Internet of things based multi‐sensor patient fall detection system
Sarah Khan, Ramsha Qamar, Rahma Zaheen, A. R. Al-Ali, Ahmad Al Nabulsi, Hasan A. Al-Nashash · Healthcare Technology Letters · 2019
Accidental falls of patients cannot be completely prevented. However, timely fall detection can help prevent further complications such as blood loss and unconsciousness. In this study, the authors present a cost‐effective integrated system designed to remotely detect patient falls in hospitals in addition to classifying non‐fall motions into activities of daily living. The proposed system is a wearable device that consists of a camera, gyroscope, and accelerometer that is interfaced with a credit card‐sized single board microcomputer. The information received from the camera is used in a visual‐based classifier and the sensor data is analysed using the k ‐Nearest Neighbour and Naïve Bayes' classifiers. Once a fall is detected, an attendant at the hospital is informed. Experimental results showed that the accuracy of the device in classifying fall versus non‐fall activity is 95%. Other requirements and specifications are discussed in greater detail.