Energy Efficient Communication among Wearable Devices using Optimized Motion Detection

Abstract

When a person is performing daily activities (e.g. walking) in the context of a WBAN application, the channel quality between the worn sensor devices and the hub can vary due to the switching of Line-Of-Sight (LOS) and None-Line-Of-Sight (NLOS) statuses among the sender and the receiver. Therefore, motion aware wireless MAC protocols are designed in order to enhance communication reliability and to avoid wasting energy on unnecessary wireless re-transmissions during most of the NLOS communications. Despite its importance, the prerequisite step of accurate and energy efficient motion detection remains as an assumption in most of the existing motion aware WBAN protocols. Hence in this paper, we propose an optimized real-time gesture detection method and implement it in a motion aware WBAN communication protocol. Wireless communications only take place when the channel condition is good, while data is buffered otherwise. It is lightweight and tailored to perform fast with accurate detection that suits embedded devices with limited memory size and relatively low MCU processing speeds. Experiments are conducted using both simulation and real-life hardware devices with 6 volunteers. The proposed motion detection method showed 99.28% off-line accuracy and 92.5% online accuracy, respectively. Thanks to which, the communication results yielded a promising 82% improvement in packet drop reduction and 32.8% improvement in energy efficiency compared to conventional methods.