Wearable health monitoring
Keywords: Wearable Health Monitoring, Activity Recognition, Acceleration Sensors, Context Awareness, Decision Trees, Defuzzification, Fuzzification, Human Wearable Health Monitoring Systems, Machine Learning, Ubiquitous Computing, Wireless Sensor Network

Schalk Wilhelm Pienaar1 , Reza Malekian1,2*

1 Department of Electrical, Electronic and Computer Engineering, University of Pretoria, 0002, Pretoria, South Africa
2 Department of Computer Science and Media Technology, Malmö University, 205 06, Malmö, Sweden 2 This email address is being protected from spambots. You need JavaScript enabled to view it.



Abstract

Wearable health monitoring systems, with a context-awareness sensing ability, can greatly improve the way such devices work, both in terms of its accuracy and efficiency. This paper summarises related work previously performed, their advantages and disadvantages, and how they can be incorporated in future work. The related work covers approaches using devices such as smartphones used in isolation, smartphones combined with wristbands and ARM based microcontrollers interfaced with different sensors. Different approaches for optimizing power usage in wireless systems are also investigated. For context awareness, various papers are analyzed to determine existing activity recognition patterns, and approaches to solve common problems experienced in this field. Furthermore, this paper proposes a system that can be considered for future work, combining methods that are found to be meet a certain set of criteria, to develop a device that would be applicable for use by underground mine workers.



