Open AccessInternet of things based multi‐sensor patient fall detection system
Author(s) -
Khan Sarah,
Qamar Ramsha,
Zaheen Rahma,
AlAli Abdul Rahman,
Al Nabulsi Ahmad,
AlNashash Hasan
Publication year - 2019
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2018.5121
Subject(s) - accelerometer , computer science , wearable computer , gyroscope , artificial intelligence , naive bayes classifier , computer vision , internet of things , software portability , accidental fall , real time computing , simulation , computer security , embedded system , medicine , support vector machine , engineering , operating system , aerospace engineering , surgery
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.