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M2M-based smart health service for human UI/UX using motion recognition

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Abstract

Home networks currently dominated by human–object or human–human information production, exchange, processing, and paradigms are transitioning to machine to machine (M2M) due to the sudden introduction of embedded devices. Recently, due to the spread of IT equipment, more M2M-related devices are being used, and M2M-based projects are underway in various fields such as M2M-based u-city, u-port, u-work, u-traffic, etc. M2M has been applied in various fields, and u-healthcare is attracting attention in the M2M medical field. U-healthcare refers to technology in which ordinary patients can receive prescription services from experts by continuously monitoring changes in their health status during daily life at home based on wired and wireless communications infrastructures. In this paper, we propose an M2M-based smart health service for human UI/UX using motion recognition. Non-IP protocol, not TCP/IP protocol, has been used in sensor networks applied to M2M-based u-healthcare. However, sensors should be connected to the Internet in order to expand the use of services and facilitate management of the M2M-based sensor network. Therefore, we designed an M2M-based smart health service considering network mobility since data measured by the sensors should be transferred over the Internet. Unlike existing healthcare platforms, M2M-based smart health services have been developed for motion recognition as well as bio-information. Smart health services for motion recognition can sense four kinds of emotions, anger, sadness, neutrality, and joy, as well as stress using sensors. Further, they can measure the state of the individual by recognizing a user’s respiratory and heart rates using an ECG sensor. In the existing medical environment, most medical information systems managing patient data use a centralized server structure. Using a fixed network, it is easy to collect and process limited data, but there are limits to processing a large amount of data collected from M2M devices in real-time. Generally, M2M communication used in u-healthcare consists of many networked devices and gateways. An M2M network may use standardized wireless technology based on the requirements of a particular device. Network mobility occurs when the connecting point changes according to the movement of any network, and the terminal can be connected without changing its address. If the terminal within the network communicates with any corresponding node, communication between the terminal and corresponding node should be continuously serviced without discontinuation. The method proposed in this paper can easily respond to dynamic changes in the wireless environment and conduct systematic management based on user’s motion recognition using technology to support mobility among sensor nodes in M2M.

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Notes

  1. Life shirt, http://www.vivometrics.com

  2. Nike \(+\) iPod sport kit service, https://secure-nikeplus.nike.com

  3. Map My Fitness, https://support.mapmyfitness.com

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Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2059964).

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Authors and Affiliations

  1. Samsun Company-Affiliated Research, Samsun Technology Research Co. Ltd., 1446-2, Juan 7-dong, Nam-gu, Incheon, 402-207, Korea

    Roy C. Park

  2. Intelligent System Laboratory, School of Computer Information Engineering, Sangji University, 83, Sangjidae-gil Gangwon-do, Wonju-si, 220-702, Korea

    Hoill Jung

  3. Division of Computer Science, Sahmyook University, Hwarangro-815 Nowon-gu, Seoul, 139-742, Korea

    Dong-Kun Shin

  4. Department of Biomedical Engineering, Konyang University, 121 Deahak-ro, Chungnam-do, Nonsan-si , 320-711, Korea

    Gui-Jung Kim

  5. Department of Endocrinology and Metabolism, Seoul St. Mary’s Hospital, The Catholic University, 222, Banpo-daero, Seocho-gu, Seoul, 137-701, Korea

    Kun-Ho Yoon

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  1. Roy C. Park
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Correspondence to Roy C. Park.

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Park, R.C., Jung, H., Shin, DK. et al. M2M-based smart health service for human UI/UX using motion recognition. Cluster Comput 18, 221–232 (2015). https://doi.org/10.1007/s10586-014-0374-z

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