Background To meet the needs of aging and dementia patients in Taiwan, this study designed a nursing system that includes communication, location tracking, and fall detection, and early warning services. The main purpose of this research is to provide timely services to the elderly and patients and hope to reduce the burden when the number of nursing staff decreases. This article is a remote disease care service platform with the Internet of Things (IoT) devices to monitor the location of the elderly and whether they have dropped warning alerts. Results The device is connected to the patient's waist and chest, monitors the patient's movement and behavior, and transmits messages to the back-end system, and informs caregivers through mobile phone applications when unexpected or shocking events occur. The system can identify whether the patient has fallen, accidentally, or long-term inactivity. The device is equipped with sensors that enable it to monitor the patient's location and behavior data through Bluetooth and GPS technology. Finally, we proposed a basic model and an integrated model that will industrialize the system and is expected to play a role in a larger patient population. Conclusions The system developed in this research has passed the Activities of Daily Living (ADL) test and verification, and is expected to provide appropriate safety care services for nursing homes and elderly residences.
Due to the influence of degeneration and chronic diseases of elderly people, a higher chance of fall-related injuries occurs among them. Falling is one of the accidents frequently confronted by elderly people, so this issue is worthy of concern. We propose diverse models to analyze falls through a wearable device. Then, we use Artificial Intelligence of Things (AIoT) biomedical sensors for fall detection to build a system for monitoring elderly people’s falls caused by dementia. The system can meet the safety needs of elderly people by providing communication, position tracking, fall detection, and pre-warning services. This device can be worn on the waist of an elderly people. Moreover, the device can monitor whether or not the person is walking normally, transmit the information to the rear-end system, and inform his/her family member via a cellphone app while an accident is occurring. Considering the risks on the fall test of elderly people, this study adopts activities of daily living (ADL) to verify the test. According to the test results, the accuracy of fall detection is 93.7%, the false positive rate is 6.2%, and the false negative rate is 6.5%. To improve the accuracy of fall detection and the timely handling of appropriate referrals, may be highly expected to reduce the occurrence of fall-related injuries. JEL classification numbers: D61, I30, O32. Keywords: Fall Detection, AIoT Sensor, Elderly People.
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