Comparison of Some Methods for the Elderly Patient Telemonitoring System
Abstract views: 378

Comparison of Some Methods for the Elderly Patient Telemonitoring System

Hendra Setiawan, Elvira Sukma Wahyuni

Abstract

This paper analyzes some research results related to patient telemonitoring system. The main objective is to collect many useful information for telemonitoring implementation and its development in the future. Telemonitoring system is focused on fall detection that generally occur prior to critical condition. There are 14 research results that discussed in this paper which have been published from 2013 to 2017. Those researches are grouped into three types i.e. intrusive, non-intrusive and mixed. Analysis is done on aspects of the comfort, complexity, cost, accuracy, and coverage. Furthermore, based on those information, a study of application feasibility is done for elderly patients in Indonesia. The result shows that the non-intrusive method using the camera or access point are the most appropriate system for the elderly fall detection.

Keywords

Patient Telemonitoring; Elderly Patient; Fall Detection; Critical Condition

Full Text:

PDF

References

[1] United Nations, "Department of Economic and Social Affairs, Population Division", World Population Prospects: The 2017 Revision, Key Findings and Advance Tables. Working Paper No. ESA/P/WP/248, 2017.

[2] Söhretoglu, D., & Arroo, R. R. J. "Dietary Flavonoids and The Prevention of Degenerative Diseases". Studium Press LLC, USA, 2015.

[3] Dallolio, et. al. 2008. "Functional and clinical outcomes of telemedicine in patients with spinal cord injury", Archives of Physical Medicine and Rehabilitation, vol. 89, Issue: 12, pp. 2332–2341, 2008.

[4] K.H. Bowles, dan A.C. Baugh. "Applying research evidence to optimize telehomecare". J. Cardiovasc Nurs, 22(1), pp. 5–15, 2007.

[5] Mubashir, M., Shao, L., & Seed, L. "A survey on fall detection: Principles and approaches". Neurocomputing, 100, pp.144-152, 2013.

[6] Rougier, C., Meunier, J., St-Arnaud, A., & Rousseau, J. "3D head tracking for fall detection using a single calibrated camera". Image and Vision Computing, 31(3), pp.246-254, 2013.

[7] Dubois, A., & Charpillet, F. "Automatic fall detection system with a RGB-D camera using a hidden Markov model". In International Conference on Smart Homes and Health Telematics, pp. 259-266. Springer, Berlin, Heidelberg, 2013.

[8] Castillo, J. C., Carneiro, D., Serrano-Cuerda, J., Novais, P., Fernández-Caballero, A., & Neves, J. "A multi-modal approach for activity classification and fall detection". International Journal of Systems Science, 45(4), pp.810-824, 2014.

[9] Bevilacqua, V., Nuzzolese, N., Barone, D., Pantaleo, M., Suma, M., D'Ambruoso, D., Volpe, A., Loconsole, C. and Stroppa, F. "Fall detection in indoor environment with kinect sensor". In Innovations in Intelligent Systems and Applications (INISTA) Proceedings, 2014 IEEE International Symposium on, pp. 319-324, 2014.

[10] Ye, Z., Li, Y., Zhao, Q., & Liu, X. "A falling detection system with wireless sensor for the elderly people based on ergonomics". International Journal of Smart Home, 8(1), pp.187-196, 2014.

[11] Bian, Z. P., Hou, J., Chau, L. P., & Magnenat-Thalmann, N. "Fall detection based on body part tracking using a depth camera". IEEE journal of biomedical and health informatics, 19(2), pp. 430-439, 2015.

[12] M.G. Amin, M. G., Zhang, Y. D., Ahmad, F., & Ho, K. D. "Radar signal processing for elderly fall detection: The future for in-home monitoring". IEEE Signal Processing Magazine, 33(2), pp.71-80, 2016.

[13] Dias, P. V. G., Costa, E. D. M., Tcheou, M. P., & Lovisolo, L. (2016). "Fall detection monitoring system with position detection for elderly at indoor environments under supervision". In Communications (LATINCOM), 8th IEEE Latin-American Conference on, pp. 1-6, 2016.

[14] Wang, Y., Wu, K., & Ni, L. M. "Wifall: Device-free fall detection by wireless networks". IEEE Transactions on Mobile Computing, 16(2), pp.581-594, 2017.

[15] Santiago, J., Cotto, E., Jaimes, L. G., & Vergara-Laurens, I. "Fall detection system for the elderly". In Computing and Communication Workshop and Conference (CCWC), 2017 IEEE 7th Annual, pp. 1-4, 2017.

[16] Saadeh, W., Altaf, M. A. B., & Altaf, M. S. B. "A high accuracy and low latency patient-specific wearable fall detection system". In Biomedical & Health Informatics (BHI), 2017 IEEE EMBS International Conference on, pp. 441-444, 2017.

[17] Hwang, S., Ahn, D., Park, H., & Park, T. (2017). "Maximizing Accuracy of Fall Detection and Alert Systems Based on 3D Convolutional Neural Network". In Proceedings of the Second International Conference on Internet-of-Things Design and Implementation, pp. 343-344, ACM, 2017.

[18] Ozcan, K., Velipasalar, S., & Varshney, P. K. "Autonomous Fall Detection With Wearable Cameras by Using Relative Entropy Distance Measure". IEEE Transactions on Human-Machine Systems, 47(1), pp.31-39, 2017.

[19] Erol, B., Amin, M. G., & Boashash, B. "Range-Doppler radar sensor fusion for fall detection". In Radar Conference (RadarConf), 2017 IEEE, pp. 0819-0824, 2017.

Refbacks

  • There are currently no refbacks.

Referencing Software:

Checked by:

Supervised by:

Statistic:

View My Stats


Creative Commons License Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control by http://kinetik.umm.ac.id is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.