Conference Proceeding Article
Ageing population would cause profound problemsand the impact is already being felt today in many developed countries such as Singapore. The main concern for the Government is tohelp the citizens with active ageing through home ownership and good healthcare. With Internet of Things (IoT) gaining traction globally, Singapore is setto take advantage of this technology and leverage it to extend its capabilitiestowards a graceful Ageing-In-Place for the elderly. This ties in nicely withthe expertise of SHINESeniors project by SMU-iCity Lab, which integrates ITwith healthcare in ways that creates innovative IT health solutions that meetthe needs of the elderlies. In this project, we study the problem of predictingpotential Alzheimer conditions in the elderly through the behavioural analysis modelsdeveloped from IoT sensors data. Our findings shows that IoT room sensors forlocation detection can enable us the capture the key three variables of elderlybehaviour; excess active levels, sleeping patterns and repetitive actions. Thethree variables are useful in predicting the early warning signs of Alzheimerand we provide recommendations to care-givers based on the prediction analysis.We studied the task on 20 elderly living alone in the flats equipped with fivesensors with the data spread over a period of 6 months.
Alzheimer, ageing population, prediction models, visual analytics
Analytical, Diagnostic and Therapeutic Techniques and Equipment | Health Information Technology
Data Management and Analytics
IRC Conference on Science, Engineering, and Technology, 2017 August 10-11
IEEE Computing Society
City or Country
CHONG, Zhi Hao Kevin; TEE, Yu Xuan; TOH, Ling Jing; PHANG, Shi Jia; LIEW, Jie Ying; QUECK, Bertran; and GOTTIPATI, Swapna.
Predicting potential Alzheimer medical condition in elderly using IOT sensors - Case study. (2017). IRC Conference on Science, Engineering, and Technology, 2017 August 10-11. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3834
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