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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.
Amato et al. (Wed,) studied this question.