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- Momeni, NiloofarLund University,Lunds universitet,Matematisk statistik,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,LTH profilområde: AI och digitalisering,LTH profilområden,Mathematical Statistics,Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH,LTH Profile Area: AI and Digitalization,LTH Profile areas,Faculty of Engineering, LTH,Swiss Federal Institute of Technology (författare)
CAFS : Cost-Aware Features Selection Method for Multimodal Stress Monitoring on Wearable Devices
- Artikel/kapitelEngelska2022
Förlag, utgivningsår, omfång ...
- 2022
- 13 s.
Nummerbeteckningar
- LIBRIS-ID:oai:lup.lub.lu.se:ae40aea8-3a17-4ed9-b975-c5029ce8e999
- https://lup.lub.lu.se/record/ae40aea8-3a17-4ed9-b975-c5029ce8e999URI
- https://doi.org/10.1109/TBME.2021.3113593DOI
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- Språk:engelska
- Sammanfattning på:engelska
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Anmärkningar
- Objective: Today, stress monitoring on wearable devices is challenged by the tension between high-detection accuracy and battery lifetime driven by multimodal data acquisition and processing. Limited research has addressed the classification cost on multimodal wearable sensors, particularly when the features are cost-dependent. Thus, we design a Cost-Aware Feature Selection (CAFS) methodology that trades-off between prediction-power and energy-cost for multimodal stress monitoring. Methods: CAFS selects the most important features under different energy-constraints, which allows us to obtain energy-scalable stress monitoring models. We further propose a self-aware stress monitoring method that intelligently switches among the energy-scalable models, reducing energy consumption. Results: Using CAFS methodology on experimental data and simulation, we reduce the energy-cost of the stress model designed without energy constraints up to 94.37%. We obtain 90.98% and 95.74% as the best accuracy and confidence values, respectively, on unseen data, outperforming state-of-the-art studies. Analyzing our interpretable and energy-scalable models, we showed that simple models using only heart rate (HR) or skin conductance level (SCL), confidently predict acute stress for HR>93.30BPM and non-stress for SCL< 6.42 μS, but, outside these values, a multimodal model using respiration and pulse wave's features is needed for confident classification. Our self-aware acute stress monitoring proposal saves 10x energy and provides 88.72% of accuracy on unseen data. Conclusion: We propose a comprehensive solution for the cost-aware acute stress monitoring design addressing the problem of selecting an optimized feature subset considering their cost-dependency and cost-constraints. Significant: Our design framework enables long-term and confident acute stress monitoring on wearable devices.
Ämnesord och genrebeteckningar
- NATURVETENSKAP Data- och informationsvetenskap Datavetenskap hsv//swe
- NATURAL SCIENCES Computer and Information Sciences Computer Sciences hsv//eng
- Cost-aware machine learning
- Cost-constraints feature selection
- Low-power wearable devices
- Stress monitoring
Biuppslag (personer, institutioner, konferenser, titlar ...)
- Valdés, Adriana ArzaSwiss Federal Institute of Technology (författare)
- Rodrigues, JoãoSwiss Federal Institute of Technology (författare)
- Sandi, CarmenSwiss Federal Institute of Technology (författare)
- Atienza, DavidSwiss Federal Institute of Technology (författare)
- Matematisk statistikMatematikcentrum (creator_code:org_t)
Sammanhörande titlar
- Ingår i:IEEE Transactions on Biomedical Engineering69:3, s. 1072-10840018-9294
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- IEEE Transactions on Biomedical Engineering (Sök värdpublikationen i LIBRIS)
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