| Summary: | Nowadays, elderly people mostly are living independently in their hometowns. Hence, their activities of daily living (ADL) are not monitored by their family and may lead to accident cases such as falling or slipping. This situation can cause trauma such as brain injury or other side effects on their health. In this project, a prototype of fall detection system was developed using wearable sensors via the internet of things (IoT) platform. Wearable sensors which are gyroscope and accelerometer are attached to the elderly person to obtain significant data of falling detection. Several ADLs i.e., walking, standing, sitting on a chair, sitting on the floor, laying on a bed, and sitting to standing will be monitored among the elders. Data analysis is performed to identify the condition between selected ADLs and imitated falls scenarios. From the experimental results, the proposed system can detect falls and send a notification when a fall occurrence is detected with accuracy of 97%. © 2021 IEEE.
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