Falling, especially in old age, is a serious health threat. Depending on the severity of such accidents, risk of injury, and the helpless state of the elderly and infirm persons, it is difficult to impossible for the concerned persons to call for help. This kind of situation and its associated perceptions already cause anxiety. There is, therefore, much interest in fall detection for persons in need of care and caregivers as these systems promise security and help when needed, and they also provide a feeling of safety. However, modern fall detection systems are mostly wearables or are expensive in their implementation. As such, this paper focuses on a novel approach to fall detection, developed in two iterative main cycles and finally implemented in a real apartment for testing purposes. The proposed fall detection aims to be installed into the baseboard of a room, which allows an easy and cost-effective integration of the proposed system. Especially in rooms like the bathroom, where discretion is very important, the proposed system benefits from its use of light barriers instead of image recognition to identify a fallen person. Additionally, the collected data can be wirelessly transmitted to a server via a gateway using XBee modules and Wi-Fi. The latest developments of the fall detection prototype, including the hardware and software implementation, will be presented in this paper, and its strength and weak points will also be discussed.
|IEEJ Transactions on Electrical and Electronic Engineering
|Published - 2018 5月
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