Real-time stress detection based on artificial intelligence for people with an intellectual disability.
People with severe intellectual disabilities (ID) could have difficulty expressing their stress which may complicate timely responses from caregivers. The present study proposes an automatic stress detection system that can work in real-time. The system uses wearable sensors that record physiologica...
| Published in: | Assistive Technology Vol. 36; no. 3; pp. 232 - 241 |
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| Main Authors: | , , , , , , |
| Format: | research tables/charts Journal Article |
| Published: |
Taylor & Francis Ltd
2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=177242162&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177242162 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10400435 YVP jtl: Assistive Technology issn: 10400435 maglogo: Y pubinfo: dt: 2024 vid: 36 iid: 3 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 177242162 172324186 177242162 177242162 10.1080/10400435.2023.2261045 177242162 ppf: 232 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Real-time stress detection based on artificial intelligence for people with an intellectual disability. aug: au: de Vries, Stefan van Oost, Fransje Smaling, Hanneke de Knegt, Nanda Cluitmans, Pierre Smits, Reon Meinders, Erwin affil: Research and Development, Mentech Eindhoven, Eindhoven, The Netherlands sug: subj: Persons with Disabilities Intellectual Disability Artificial Intelligence Utilization Stress, Psychological Diagnosis Human Netherlands Nonexperimental Studies Experimental Studies Male Female Adolescence Descriptive Statistics Post Hoc Analysis T-Tests Analysis of Variance Random Sample Multitasking Behavior Neural Networks (Computer) Physiological Processes Funding Source Product Evaluation Adolescent: 13-18 years Male Female ab: People with severe intellectual disabilities (ID) could have difficulty expressing their stress which may complicate timely responses from caregivers. The present study proposes an automatic stress detection system that can work in real-time. The system uses wearable sensors that record physiological signals in combination with machine learning to detect physiological changes related to stress. Four experiments were conducted to assess if the system could detect stress in people with and without ID. Three experiments were conducted with people without ID (n = 14, n = 18, and n = 48), and one observational study was done with people with ID (n = 12). To analyze if the system could detect stress, the performance of random, general, and personalized models was evaluated. The mixed ANOVA found a significant effect for model type, F(2, 134) = 116.50, p <.001. Additionally, the post-hoc t-tests found that the personalized model for the group with ID performed better than the random model, t(11) = 9.05, p <.001. The findings suggest that the personalized model can detect stress in people with and without ID. A larger-scale study is required to validate the system for people with ID. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Unknown language: English refInfo: holdings: @attributes: islocal: N |
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