Accurate Human Tissue Characterization for Energy-Efficient Wireless On-Body Communications

Abstract

The demand for Wireless Body Sensor Networks (WBSNs) is rapidly increasing due to the revolution in wearable systems demonstrated by the penetration of on-the-body sensors in hospitals, sports medicine and general health-care practices. In WBSN, the body acts as a communication channel for the propagation of electromagnetic (EM) waves, where losses are mainly due to absorption of power in the tissue. This paper shows the effects of the dielectric properties of biological tissues in the signal strength and, for the first time, relates these effects with the human body composition. After a careful analysis of results, this work proposes a reactive algorithm for power transmission to alleviate the effect of body movement and body type. This policy achieves up to 40.8% energy savings in a realistic scenario with no performance overhead.

A wireless body sensor network based on dynamic power control and opportunistic packet scheduling mechanisms

“…The mechanisms presented in [19] and [22] are examples of the second type of TPC described above. Vallejo et al introduce a TPC which uses a sensor, accelerometer, to determine the node's position relative to the receiver frame [22].…”

“…Vallejo et al introduce a TPC which uses a sensor, accelerometer, to determine the node's position relative to the receiver frame [22]. Through a previous channel characterization, the mechanism is able to predict the channel's conditions.…”

A Link Quality Estimator for Power-Efficient Communication Over On-Body Channels

“…With this experimental profiling, we are interested in the research about the real impact of body movements, body shape and body composition as input parameters in the RSSI model. In this paper, we follow a similar experimental methodology to [11], but we extend the sample group to a larger population of 37 people with different physical characteristics, what allows to extract several interesting conclusions.…”

“…We remind that the A-LQE models only need as input variables an initial transmission power value, the body position detected by accelerometry with low-complexity and low overhead, and some constants of body parameters (Body Fat Total and Circumference Arm for the Link 1 and Leg Lower Length for the Link 2). The TPC block adjusts the transmission power to the minimum value to assure that the RSSI value does not drop below a threshold (which has been fixed to -85 dBm as proposed in [11]). P T xnew ← −5 10: elseRSSI ≤ T min 11:…”

Deep Learning and IoT to Assist Multimorbidity Home Based Healthcare

“…More recently, using the body tissue as transmission medium is being proposed. From the foundational thesis by Wegmüller [9] to the more recent works like [10,11].…”