Predicting potential Alzheimer medical condition in elderly using IOT sensors - Case study

• Main Authors: CHONG, Zhi Hao Kevin, TEE, Yu Xuan, TOH, Ling Jing, PHANG, Shi Jia, LIEW, Jie Ying, QUECK, Bertran, GOTTIPATI, Swapna
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Alzheimer; ageing population; prediction models; visual analytics; Analytical, Diagnostic and Therapeutic Techniques and Equipment; Databases and Information Systems; Health Information Technology
• Online Access: https://ink.library.smu.edu.sg/sis_research/3834; https://ink.library.smu.edu.sg/context/sis_research/article/4836/viewcontent/Predict_Alzheimers_Camera.pdf
• Institution: Singapore Management University

Ageing population would cause profound problems and the impact is already being felt today in many developed countries such as Singapore. The main concern for the Government is to help the citizens with active ageing through home ownership and good healthcare. With Internet of Things (IoT) gaining traction globally, Singapore is set to take advantage of this technology and leverage it to extend its capabilities towards a graceful Ageing-In-Place for the elderly. This ties in nicely with the expertise of SHINE Seniors project by SMU-iCity Lab, which integrates IT with healthcare in ways that creates innovative IT health solutions that meet the needs of the elderlies. In this project, we study the problem of predicting potential Alzheimer conditions in the elderly through the behavioural analysis models developed from IoT sensors data. Our findings shows that IoT room sensors for location detection can enable us the capture the key three variables of elderly behaviour; excess active levels, sleeping patterns and repetitive actions. The three variables are useful in predicting the early warning signs of Alzheimer and 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 five sensors with the data spread over a period of 6 months.