Data Processing of Physiological Sensor Data and Alarm Determination Utilising Activity Recognition
Main Article Content
Keywords
Body sensors, WBAN, IoT, Activity Recognition, Inference
Abstract
Current physiological sensors are passive and transmit sensed data to Monitoring centre (MC) through wireless body area network (WBAN) without processing data intelligently. We propose a solution to discern data requestors for prioritising and inferring data to reduce transactions and conserve battery power, which is important requirements of mobile health (mHealth). However, there is a problem for alarm determination without knowing the activity of the user. For example, 170 beats per minute of heart rate can be normal during exercising, however an alarm should be raised if this figure has been sensed during sleep. To solve this problem, we suggest utilising the existing activity recognition (AR) applications. Most of health related wearable devices include accelerometers along with physiological sensors. This paper presents a novel approach and solution to utilise physiological data with AR so that they can provide not only improved and efficient services such as alarm determination but also provide richer health information which may provide content for new markets as well as additional application services such as converged mobile health with aged care services. This has been verified by experimented tests and examples of using vital signs such as heart pulse rate, respiration rate and body temperature with a demonstrated outcome of AR accelerometer sensors integrated with an Android app.
References
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Lim, C. L., Byrne, C., & Lee, J. K. (2008). Human Thermoregulation and Measurement of Body Temperature in Exercise and Clinical Settings. Ann Acad Med Singapore, 37, 347-353.
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Pantelopoulos, A., & Bourbakis, N. G. (2010). A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 40(1), 1-12. doi:DOI: http://dx.doi.org/10.1109/TSMCC.2009.2032660
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Tang, W., & Sazonov, E. S. (2014). Highly Accurate Recognition of Human Postures and Activities Through Classification With Rejection. IEEE Journal of Biomedical and Health Informatics, 18(1), 309-315. doi:http://dx.doi.org/10.1109/JBHI.2013.2287400
Vorvick, L. J. (2015). Vital signs. U.S. National Library of Medicine.
Zhang, L., Wu, X., & Luo, D. (2015, 2-5 Aug. 2015). Improving activity recognition with context information. Paper presented at the 2015 IEEE International Conference on Mechatronics and Automation (ICMA).
Abdallah, Z. S., Gaber, M. M., Srinivasan, B., & Krishnaswamy, S. (2012, 7-9 Nov. 2012). StreamAR: Incremental and Active Learning with Evolving Sensory Data for Activity Recognition. Paper presented at the 2012 IEEE 24th International Conference on Tools with Artificial Intelligence.
Arnon, P. (2014, 2-4 Sept. 2014). Classification model for multi-sensor data fusion apply for Human Activity Recognition. Paper presented at the Computer, Communications, and Control Technology (I4CT), 2014 International Conference on.
Atallah, L., Lo, B., King, R., & Yang, G. Z. (2011). Sensor Positioning for Activity Recognition Using Wearable Accelerometers. IEEE Transactions on Biomedical Circuits and Systems, 5(4), 320-329. doi: http://dx.doi.org/10.1109/TBCAS.2011.2160540
Chen, Y., Guo, M., & Wang, Z. (2016, 14-16 Feb. 2016). An improved algorithm for human activity recognition using wearable sensors. Paper presented at the 2016 Eighth International Conference on Advanced Computational Intelligence (ICACI).
Chernbumroong, S., Atkins, A. S., & Yu, H. (2011, 8-11 Sept. 2011). Activity classification using a single wrist-worn accelerometer. Paper presented at the Software, Knowledge Information, Industrial Management and Applications (SKIMA), 2011 5th International Conference on.
Dohn, P., x00E, lek, Gajdo, P., x, & Peterek, T. (2013, 2-4 July 2013). Human activity recognition on raw sensor data via sparse approximation. Paper presented at the Telecommunications and Signal Processing (TSP), 2013 36th International Conference on.