References

  1. V. Shnayder, B. Chen, K. Lorincz, T. Fulford-Jones, M. Welsh, "Sensor Networks for Medical Care". In the Proceedings of the 3rd international conference on Embedded networked sensor systems, NY, USA, 2005. Google Scholar
  2. G. Werner-Allen, K. Lorincz, M. Ruiz, O. Marcillo, J. Johnson, J. Lees, M. Welsh, "Deploying a Wireless Sensor Network on an Active Volcano," IEEE Internet Computing, March 2006, Vol. 10, no. 2, pp. 18-25. Google Scholar
  3. T. Schmid, H. Dubois-Ferrière, M. Vetterli, “SensorScope: Experiences with a wireless building monitoring sensor network”, in Proceedings of the Workshop on Real-World Wireless Sensor Networks, Stockholm, Sweden, June 2005. Google Scholar
  4. B. N. Schilit and M. M. Theimer, "Disseminating active map information to mobile hosts," in IEEE Network, vol. 8, no. 5, pp. 22-32, Sept.-Oct. 1994.
  5. O. D. Lara and M. A. Labrador, "A Survey on Human Activity Recognition using Wearable Sensors," in IEEE Communications Surveys & Tutorials, vol. 15, no. 3, pp. 1192-1209, Third Quarter 2013. Google Scholar
  6. A. Pantelopoulos and N. Bourbakis, “A survey on wearable sensor-based systems for health monitoring and prognosis,” IEEE Trans. Syst., Man Cybern. C, Appl. Rev., vol. 40, pp. 1–12, Jan. 2010. Google Scholar
  7. L. Gatzoulis and I. Iakovidis, “Wearable and portable ehealth systems,” IEEE Eng. Med. Biol. Mag., vol. 26, no. 5, pp. 51–56, Sep./Oct. 2007. Google Scholar
  8. L. C. Jatoba, U. Grossmann, C. Kunze, J. Ottenbacher and W. Stork, "Context-aware mobile health monitoring: Evaluation of different pattern recognition methods for classification of physical activity," 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, BC, 2008, pp. 5250-5253. Google Scholar
  9. J. Wannenburg and R. Malekian, "Physical Activity Recognition From Smartphone Accelerometer Data for User Context Awareness Sensing," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 12, pp. 3142-3149, Dec. 2017. Google Scholar
  10. M. Shoaib, S. Bosch, O. D. Incel, H. Scholten, and P. J. M. Havinga, “Complex human activity recognition using smartphone and Wrist–Worn motion sensors,” Sensors, vol. 16, no. 4, p. 426, 2016. Google Scholar
  11. A. Jain and V. Kanhangad, "Human Activity Classification in Smartphones Using Accelerometer and Gyroscope Sensors," in IEEE Sensors Journal, vol. 18, no. 3, pp. 1169-1177, Feb.1, 1 2018Google Scholar
  12. M. Shoaib, O. D. Incel, H. Scolten and P. Havinga, "Resource consumption analysis of online activity recognition on mobile phones and smartwatches," 2017 IEEE 36th International Performance Computing and Communications Conference (IPCCC), San Diego, CA, 2017, pp. 1-6. Google Scholar
  13. P. Siirtola and J. Röning, "Ready-to-use activity recognition for smartphones," 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), Singapore, 2013, pp. 59-64. Google Scholar
  14. D. Tian, X. Xu, Y. Tao and X. Wang, "An Improved Activity Recognition Method Based on Smart Watch Data," 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), Guangzhou, 2017, pp. 756-759.
  15. A. G. Floares, G. A. Calin, and F. B. Manolache, “Bigger Data Is Better for Molecular Diagnosis Tests Based on Decision Trees,” pp. 288–295, 2016.
  16. P. Pošík, “13 . Introduction to Fuzzy Logic and Fuzzy Systems.”
  17. F. S. Aguiar, L. L. Almeida, A. Ruffino-Netto, A. L. Kritski, F. C. Q. Mello, and G. L. Werneck, “Classification and regression tree (CART) model to predict pulmonary tuberculosis in hospitalized patients,” BMC Pulm. Med., vol. 12, 2012.
  18. M. Ray, “Nearest Neighbours: Pros and Cons,” 2010. [Online]. Available: http://www2.cs.man.ac.uk/~raym8/comp37212/main/node264.html.
  19. G. N. B. Rodriguez, “Don’t be naive with Naive Bayes,” 2017. [Online]. Available: https://workwiththebest.intraway.com/white-paper/dont-be-naive-with-naive-bayes/. [Accessed: 08-Aug-1BC].
  20. S. Vo Que, N. Ta Tri and T. L. P. Nguyen, "Context-aware monitoring model using fuzzy logic for Wireless Sensor Networks in logistics," 2014 International Conference on Advanced Technologies for Communications (ATC 2014), Hanoi, 2014, pp. 78-83. Google Scholar
  21. Zhen-Yu He and Lian-Wen Jin, "Activity recognition from acceleration data using AR model representation and SVM," 2008 International Conference on Machine Learning and Cybernetics, Kunming, 2008, pp. 2245-2250. Google Scholar
  22. K. Frank, M. Röckl, M. J. Vera Nadales, P. Robertson and T. Pfeifer, "Comparison of exact static and dynamic Bayesian context inference methods for activity recognition," 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), Mannheim, 2010, pp. 189-195. Google Scholar
  23. E. M. Tapia et al., "Real-Time Recognition of Physical Activities and Their Intensities Using Wireless Accelerometers and a Heart Rate Monitor," 2007 11th IEEE International Symposium on Wearable Computers, Boston, MA, 2007, pp. 37-40.Google Scholar
  24. J. Parkka, M. Ermes, P. Korpipaa, J. Mantyjarvi, J. Peltola and I. Korhonen, "Activity classification using realistic data from wearable sensors," in IEEE Transactions on Information Technology in Biomedicine, vol. 10, no. 1, pp. 119-128, Jan. 2006. Google Scholar
  25. A. Zhen-Peng, S. Hu-Lin and W. Jun, "Classify and Prospect of Indoor Positioning and Indoor Navigation," 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), Qinhuangdao, 2015, pp. 1893-1897. Google Scholar
  26. Texas Instruments (2018). http://processors.wiki.ti.com/index.php/Contiki-6LOWPAN.
  27. Wang Zhong-qin, Ye Ning, R. Malekian, Zhao Ting-ting, Wang Ru-chuan, Measuring the Similarity of PML Documents with RFID-based Sensors”, International Journal of Ad Hoc and Ubiquitous Computing, Inderscience, Vol.17, No.3, 2014. Google Scholar
  28. Roberts, R. (2007). ENVIRONMENTAL MONITORING SYSTEMS FOR UNDERGROUND MINES. [ebook] Johannesburg: Anglo Technical Division, pp.1-6. Available at: https://www.sciencedirect.com/science/article/pii/S1474667015315330 [Accessed 4 Aug. 2018].

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