INTELLIGENT PERSONAL HEALTH DEVICES CONVERGED WITH INTERNET OF THINGS NETWORKS

Authors

  • JAMES J. KANG Deakin University
  • HENRY LARKIN Deakin University

Keywords:

Body sensors, WBAN, IoT, mHealth, Personal sensor device (PSD), Body sensor network (BSN), Activity Recognition (AR)

Abstract

Smartphone technology has become more popular and innovative over the last few years, and has led to the prevalence of wearable devices embedded with body sensors for fitness tracking and various smartphone features. Internet of Things (IoT), which can interact with wearables and personal sensor devices (PSDs), is emerging with technologies such as mobile health (mHealth), the cloud, big data and smart environments like smart homes. It may also provide enhanced services utilising health data obtained from physiological sensors. When these sensors are converged with IoT devices, the volume of transactions and traffic are expected to increase immensely due to the increased demand of health data from the IoT network. These additional demands will affect the existing mHealth services. Health service providers may also demand more data to enhance their services such as real-time monitoring and actuation of sensors alongside the existing monitoring of traffic. Both of these situations can cause rapid battery consumption and consume significant bandwidth. Some PSDs are implanted on or inside the body, and may require invasive surgical operations to replace batteries, such as for a heart pacemaker. It is therefore crucial to save and conserve power consumption in order to reduce the frequency of such procedures as well as health data transmission when needed. There has not yet been any research into managing and controlling data processing and transmission to reduce transactions by applying intelligence onto body sensors. This paper provides a novel approach and solution to reduce data transactions in sensors and allow for the transfer of critical data without failure to medical practitioners over IoT traffic. This can be done via an inference system to transfer health data collected by body sensors efficiently and effectively to mHealth and IoT networks. The results from the experiments to reduce bandwidth and battery resources with heart rate sensors show a possible savings in resource usage of between 66% and 99.5%. Battery power can be saved by 3.14 Watts in the experiments if the transmission of a single 1KB data point is reduced, and by 7.47 Watts if the transmission of 628 data points totalling the size of 120KB is reduced. The accuracy of data inference between the originally sensed data and the data transmitted after inference can be maintained by up to 99% or more. Such savings have the potential of making always-on mHealth devices a practical reality. This research contributes a lowoverhead approach to mHealth sensors by inferring the processing and transferring of data.

 

Downloads

Download data is not yet available.

References

D. Bragg, M. Yun, H. Bragg, and H.-A. Choi, "Intelligent Transmission of Patient Sensor Data

in Wireless Hospital Networks," in AMIA Annual Symposium Proceedings, 2012, p. 1139.

R. v. d. Meulen. Gartner Says 6.4 Billion Connected "Things" Will Be in Use in 2016, Up 30

Percent From 2015 [Newsroom Press Release]. Available:

http://www.gartner.com/newsroom/id/3165317, [Accessed: 22 June 2016]

S. Adibi, "An application layer non-repudiation wireless system: A cross-layer approach," in

World of Wireless Mobile and Multimedia Networks (WoWMoM), 2010 IEEE International

Symposium on a, 2010, pp. 1-2.

Australian Government Department of Health. Middle East Respiratory Syndrome (MERS).

Important information for health professionals and the general public on MERS. Available:

http://www.health.gov.au/internet/main/publishing.nsf/Content/ohp-mers-cov.htm, [Accessed:

October 2016]

The Korean Society of Infectious Diseases and Korean Society for Healthcare-associated

Infection Control Prevention, "The Same Middle East Respiratory Syndrome-Coronavirus

(MERS-CoV) yet Different Outbreak Patterns and Public Health Impacts on the Far East Expert

Opinion from the Rapid Response Team of the Republic of Korea," Infection & Chemotherapy,

vol. 47, pp. 247-251, 2015.

J. Evans, "Pandemics and national security," Global Security Studies, vol. 1, pp. 100-109, 2010.

J. J. Kang, S. Adibi, H. Larkin, and T. Luan, "Predictive data mining for Converged Internet of

Things: A Mobile Health perspective," in Telecommunication Networks and Applications

Conference (ITNAC), 2015 International, 2015, pp. 5-10.

