 Alcala de Henares, Madrid, Spain
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- Gil-Pita, Roberto (författare)
- Department of Signal Theory and Communications, University of Alcala, 28805 Alcala de Henares, Madrid, Spain
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- Rosa-Zurera, Manuel (författare)
- Department of Signal Theory and Communications, University of Alcala, 28805 Alcala de Henares, Madrid, Spain
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- Seoane, Fernando, 1976- (författare)
- Karolinska Institutet,Högskolan i Borås,Akademin för textil, teknik och ekonomi,Akademin för vård, arbetsliv och välfärd,Clinical Science, Intervention an Technology, Karolinska Institutet, 17177 Stockholm, Sweden; Department Biomedical Engineering, Karolinska University Hospital, 14186 Stockholm, Sweden,Textilier och bärbara sensorer för P-hälsa
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(creator_code:org_t)
- 2019-12-13
- 2019
- Engelska.
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 19:24
- Relaterad länk:
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https://doi.org/10.3...
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https://hb.diva-port... (primary) (Raw object)
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https://www.mdpi.com...
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https://urn.kb.se/re...
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https://doi.org/10.3...
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http://kipublication...
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Abstract
Ämnesord
Stäng
- Activity and emotion recognition based on physiological signal processing in health care applications is a relevant research field, with promising future and relevant applications, such as health at work or preventive care. This paper carries out a deep analysis of features proposed to extract information from the electrocardiogram, thoracic electrical bioimpedance, and electrodermal activity signals. The activities analyzed are: neutral, emotional, mental and physical. A total number of 533 features are tested for activity recognition, performing a comprehensive study taking into consideration the prediction accuracy, feature calculation, window length, and type of classifier. Feature selection to know the most relevant features from the complete set is implemented using a genetic algorithm, with a different number of features. This study has allowed us to determine the best number of features to obtain a good error probability avoiding over-fitting, and the best subset of features among those proposed in the literature. The lowest error probability that is obtained is 22.2%, with 40 features, a least squares error classifier, and 40 s window length.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering (hsv//eng)
Nyckelord
- activity recognition
- physiological signals
- electrocardiogram
- thoracic electrical bioimpedance
- electrodermal activity
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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