Adaptive Body Area Networks Using Kinematics and Biosignals

TL;DR

This paper introduces an adaptive WBAN scheme that leverages body kinematics and biosignals to optimize network reconfiguration, significantly improving packet delivery and reducing power consumption in wearable health monitoring devices.

Contribution

It presents a novel adaptive WBAN approach that predicts channel conditions using biosignals and kinematics, enabling real-time network reconfiguration with low overhead.

Findings

Up to 41% improvement in packet delivery ratio (PDR)

Up to 27% reduction in power consumption

20% PDR increase with EMG-based power control

Abstract

The increasing penetration of wearable and implantable devices necessitates energy-efficient and robust ways of connecting them to each other and to the cloud. However, the wireless channel around the human body poses unique challenges such as a high and variable path-loss caused by frequent changes in the relative node positions as well as the surrounding environment. An adaptive wireless body area network (WBAN) scheme is presented that reconfigures the network by learning from body kinematics and biosignals. It has very low overhead since these signals are already captured by the WBAN sensor nodes to support their basic functionality. Periodic channel fluctuations in activities like walking can be exploited by reusing accelerometer data and scheduling packet transmissions at optimal times. Network states can be predicted based on changes in observed biosignals to reconfigure the network parameters in real time. A realistic body channel emulator that evaluates the path-loss for everyday human activities was developed to assess the efficacy of the proposed techniques. Simulation results show up to 41% improvement in packet delivery ratio (PDR) and up to 27% reduction in power consumption by intelligent scheduling at lower transmission power levels. Moreover, experimental results on a custom test-bed demonstrate an average PDR increase of 20% and 18% when using our adaptive EMG- and heart-rate-based transmission power control methods, respectively. The channel emulator and simulation code is made publicly available at https://github.com/a-moin/wban-pathloss.