ISSN 0914-4935
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
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Recognizing Falls, Daily Activities, and Health Monitoring by Smart Devices

Sittichai Sukreep, Khalid Elgazzar, Cheehung Henry Chu, Chakarida Nukoolkit, and Pornchai Mongkolnam

(Received January 21, 2019; Accepted March 25, 2019)

Keywords: health monitoring, smart devices, IoT, daily activity, fall recognition, data mining, classification

One of the biggest challenges in ageing societies is to improve life, health, safety, and support of the elderly population in their daily life. Currently, the number of elderly people living alone is increasing every year. Living alone allows more freedom but raises the risk of serious injuries or fatal accidents. Falls are the key cause of significant health problems, especially for an elderly person who lives alone. Moreover, vital signs such as heart rate, balancing activities, and environment context are crucial based on the user’s condition. To assist people living alone and improve their health quality, we firmly believe that the advances in Smart Devices, Smart Environment, and Internet of Things paradigms are very helpful for developing a fall and activity recognition system. We propose a system using an unobtrusive device consisting of a smartwatch and a smartphone to identify falls and thirteen daily activities (e.g., walking, running, typing, and waving the hand). The transition before falling down, the speed of falling down, the heart rate while doing the activity, and the time passed since then are important data that we store to help a doctor diagnose and rehabilitate a patient. Environment sensors are used to indicate the index level of ambient conditions such as temperature, humidity, brightness and motion detection. Suitable machine learning techniques are applied for daily activity recognition, and the processing time for classification was compared on the basis of a smartwatch and Amazon Web Services (AWS) cloud server. Threshold-based health risk analysis models are utilized for abnormal activity recognition, heart rate, and heat index (temperature and humidity). The system issues different types of notifications such as warning messages, sounding alarms, and phone calls to related persons such as family members, caregivers or doctors. Various easy-to-understand visualizations are presented to track and monitor the subjects in real-time, including heart rate, daily activity summary, health risk status and environment information.

Corresponding author: Sittichai Sukreep




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