title: Analysis of Optimal Sensor Positions for Activity Classification and Application on a Different Data Collection Scenario.
abstract: This paper focuses on optimal sensor positioning for monitoring activities of daily living and investigates different combinations of features and models on different sensor positions, i.e., the side of the waist, front of the waist, chest, thigh, head, upper arm, wrist, and ankle. Nineteen features are extracted, and the feature importance is measured by using the Relief-F feature selection algorithm. Eight classification algorithms are evaluated on a dataset collected from young subjects and a dataset collected from elderly subjects, with two different experimental settings. To deal with different sampling rates, signals with a high data rate are down-sampled and a transformation matrix is used for aligning signals to the same coordinate system. The thigh, chest, side of the waist, and front of the waist are the best four sensor positions for the first dataset (young subjects), with average accuracy values greater than 96%. The best model obtained from the first dataset for the side of the waist is validated on the second dataset (elderly subjects). The most appropriate number of features for each sensor position is reported. The results provide a reference for building activity recognition models for different sensor positions, as well as for data acquired from different hardware platforms and subject groups.
authors.affiliation: School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathumthani 12000, Thailand. p_natthapon@yahoo.com.
authors.forename: Natthapon
authors.initials: N
authors.lastname: Pannurat
authors.affiliation: National Electronics and Computer Technology Center, Pathumthani 12120, Thailand. surapa.thiemjarus@nectec.or.th.
authors.forename: Surapa
authors.initials: S
authors.lastname: Thiemjarus
authors.affiliation: School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathumthani 12000, Thailand. ekawit@siit.tu.ac.th.
authors.forename: Ekawit
authors.initials: E
authors.lastname: Nantajeewarawat
authors.affiliation: International College, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand. isara.an@kmitl.ac.th.
authors.forename: Isara
authors.initials: I
authors.lastname: Anantavrasilp
keywords: activity classification; activity monitoring; sensor positions; wearable sensors