Abstract
Improving quality of life in geriatric patients is related to constant physical activity and fall prevention. In this paper, we propose a wearable system that takes advantage of sensors embedded in a smart device to collect data for movement identification (running, walking, falling and daily activities) of an elderly user in real-time. To provide high efficiency in fall detection, the sensor’s readings are analysed using a neural network. If a fall is detected, an alert is sent though a smartphone connected via Bluetooth. We conducted an experimental session using an Arduino Nano 33 BLE Sense board in inside and outside environments. The results of the experiment have shown that the system is extremely portable and provides high success rates in fall detection in terms of accuracy and loss.
Highlights
Among the leading causes of severe harm and death in the elderly, there is the problem of falls at home
In this work we have presented a system for fall detection for elderly people
The board interacts with a smartphone application, connected through Bluetooth with the board, which is responsible for getting user feedback to supposed fall events and forwarding emergency calls if necessary
Summary
Among the leading causes of severe harm and death in the elderly, there is the problem of falls at home. How a person falls will dictate the types of injuries that may result. Many elderly patients are reluctant to report falling because they view falling as part of the ageing process or fear being restricted in their activities or hospitalised. Falls can impair the independence of older adults and cause a range of personal and socioeconomic consequences. Falls were responsible for more than 3 million emergency department visits by older persons. We designed a wearable emergency recognition device for elder persons with the aim of detecting dangerous events, such as falls, in order to trigger assistance.
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