A Framework on Health Smart Home Using IoT and Machine Learning for Disabled People
DOI:
https://doi.org/10.61841/qcer6x08Keywords:
Smart Environment, IoT, Machine Learning, Fuzzy LogicAbstract
The creation of accessible environments that promote independence, participation has been and remains a point of argument by and for disabled people. People with disabilities face large amount of hurdles and problems while trying to get help in the field of healthcare. In looking after disabled people the amount of services are limited, the treatment may be cost-effective and face many physical barriers. The paper presents a generic framework for an intelligent health smart home making the possibility for a person with disability to live in the comforts of home and be provided with healthcare services. The proposal of system is to develop an IoT based mobile gateway solution for Interactive Home wireless system, monitoring and control system and data acquisition system to display on mobile phone using S MS and email alert. The IOT applications developed in healthcare domain in smart home include mobile based control system, monitoring system and decision-making system. Machine Learning is being employed to enable the IoT system to analyze sensor data, look for correlations and determine the best response to take. By detecting the values of the sensors, the temperature, smoke and presence of fire are found out in the environment and assuring the disabled person to handle any situation through IoT. Moreover, information about patient’s heart rate, ECG and body temperature is collected and sent to a decision-making system which contains fuzzy rules which calculates how frequently patient data are classified as normal and critical and also decides whether to store the data in database or forwarding to a caretaker to take immediate actions so that critical situations can be avoided.
Downloads
References
[1] Ruijiao Li, Bowen Lu, Klaus D. McDonald- Maier (2015) Cognitive assisted living ambient system: a survey. Digital Communications and Networks Vol. 1, pp229–252.
[2] Alexandre Santos, Joaquim Macedo, Antonio Costa, M, Joao Nicolau (2014) Internet of Things and Smart Objects for M-Health Monitoring and Control. Procedia Technology Vol. 16 pp. 1351–1360.
[3] M. Wcislik, M. Pozoga, P. Smerdzynski (2015) Wireless Health Monitoring system. IFAC-Papers Online,Vol. 48 (4) pp. 312–317.
[4] J. Vanus, T. Novak, J. Koziorek, J. Konecny, R. Hrbac (2013) The Proposal Model of Energy Savings of Lighting Systems in the SmartHomeCare.12th IFAC Conference on Programmable Devices and Embedded Systems The International Federation of Automat IC Control September 25-27. Velke Karlovice, Czech Republic, Vol. 26(28), pp. 413 –415.
[5] J. Vanus, J. Koziorek, R. Hercik (2013) Design of a smart building control with view to the senior citizens' needs. 12th IFAC Conference on Programmable Devices and Embedded Systems. The International Federation of Automat IC Control September 25-27. Velke Karlovice, Czech Republic, Vol. 46 (28), pp.422 –427.
[6] Anne Hakansson, Ronald Hartung (2014). An infrastructure for individualized and intelligent decision making and negotiation in cyber- physical systems. Procedia Computer Science Vol. 35 pp822–831.
[7] Sarah N. Abdulkader, Ayman Atia, Mostafa-Sami M.Mostafa (2015) Brain computer interfacing: Applications and challenges. Egyptian Informatics Journal Vol 16, pp213–230.
[8] Anthony Fleury, Michel Vacher, Norbert Noury (2010). SVM-Based Multimodal Classification of Activities of Daily Living in Health Smart Homes: Sensors, Algorithms, and First Experimental Results. IEEE Transactions on Information Technology in biomedicine, Vol. 14(2), pp.274- 283
[9] Samaneh Zolfaghari, Raziyeh Zall, Mohammad Reza Keyvanpour (2016). SOnAr: Smart Ontology Activity recognition framework to fulfill Semantic Web in smarthomes. 2016 Second International Conference on Web Research (ICWR) 23 June, DOI:10.1109/ICWR.2016.7498458.
[10] Pinar Kirci, Gokhan Kurt (2015). Smart Phones and Health Monitoring. The Fourth International Conference on Future Generation Communication Technologies (FGCT2015) 26 October, DOI:10.1109/FGCT.2015.7300236.
[11] Lin Yang, Yanhong Ge, Wenfeng Li, WenbiRao, Weiming Shen (2014). A Home Mobile Healthcare System for Wheel chair Users. Proceedings of the 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design 21-23 May, DOI: 10.1109/CSCWD.2014.6846914.
[12] Maruthalingam Pirapinthan, Bruce Moulton, Sara Lal (2011). T rends in home-based safety and health alert support systems for older people. Proceedings of the 6th International Conference on Broadband Communications &Biomedical Applications, November 21 - 24, 2011, Melbourne, Australia,DOI:10.1109/IB2Com.2011.6217921.
[13] Jukka Ojasalo, Heikki Seppälä, Niko Suomalainen, Rob Moonen (2010). Better Technologies and Services for Smart Homes of Disabled People: Empirical Findings from an Explorative Study among Intellectually Disabled. 2010 2nd International Conference on Software Technology and Engineering (ICSTE), 3-5 October, DOI:10.1109/ICSTE.2010.5608845.
[14] WANG Xianmei, LIANG Lingyan, DENGTi, WANG Zhiliang (2010). Smart Home Control System for the disabled using the head and the Mouth Movement. Proceedings of the 29th Chinese Control Conference July 29-31, 2010, Beijing, China.
[15] Michael Marschollek, Wolfram Ludwig, Ines Schapiewksi, Elin Schriever, Rainer Schubert, Hartmut Dybowski, Hubertus Meyer zu Schwabedissen, Juergen Howe, and Reinhold Haux (2007). Multimodal home monitoring of elderly people– first results from the LASS study. 21st International Conference on Advanced Information Networking and Applications Workshops, May 21-23, Vol2.
[16] Diulie J. Freitas, Tiago B. Marcondes, Luis H. V. Nakamura, RodolfoI. Meneguette (2015) A Health Smart Home System to Report Incidents for Disabled People. 2015 International Conference on Distributed Computing in Sensor Systems, 10-12 June, DOI:10.1109/DCOSS.2015.28.
[17] Lorenzo Scalise, Filippo Pietroni, Sara Casaccia, Gian Marco Revel, Andrea Monteriù, Mariorosario Prist, SauroLonghi (2016) Implementation of an At-Home e-Health System Using Heterogeneous Devices, IEEE International Smart Cities Conference (ISC2) 12-15.
[18] Rubin, J., Potes, C., Xu-wilson, M., Dong, J., Rahman, A., Nguyen, H., & Moromisato, D. (2018). International Journal of Medical Informatics An ensemble boosting model for predicting transfer to the pediatric intensive care unit. International Journal of Medical Informatics, 112(November 2017), 15–20.
[19] Gorczyca, M.T., Toscano, N.C., & Cheng, J.D. (2019). The trauma severity model : An ensemble machine learning approach to risk prediction. Computers in Biology and Medicine, 108(October 2018), 9–19.
Downloads
Published
Issue
Section
License
Copyright (c) 2020 AUTHOR
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.