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dc.contributor.authorChowdhury, AK
dc.contributor.authorTjondronegoro, D
dc.contributor.authorChandran, V
dc.contributor.authorZhang, J
dc.contributor.authorTrost, SG
dc.date.accessioned2020-03-20T04:44:45Z
dc.date.available2020-03-20T04:44:45Z
dc.date.issued2019
dc.identifier.issn1424-8220
dc.identifier.doi10.3390/s19204509
dc.identifier.urihttp://hdl.handle.net/10072/392504
dc.description.abstractThis study examined the feasibility of a non-laboratory approach that uses machine learning on multimodal sensor data to predict relative physical activity (PA) intensity. A total of 22 participants completed up to 7 PA sessions, where each session comprised 5 trials (sitting and standing, comfortable walk, brisk walk, jogging, running). Participants wore a wrist-strapped sensor that recorded heart-rate (HR), electrodermal activity (Eda) and skin temperature (Temp). After each trial, participants provided ratings of perceived exertion (RPE). Three classifiers, including random forest (RF), neural network (NN) and support vector machine (SVM), were applied independently on each feature set to predict relative PA intensity as low (RPE ≤ 11), moderate (RPE 12–14), or high (RPE ≥ 15). Then, both feature fusion and decision fusion of all combinations of sensor modalities were carried out to investigate the best combination. Among the single modality feature sets, HR provided the best performance. The combination of modalities using feature fusion provided a small improvement in performance. Decision fusion did not improve performance over HR features alone. A machine learning approach using features from HR provided acceptable predictions of relative PA intensity. Adding features from other sensing modalities did not significantly improve performance.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherMDPI AG
dc.relation.ispartofpagefrom4509:1
dc.relation.ispartofpageto4509:14
dc.relation.ispartofissue20
dc.relation.ispartofjournalSensors (Switzerland)
dc.relation.ispartofvolume19
dc.subject.fieldofresearchAnalytical Chemistry
dc.subject.fieldofresearchDistributed Computing
dc.subject.fieldofresearchElectrical and Electronic Engineering
dc.subject.fieldofresearchEnvironmental Science and Management
dc.subject.fieldofresearchEcology
dc.subject.fieldofresearchcode0301
dc.subject.fieldofresearchcode0805
dc.subject.fieldofresearchcode0906
dc.subject.fieldofresearchcode0502
dc.subject.fieldofresearchcode0602
dc.subject.keywordsBorg’s RPE
dc.subject.keywordsclassification
dc.subject.keywordsmachine learning
dc.subject.keywordsmotion sensors
dc.subject.keywordsneural networks
dc.titlePrediction of relative physical activity intensity using multimodal sensing of physiological data
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationChowdhury, AK; Tjondronegoro, D; Chandran, V; Zhang, J; Trost, SG, Prediction of relative physical activity intensity using multimodal sensing of physiological data, Sensors (Switzerland), 2019, 19 (20), pp. 4509-4509
dcterms.dateAccepted2019-10-15
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2020-03-20T03:55:00Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
gro.hasfulltextFull Text
gro.griffith.authorTjondronegoro, Dian W.


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