Literature DB >> 27810839

Anatomical Region-Specific In Vivo Wireless Communication Channel Characterization.

Ali Fatih Demir, Qammer H Abbasi, Z Esat Ankarali, Akram Alomainy, Khalid Qaraqe, Erchin Serpedin, Huseyin Arslan.   

Abstract

In vivo wireless body area networks and their associated technologies are shaping the future of healthcare by providing continuous health monitoring and noninvasive surgical capabilities, in addition to remote diagnostic and treatment of diseases. To fully exploit the potential of such devices, it is necessary to characterize the communication channel, which will help to build reliable and high-performance communication systems. This paper presents an in vivo wireless communication channel characterization for male torso both numerically and experimentally (on a human cadaver) considering various organs at 915 MHz and 2.4 GHz. A statistical path loss (PL) model is introduced, and the anatomical region-specific parameters are provided. It is found that the mean PL in decibel scale exhibits a linear decaying characteristic rather than an exponential decaying profile inside the body, and the power decay rate is approximately twice at 2.4 GHz as compared to 915 MHz. Moreover, the variance of shadowing increases significantly as the in vivo antenna is placed deeper inside the body since the main scatterers are present in the vicinity of the antenna. Multipath propagation characteristics are also investigated to facilitate proper waveform designs in the future wireless healthcare systems, and a root-mean-square delay spread of 2.76 ns is observed at 5 cm depth. Results show that the in vivo channel exhibit different characteristics than the classical communication channels, and location dependence is very critical for accurate, reliable, and energy-efficient link budget calculations.

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Year:  2016        PMID: 27810839     DOI: 10.1109/JBHI.2016.2618890

Source DB:  PubMed          Journal:  IEEE J Biomed Health Inform        ISSN: 2168-2194            Impact factor:   5.772


  2 in total

1.  An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare.

Authors:  William Taylor; Syed Aziz Shah; Kia Dashtipour; Adnan Zahid; Qammer H Abbasi; Muhammad Ali Imran
Journal:  Sensors (Basel)       Date:  2020-05-06       Impact factor: 3.576

2.  Ultra-Low Power Wearable Infant Sleep Position Sensor.

Authors:  Inyeol Yun; Jinpyeo Jeung; Mijung Kim; Young-Seok Kim; Yoonyoung Chung
Journal:  Sensors (Basel)       Date:  2019-12-20       Impact factor: 3.576

  2 in total

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