dc.contributor.author | Khan, Sarah | |
dc.contributor.author | Qamar, Ramsha | |
dc.contributor.author | Zaheen, Rahma | |
dc.contributor.author | Al-Ali, Abdul-Rahman | |
dc.contributor.author | Al Nabulsi, Ahmad | |
dc.contributor.author | Al-Nashash, Hasan | |
dc.date.accessioned | 2020-02-16T06:41:05Z | |
dc.date.available | 2020-02-16T06:41:05Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Khan, S., Qamar, R., Zaheen, R., Al-Ali, A. R., Al Nabulsi, A., & Al-Nashash, H. (2019). Internet of things based multi-sensor patient fall detection system. Healthcare technology letters, 6(5), 132–137. https://doi.org/10.1049/htl.2018.5121 | en_US |
dc.identifier.issn | 2053-3713 | |
dc.identifier.uri | http://hdl.handle.net/11073/16598 | |
dc.description.abstract | 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. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | National Center for Biotechnology Information | en_US |
dc.relation.uri | https://dx.doi.org/10.1049%2Fhtl.2018.5121 | en_US |
dc.subject | Pattern classification | en_US |
dc.subject | Body sensor networks | en_US |
dc.subject | Biomedical equipment | en_US |
dc.subject | Gyroscopes | en_US |
dc.subject | Geriatrics | en_US |
dc.subject | Bayes methods | en_US |
dc.subject | Medical signal processing | en_US |
dc.subject | Microcomputers | en_US |
dc.subject | Accelerometers | en_US |
dc.subject | Patient monitoring | en_US |
dc.subject | Internet of Things | en_US |
dc.subject | Nearest neighbour methods | en_US |
dc.subject | Cost-effective integrated system | en_US |
dc.subject | Credit card-sized single board microcomputer | en_US |
dc.subject | Visual-based classifier | en_US |
dc.subject | Sensor data | en_US |
dc.subject | Naive Bayes' classifiers | en_US |
dc.subject | Internet of things based multisensor patient fall detection system | en_US |
dc.subject | Nonfall motions classification | en_US |
dc.subject | k-nearest neighbour | en_US |
dc.title | Internet of things based multi-sensor patient fall detection system | en_US |
dc.type | Peer-Reviewed | en_US |
dc.type | Article | en_US |
dc.type | Published version | en_US |
dc.identifier.doi | 10.1049%2Fhtl.2018.5121 | |