Khan, SarahQamar, RamshaZaheen, RahmaAl-Ali, Abdul-RahmanAl Nabulsi, AhmadAl-Nashash, Hasan2020-02-162020-02-162019Khan, 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.2018.51212053-3713http://hdl.handle.net/11073/16598Accidental 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-USPattern classificationBody sensor networksBiomedical equipmentGyroscopesGeriatricsBayes methodsMedical signal processingMicrocomputersAccelerometersPatient monitoringInternet of ThingsNearest neighbour methodsCost-effective integrated systemCredit card-sized single board microcomputerVisual-based classifierSensor dataNaive Bayes' classifiersInternet of things based multisensor patient fall detection systemNonfall motions classificationk-nearest neighbourPeer-Reviewed10.1049%2Fhtl.2018.5121