Fujimoto, T., Nakajima, H., Tsuchiya, N., Marukawa, H., Kuramoto, K., Kobashi, S., & Hata, Y. (2013, 22-24 May 2013). Wearable Human Activity Recognition by Electrocardiograph and Accelerometer. Paper presented at the Multiple-Valued Logic (ISMVL), 2013 IEEE 43rd International Symposium on.
Ganong, W. F., & Barrett, K. E. (1995). Review of medical physiology: Appleton & Lange Norwalk, CT.
Hong, J. H., Ramos, J., & Dey, A. K. (2016). Toward Personalized Activity Recognition Systems With a Semipopulation Approach. IEEE Transactions on Human-Machine Systems, 46(1), 101-112.doi:http://dx.doi.org/doi:10.1109/THMS.2015.2489688
Karvonen, M. J., Kentala, E., & Mustala, O. (2010). The effects of training on heart rate: a longitudinal study. JPAH, 4(3).
Lim, C. L., Byrne, C., & Lee, J. K. (2008). Human Thermoregulation and Measurement of Body Temperature in Exercise and Clinical Settings. Ann Acad Med Singapore, 37, 347-353.
Mackowiak, P. A. (2000). Temperature regulation and the pathogenesis of fever. Principles and practice of infectious diseases, 6, 703-718.
Miu, T., Missier, P., Pl, T., x00F, & tz. (2015, 26-28 Oct. 2015). Bootstrapping Personalised Human Activity Recognition Models Using Online Active Learning. Paper presented at the Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), 2015 IEEE International Conference on.
Orha, I., & Oniga, S. (2014, 23-26 Oct. 2014). Study regarding the optimal sensors placement on the body for human activity recognition. Paper presented at the Design and Technology in Electronic Packaging (SIITME), 2014 IEEE 20th International Symposium for.
Orha, I., & Oniga, S. (2015, 22-25 Oct. 2015). Activity recognition using an e-textile data acquisition system. Paper presented at the Design and Technology in Electronic Packaging (SIITME), 2015 IEEE 21st International Symposium for.
Pantelopoulos, A., & Bourbakis, N. G. (2010). A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 40(1), 1-12. doi:DOI: http://dx.doi.org/10.1109/TSMCC.2009.2032660
Sheldon, L. (2016, Mar 12, 2011). Normal Heart Rate When Walking. Retrieved from http://www.livestrong.com/article/401591-normal-heart-rate-when-walking/
Tang, W., & Sazonov, E. S. (2014). Highly Accurate Recognition of Human Postures and Activities Through Classification With Rejection. IEEE Journal of Biomedical and Health Informatics, 18(1), 309-315. doi:http://dx.doi.org/10.1109/JBHI.2013.2287400
Vorvick, L. J. (2015). Vital signs. U.S. National Library of Medicine.
Zhang, L., Wu, X., & Luo, D. (2015, 2-5 Aug. 2015). Improving activity recognition with context information. Paper presented at the 2015 IEEE International Conference on Mechatronics and Automation (ICMA).
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Published
Sep 24, 2016
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Data Processing of Physiological Sensor Data and Alarm Determination Utilising Activity Recognition. (2016). International Journal of Information, Communication Technology and Applications, 2(1), 108-131. https://doi.org/10.17972/ijicta20162132
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Author Biography
James Jin Kang, Deakin University
James Kang is currently a Ph.D. candidate at the School of Information Technology in Deakin University, Australia with interests in mHealth networks, IoT, big data and Health Informatics. He has worked in the ICT industry for over 20 years in roles such as solutions design, testing, deployment, operation and technical support. He has specialised in Intelligent Networks for wired and mobile networks during the earlier stages of his career, and later worked on IP, IMS, NGN and VoIP technologies. James has experience with major solutions and service providers such as LG, Telecom NZ, Vodafone, Siemens, Telstra, Alcatel-Lucent and NBN Co. He has recently went to Africa as a volunteer IT advisor sponsored by the Australian government (DFAT) to help NGOs.