Cloud-based fall Detection System with Passive RFID Sensor Tags and Supervised Learning

Falls in older adults are a severe worldwide issue that threatens healthy living with a high risk of fatal accidents. In order to maintain a healthy life for them, it is necessary to detect fall accidents early to rescue them quickly. This paper proposes a fall detection system with passive RFID sensor tags measuring Received Signal Strength and Pressure values. By placing sensors on daily items, the system is expected to be non-invasive to the daily lives of older adults, compared to systems using cameras or wearable sensors. In this paper, we have compared the classification performance of several learning algorithms and have developed a prototype system based on a cloud.