An acceleration vector variance based method for energy expenditure estimation in real-life environment with a smartphone/smartwatch integration |
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| Affiliation: | 1. INRA Unité de Nutrition Humaine UMR 1019, CRNH d''Auvergne 63009 Clermont-Ferrand, France;2. Université d''Auvergne, CHU Clermont-Ferrand, 63003 Clermont-Ferrand, France;3. LIMOS, UMR6520 CNRS 6158, Campus des Cézeaux, 63173 Aubière Cedex, France;4. Center for Biomedical and Healthcare Engineering, Ecole Nationale Supérieure des Mines de Saint Etienne, 158 Cours Fauriel, 42023 Saint Etienne, France;5. CRCGM, EA3849, 63000 Clermont-Ferrand, France;1. Department of Mathematics, VIT University Vellore Campus, Tamil Nadu, INDIA;2. Department of Mathematics, NIT, Silchar, Assam, India;1. Facultad de Ingeniería Informática, Instituto Superior Politécnico “José Antonio Echeverría” (CUJAE), Marianao 19390, La Habana, Cuba;2. Dep. Ingeniería del Software e Inteligencia Artificial, Universidad Complutense de Madrid, Spain;1. Institute of Computer and Communication Engineering, National Cheng Kung University, Tainan 701, Taiwan, ROC;2. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 807, Taiwan, ROC;3. Department of Information Management, Lunghwa University of Science and Technology, Taoyuan 333, Taiwan, ROC;1. School of Computing, National University of Singapore, 117417, Singapore;2. Handal Indah Sdn Bhd, 728789, Singapore;3. Department of Computer and Information, Hefei University of Technology, Anhui, 230009, China;4. National University of Singapore Research Institute, Suzhou, 215123, China;5. College of Computer Science, Zhejiang University, Hangzhou, 310027, China;1. Bioengineering and Telemedicine group, Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Avd. Complutense 30, 28040, Madrid, Spain;2. CIBER-BBN: Networking Research Centre for Bioengineering, Biomaterials and Nanomedicine, Madrid, Spain;3. Endocrinology and Nutrition department, Hospital de Sabadell. Parc Taulí 1, 08208 Sabadell, Spain |
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| Abstract: | By combining embedded passive sensing technologies from both smartphone and smartwatch, it is possible to obtain a high quality detection of sedentary activities (sitting, reclining posture…), movements (walking…) and periods of more intense body movements (running…). Our research encompasses the definition of an energy-saving function for the total energy expenditure (TEE) estimation using accelerometry data. This topic is clearly at the crossroad of both computer science and medical research. The present contribution proposes an intelligent wearable system, which combines the use of two complementary devices: smartphone and smartwatch to collect accelerometry data. Together they can precisely discriminate real-world human sedentary and active behaviors and their duration and estimate energy expenditure in real time and in free-living conditions. The results of the study are expected to help subjects to handle their daily-living physical activity notably for being compliant with the physical activity international guidelines (150 min of moderate intensity activity/week). It is also expected that the physical activity feedbacks using these popular devices can prove the effectiveness of such wearable objects to promote individually-adapted healthy behavioral changes. The performance of the proposed function was evaluated by comparing the energy expenditure given by the smartphone and smartwatch with that produced by Armband®. The mean error of TEE between the proposed function and Armband® was less than 4% for an average 6 h period of daily-living activities. The main theoretical contribution is the definition of a new predictive mathematical function of energy expenditure, which competes with the non-public function used in dedicated costly devices such as Armband®. In addition, this work demonstrates the potential of wearable technologies. |
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