Internet of things based multi-sensor patient fall detection system

Healthcare Technology Letters

Sarah Khan¹  Ahmad Al Nabulsi¹  Rahma Zaheen¹  Hasan Al-Nashash¹  Ramsha Qamar¹  Abdul Rahman Al-Ali¹ 

[1] American University of Sharjah;

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

DOI  :  10.1049/htl.2018.5121

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown