Nowadays, the number of elderly and other people living alone is increasing. Although living alone allows more independence, it raises the risk of serious or even fatal accidents. To help assist those who live alone, we propose a monitoring system to detect indoor position by using a smartwatch and beacons, which are effective and low cost, easy to install, convenient, and unobtrusive in daily life. Data mining techniques were applied to classify indoor positioning zones. A noise reduction process combining two data smoothing techniques was incorporated. The best model for indoor positioning was chosen from various algorithms and different window sizes of data to achieve the best usage in a real-time classification. Both the number and positioning of beacons were also considered in this research. Various useful screens with easy-to-understand visualizations are provided for monitoring subject behaviors and time spent in certain areas, giving a summary of indoor positioning. Finally, the system allows users to manage indoor positioning by combining the marked spots as zoning areas. The management of different numbers of beacons and their locations is also provided to users.
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 environmental context are crucial in relation to 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 events leading to a fall, the speed of falling down, the heart rate while doing an activity, and the time passed since the fall are important data that we store to help a doctor diagnose and rehabilitate a patient. Environment sensors are used to indicate the indices of ambient conditions such as temperature, humidity, brightness, and motion detected. Suitable machine learning techniques are used for daily activity recognition, and the processing time for classification was compared on the basis of a smartwatch and an Amazon Web Services (AWS) cloud server. Threshold-based health risk analysis models are utilized for abnormal activity recognition and heart rate and heat index (temperature and humidity) determination. 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 environmental information.
Recent reports show that the average life expectancy is increasing worldwide, posing significant overhead on healthcare systems and increasing demands on long-term care facilities. One of the grand challenges directly related to growing ageing societies is the implications of falling. Many elderly people live alone, especially those in Western countries who cannot afford living in a senior house or retirement facility. In such cases, not only falling is a major concern, but also daily activities must be continuously monitored and analyzed to provide immediate support when needed. Vital signs and environment context are also crucial conditions for pre-and post-event assessments. Thanks to technology advancements and widespread adoption of the Internet of Things which enables us to provide smart and ubiquitous healthcare services. In this paper, we propose iWatch, a smart and flexible system for fall detection and activity recognition using common smart devices, a smartwatch and a smartphone. Machine learning techniques are used to build efficient and highly accurate activity recognition classifiers. iWatch also provides health risk analysis using threshold-based models and leverages visualization tools to better communicate with the user. iWatch is a promising technology that provides a small step in a giant leap to revolutionize healthcare services, especially for those who needs extra care.
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