WFRF:(Sandi Carmen)
Sökning: WFRF:(Sandi Carmen) > CAFS : Cost-Aware F...
LIBRIS Formathandbok (Information om MARC21)
| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 04082naa a2200385 4500 | |
| 001 | oai:lup.lub.lu.se:ae40aea8-3a17-4ed9-b975-c5029ce8e999 | |
| 003 | SwePub | |
| 008 | 221230s2022 | |||||||||||000 ||eng| | |
| 024 | 7 | a https://lup.lub.lu.se/record/ae40aea8-3a17-4ed9-b975-c5029ce8e9992 URI |
| 024 | 7 | a https://doi.org/10.1109/TBME.2021.31135932 DOI |
| 040 | a (SwePub)lu | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a art2 swepub-publicationtype |
| 072 | 7 | a ref2 swepub-contenttype |
| 100 | 1 | a Momeni, Niloofaru Lund 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 Technology4 aut0 (Swepub:lu)ni4344mo |
| 245 | 1 0 | a CAFS : Cost-Aware Features Selection Method for Multimodal Stress Monitoring on Wearable Devices |
| 264 | 1 | c 2022 |
| 300 | a 13 s. | |
| 520 | a 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. | |
| 650 | 7 | a NATURVETENSKAPx Data- och informationsvetenskapx Datavetenskap0 (SwePub)102012 hsv//swe |
| 650 | 7 | a NATURAL SCIENCESx Computer and Information Sciencesx Computer Sciences0 (SwePub)102012 hsv//eng |
| 653 | a Cost-aware machine learning | |
| 653 | a Cost-constraints feature selection | |
| 653 | a Low-power wearable devices | |
| 653 | a Stress monitoring | |
| 700 | 1 | a Valdés, Adriana Arzau Swiss Federal Institute of Technology4 aut |
| 700 | 1 | a Rodrigues, Joãou Swiss Federal Institute of Technology4 aut |
| 700 | 1 | a Sandi, Carmenu Swiss Federal Institute of Technology4 aut |
| 700 | 1 | a Atienza, Davidu Swiss Federal Institute of Technology4 aut |
| 710 | 2 | a Matematisk statistikb Matematikcentrum4 org |
| 773 | 0 | t IEEE Transactions on Biomedical Engineeringg 69:3, s. 1072-1084q 69:3<1072-1084x 0018-9294 |
| 856 | 4 | u http://dx.doi.org/10.1109/TBME.2021.3113593y FULLTEXT |
| 856 | 4 8 | u https://lup.lub.lu.se/record/ae40aea8-3a17-4ed9-b975-c5029ce8e999 |
| 856 | 4 8 | u https://doi.org/10.1109/TBME.2021.3113593 |
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