Contactless Healthcare Monitoring System Performance Analysis of Multiple Devices

Abstract

Wearable sensors are often used to assist with the healthcare of elderly people within their own homes for detecting incidents such as falls. However, wearable sensors cause numerous issues: they are uncomfortable, they are often forgotten, or they simply run out of battery. One alternative to providing accurate fall detection systems is making use of ambient microwave signals to detect movements. WiFi in particular is a technology that is present in many homes and interestingly, the signal transmission is affected when humans move in between the path of transmission. However, the setup needed for using this as an effective identification method is unclear. This paper focuses here on the performance gain in using multiple devices as transmitters and receivers, instead of only one pair in a simple identification task. We report an increased accuracy in identifying a human subject sitting and standing over a pool of machine learning algorithms when the two Raspberry PI pairs of devices were used in comparison to a single pair of devices.