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Wearable-Based Intelligent Emotion Monitoring in Older Adults during Daily Life Activities
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-6566-3097
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0001-9059-6175
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-0804-8637
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-3908-4921
2023 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 13, no 9, article id 5637Article in journal (Refereed) Published
Abstract [en]

We present a system designed to monitor the well-being of older adults during their daily activities. To automatically detect and classify their emotional state, we collect physiological data through a wearable medical sensor. Ground truth data are obtained using a simple smartphone app that provides ecological momentary assessment (EMA), a method for repeatedly sampling people's current experiences in real time in their natural environments. We are making the resulting dataset publicly available as a benchmark for future comparisons and methods. We are evaluating two feature selection methods to improve classification performance and proposing a feature set that augments and contrasts domain expert knowledge based on time-analysis features. The results demonstrate an improvement in classification accuracy when using the proposed feature selection methods. Furthermore, the feature set we present is better suited for predicting emotional states in a leave-one-day-out experimental setup, as it identifies more patterns.

Place, publisher, year, edition, pages
MDPI , 2023. Vol. 13, no 9, article id 5637
Keywords [en]
activities for daily life (ADL), artificial intelligence, affective computing, machine learning, medical wearable, mental well-being, older adults, smart health
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:oru:diva-106058DOI: 10.3390/app13095637ISI: 000986950700001Scopus ID: 2-s2.0-85159278222OAI: oai:DiVA.org:oru-106058DiVA, id: diva2:1759506
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Knut and Alice Wallenberg FoundationAvailable from: 2023-05-26 Created: 2023-05-26 Last updated: 2024-01-03Bibliographically approved

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Gutiérrez Maestro, EduardoAlmeida, Tiago Rodrigues deSchaffernicht, ErikMartinez Mozos, Oscar

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