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Abstract Continuous multi-channel monitoring of biopotential signals is vital in understanding the body as a whole, facilitating accurate models and predictions in neural research. The current state of the art in wireless technologies for untethered biopotential recordings rely on radiative electromagnetic (EM) fields. In such transmissions, only a small fraction of this energy is received since the EM fields are widely radiated resulting in lossy inefficient systems. Using the body as a communication medium (similar to a ’wire’) allows for the containment of the energy within the body, yielding order(s) of magnitude lower energy than radiative EM communication. In this work, we introduce Animal Body Communication (ABC), which utilizes the concept of using the body as a medium into the domain of untethered animal biopotential recording. This work, for the first time, develops the theory and models for animal body communication circuitry and channel loss. Using this theoretical model, a sub-inch$$^3$$ ${}^3$[1″ × 1″ × 0.4″], custom-designed sensor node is built using off the shelf components which is capable of sensing and transmitting biopotential signals, through the body of the rat at significantly lower powers compared to traditional wireless transmissions. In-vivo experimental analysis proves that ABC successfully transmits acquired electrocardiogram (EKG) signals through the body with correlation$$>99\%$$ $>99\%$when compared to traditional wireless communication modalities, with a 50$$\times$$ $\times$reduction in power consumption. 

This work presents an interference-adaptive Gallium Nitride (GaN) low-noise amplifier (LNA) front-end with orthogonal frequency and linearity tuning for applications in communication base stations, radar and electronic warfare (EW). The system operates between 2–6 GHz and provides a sub 5 ms tuning time for an input power tuning range of 40 dB. The orthogonal tuning consists of two phases: 1. frequency tuning with four tunable bandpass and bandstop filters for interference rejection, 2. linearity tuning with a combination of coarse tuning through look-up table (LUT) and fine-tuning through incremental adaptation to trade off power with linearity. GaN LNA’s linearity can be adjusted between P textsubscript 1dB,IN = -10 and 1.5 dBm with output P textsubscript 1dB up to 25 dBm (11.5 dB range) with the LNA power changing from 500 mW to 2 W (x4 increase). The average LNA power with orthogonal frequency and linearity tuning decreases by 56% as compared with the system operating at the worst-case no tuning condition. Two systems involving commercial filters and custom cavity resonator-based filters were constructed. The filters further increase the system P textsubscript 1dB,IN by the filter rejection of the interference signal. The rest of the controls consume about 10% of the worst-case condition LNA power. 

While the number of wearables is steadily growing, the wearables/person wearing them faces a limitation due to the need for charging all of them every day. To unlock the true power of IoB, we need to make these IoB nodes perpetual. However, that is not possible with today’s technology. In this paper, we will debate, whether with the advent of Wi-R protocol that uses the body to communicate at 100X lower energy that BTLE/Wi-Fi, is it going to be possible to enable the long-standing desire of perpetual sensing/actuation nodes for the Internet of Bodies. 

Wearable devices typically use electromagnetic fields for wireless information exchange. For implanted devices, electromagnetic signals suffer from a high amount of absorption in tissue, and alternative modes of transmission (ultrasound, optical and magneto-electric) cause large transduction losses due to energy conversion. To mitigate this challenge, we report biphasic quasistatic brain communication for wireless neural implants. The approach is based on electro-quasistatic signalling that avoids transduction losses and leads to an end-to-end channel loss of only around 60 dB at a distance of 55 mm. It utilizes dipole-coupling-based signal transfer through the brain tissue via differential excitation in the transmitter (implant) and differential signal pickup at the receiver (external hub). It also employs a series capacitor before the signal electrode to block d.c. current flow through the tissue and maintain ion balance. Since the electrical signal transfer through the brain is electro-quasistatic up to the several tens of megahertz, it provides a scalable (up to 10 Mbps), low-loss and energy-efficient uplink from the implant to an external wearable. The transmit power consumption is only 0.52 μW at 1 Mbps (with 1% duty cycling)—within the range of possible energy harvesting in the downlink from a wearable hub to an implant. 

Over six decades of semiconductor technology scaling (Moore's Law) and subsequently system size scaling (Bell's Law) has reduced the size of unit computing to virtually zero. This has led to computing becoming ubiquitous in everything around us, making everyday things smart. Similarly, tremendous progress in communication capacity (Shannon's theorem) has made these smart things connected to the internet and forming the Internet of Things (IoT). Many of these smart, connected devices are present in, on, or around the human body. This subset of IoT around the human body has a distinguishing feature, that it has a common medium, i.e. the body itself. This subset is increasingly becoming popular as the Internet of Bodies (IoB). In this paper, we look into the need and growth of IoB devices, including the technological landscape, current challenges and the future that IoB will enable for empowering humans. 

Energy-efficient sensing with physically secure communication for biosensors on, around, and within the human body is a major area of research for the development of low-cost health care devices, enabling continuous monitoring and/or secure perpetual operation. When used as a network of nodes, these devices form the Internet of Bodies, which poses challenges including stringent resource constraints, simultaneous sensing and communication, and security vulnerabilities. Another major challenge is to find an efficient on-body energy-harvesting method to support the sensing, communication, and security submodules. Due to limitations in the amount of energy harvested, we require a reduction in energy consumed per unit information, making the use of in-sensor analytics and processing imperative. In this article, we review the challenges and opportunities of low-power sensing, processing, and communication with possible powering modalities for future biosensor nodes. Specifically, we analyze, compare, and contrast ( a) different sensing mechanisms such as voltage/current domain versus time domain, ( b) low-power, secure communication modalities including wireless techniques and human body communication, and ( c) different powering techniques for wearable devices and implants. 

Recent advancements in low power and low noise front-end amplifiers have made it possible to support high-speed data transmission within the deep roll-off regions of conventional wireline channels. Despite being primarily limited by inter-symbol-interference (ISI), these legacy channels also require power-consuming front-end amplifiers due to increased insertion-loss at high frequencies. Wireline-like broadband channels, such as proximity communication and human-body-communication (HBC), as well as multi-lane, densely-packed channels, are further constrained by their high loss and unique channel responses which cause the received signal to be noise-limited. To address these challenges, this paper proposes the use of a discrete-time integrating amplifier as a low power <1 pJ/b using 65nm CMOS up to 5-6 Gb/s) alternative to traditional continuous-time front-end amplifiers. Integrating amplifiers also reduce the effects of noise due to its inherent current integrating process. The paper provides a detailed mathematical analysis of gain of two conventional and three novel and improved integrating amplifiers, accurate input referred noise estimations, signal-to-noise ratio, and a comparison of the integrating amplifier’s performance with that of a low-noise amplifier. The analysis identifies the most optimum integrator architecture and provides comparison with simulated results. This paper also develops theoretical expressions and provides in-depth understanding of input referred noise, while supporting them by simulations using 65nm CMOS technology node. Finally, a comparative analysis between low-noise amplifier and discrete-time integrating amplifier is presented to demonstrate power and noise benefits for both legacy and wireline-like channels, while providing an easier design space as integrator provides two-dimensional controllability for gain.