An IoT-Based Bed Fall Prediction System Using Force Sensitive Resistor

Abstract

Patients with impaired mobility and neurological disorders such as Alzheimer's disease, Parkinson's disease, dementia etc. are vulnerable to bed falls, which can be damaging to their physical and psychological well-being. Existing systems are mostly fall detection based on wearable devices, which can be uncomfortable to wear or ambient devices such as cameras that invade privacy. A bed falls prediction system using force sensitive resistors (FSR) has been proposed in this paper. It is designed to eliminate privacy intrusion and discomfort issues. The system can identify the patient's different on-bed positions and determine the possibility of bed falls. In case of any risky position, the caretaker will be alerted to mobile applications via the Internet of Things (IoT), making patient monitoring more accessible and manageable. This integrated system yields an average of 92% accuracy for 5 different on-bed positions. The bed fall prediction system will facilitate caretakers/nurses to take care conveniently at homes, hospitals and assisted care facilities to ensure patients' health and safety.