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Analysis of Health Management Using Physiological Data Based on Continuous Exercise

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Abstract

To accurately predict and monitor an individual’s health, accurate information on the changing patterns of the individual’s physiological signals is required. In this study, obesity parameters were analyzed using regression analysis based on the physiological signals of individuals who performed continuous exercise. To this end, three healthy adult subjects participated in the experiment, and their health management was analyzed by monitoring changes in health information parameters while they performed continuous exercise with daily activities over a long period of time. Subjects’ exercise parameters included heart rate were collected through smart watches, and their specified obesity parameters were measured using a smart scale. The analysis results showed that weight and body fat decreased and muscle composition increased in conjunction with the amount of exercise for two subjects who performed continuous exercise while conducting daily activities. For one subject who did not perform any specific exercise, however, the weight and body muscle rate tended to increase slightly and the body fat rate tended to decrease slightly. It was found that continuous exercise improved the subjects’ ability to adapt to exercise, thereby increasing Max BPM and reducing weight. Performing continuous exercise while conducting daily activities appears to improve physical measures of health, as it decreases weight and body fat rate and increases body muscle rate. Health management analysis based on the physiological data collected from smart devices is expected to play a role in keeping the human body healthy.

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Acknowledgements

This work was supported in part by the Ministry of Trade, Industry and the Korea Evaluation Institute of Industrial Technology Energy Science (KEIT) in 2017 (Core Medical Equipment Technology Development Project/10068076) and by the Basic Science Research Program through the National Foundation of Korea (NRF) funded by the Ministry of Education (No. NRF-2020R1C1C1008728).

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Authors and Affiliations

  1. R&BD Division, CAMTIC Advanced Mechatronics Technology Institute for Commercialization, Jeonju, 54852, Republic of Korea

    Woosuk Chong

  2. Department of Smart Health, Chungbuk Provincial University, Okcheon, 29046, Republic of Korea

    Seonghyun Kim

  3. Department of Energy Storage/Coversion, Engineering of Graduate School, Jeonbuk National University, Jeonju, 54896, Republic of Korea

    Changho Yu

  4. Division of Convergence Technology Engineering, Jeonbuk National University, Jeonju, Jeonbuk, 54896, Republic of Korea

    Changho Yu

  5. Department of Electronic Engineering, Dong Seoul University, Seongnam-si, Gyeonggi-do, 13117, Republic of Korea

    Seongtak Woo

  6. Department of Rehabilitation Engineering, Daegu Hanny University, Gyeongsan, Gyeongsangbuk-do, 54896, Republic of Korea

    Kyong Kim

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  1. Woosuk Chong
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  2. Seonghyun Kim
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  3. Changho Yu
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Correspondence to Kyong Kim.

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Chong, W., Kim, S., Yu, C. et al. Analysis of Health Management Using Physiological Data Based on Continuous Exercise. Int. J. Precis. Eng. Manuf. 22, 899–907 (2021). https://doi.org/10.1007/s12541-021-00503-3

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  • DOI: https://doi.org/10.1007/s12541-021-00503-3

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