J. J. Kang and H. Larkin, "Inference of Personal Sensors in the Internet of Things,"

International Journal of Information, Communication Technology and Applications, vol. 2,

G. Fortino, R. Giannantonio, R. Gravina, P. Kuryloski, and R. Jafari, "Enabling Effective

Programming and Flexible Management of Efficient Body Sensor Network Applications,"

IEEE Transactions on Human-Machine Systems, vol. 43, pp. 115-133, 2013.

J. J. Kang, T. Luan, and H. Larkin, "Data Processing of Physiological Sensor Data and Alarm

Determination Utilising Activity Recognition," International Journal of Information,

Communication Technology and Applications, vol. 2, p. 24, 2016-09-24 2016.

B. J. Oates, Researching information systems and computing. London SAGE, 2006.

R. A. Fisher, The design of experiments, 7th ed. Edinburgh: Oliver & Boyd, 1960.

R. E. Kirk, "Experimental design," Sage handbook of quantitative methods in psychology, pp.

-45, 2009.

D. W. Cheun, "A Framework for Controlling Internet of Things Devices and Inference with

Contexts," PhD, Soongsil University, 2012.

P. D. Haghighi, A. Perera, M. Indrawan-Santiago, and T. M. Huynh, "Situation-aware mobile

health monitoring," in Proceedings of the 11th International Conference on Mobile and

Ubiquitous Systems: Computing, Networking and Services, London, United Kingdom, 2014,

pp. 248-256.

K. Aberer, M. Hauswirth, and A. Salehi, "Infrastructure for Data Processing in Large-Scale

Interconnected Sensor Networks," in 2007 International Conference on Mobile Data

Management, 2007, pp. 198-205.

M. A. Osborne, S. J. Roberts, A. Rogers, S. D. Ramchurn, and N. R. Jennings, "Towards realtime

information processing of sensor network data using computationally efficient multioutput

Gaussian processes," in Proceedings of the 7th international conference on Information

processing in sensor networks, 2008, pp. 109-120.

A. J. Jara, M. A. Zamora-Izquierdo, and A. F. Skarmeta, "Interconnection Framework for

mHealth and Remote Monitoring Based on the Internet of Things," Selected Areas in

Communications, IEEE Journal on, vol. 31, pp. 47-65, 2013.

A. Borodin, Y. Zavyalova, A. Zaharov, and I. Yamushev, "Architectural approach to the

multisource health monitoring application design," in Open Innovations Association (FRUCT),

17TH Conference of, 2015, pp. 16-21.

H. Banaee, M. U. Ahmed, and A. Loutfi, "Data mining for wearable sensors in health

monitoring systems: a review of recent trends and challenges," Sensors, vol. 13, pp. 17472-

, 2013.

X. Zhu, K. Fang, and W. Yongheng, "Predictive Analytics by Using Bayesian Model Averaging

for Large-Scale Internet of Things," International Journal of Distributed Sensor Networks, pp.

-10, 2013.

J. J. Kang and S. Adibi, "A Review of Security Protocols in mHealth Wireless Body Area

Networks (WBAN)," in International Conference on Future Network Systems and Security

, Paris, France, 2015, pp. 61-83.

L. J. Vorvick, "Vital signs," U.S. National Library of Medicine, 2015.

C. L. Lim, C. Byrne, and J. K. Lee, "Human Thermoregulation and Measurement of Body

Temperature in Exercise and Clinical Settings," Ann Acad Med Singapore, vol. 37, pp. 347-53,

J. J. Kang, T. H. Luan, and H. Larkin, "Alarm Notification of Body Sensors Utilising Activity

Recognition and Smart Device Application," presented at the Proceedings of the 14th

International Conference on Advances in Mobile Computing and Multi Media, Singapore,

Singapore, 2016.

B. M. Bass, D. R. Henderson, E. H.-C. Ku, S. J. Lemke, J. M. Rash, L. B. Reiss, et al.,

"Simultaneous cut through and store-and-forward frame support in a network device," ed:

Google Patents, 2000.

M. Brain. A Typical Mote - How Motes Work. Available:

http://computer.howstuffworks.com/mote4.htm, [Accessed: 15 June 2016]

NHMRC, "National Statement on Ethical Conduct in Human Research (2007) - Updated May

," ed: Australian Government National Health and Medical Research Council, 2015.

Downloads

Published

2017-03-28

Issue

Section

Articles