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.
Keywords: Bayes methods; Internet of Things; Internet of things based multisensor patient fall detection system; accelerometers; biomedical equipment; body sensor networks; cost-effective integrated system; credit card-sized single board microcomputer; geriatrics; gyroscopes; k-nearest neighbour; medical signal processing; microcomputers; naive Bayes' classifiers; nearest neighbour methods; nonfall motions classification; patient monitoring; pattern classification; sensor data; visual-based classifier.