This content will become publicly available on February 20, 2023
- Award ID(s):
- Publication Date:
- NSF-PAR ID:
- Journal Name:
- 2022 IEEE International Solid- State Circuits Conference (ISSCC)
- Sponsoring Org:
- National Science Foundation
More Like this
A 65nm Resonant Electro-Quasistatic 5-240uW Human Whole-Body Powering and 2.19uW Communication SoC with Automatic Maximum Resonant Power TrackingApplications like Connected Healthcare through physiological signal monitoring and Secure Authentication using wearable keys can benefit greatly from battery-less operation. Low power communication along with energy harvesting is critical to sustain such perpetual battery-less operation. Previous studies have used techniques such as Tribo-Electric, Piezo-Electric, RF energy harvesting for Body Area Network devices, but they are restricted to on-body node placements. Human body channel is known to be a promising alternative to wireless radio wave communication for low power operation [1-4], through Human Body Communication, as well as very recently as a medium for power transfer through body coupled power transfer . However, channel length (L) dependency of the received power makes it inefficient for L>40cm. There have also been a few studies for low power communication through the human body, but none of them could provide sustainable battery-less operation. In this paper, we utilize Resonant Electro Quasi-Static Human Body Communication (Res-EQS HBC) with Maximum Resonance Power Tracking (MRPT) to enable channel length independent whole-body communication and powering (Fig.1). We design the first system to simultaneously transfer Power and Data between a HUB device and a wearable through the human body to enable battery-less operation. Measurement results show 240uW, 28uW andmore »
Radiative communication using electro-magnetic (EM) fields amongst the wearable and implantable devices act as the backbone for information exchange around a human body, thereby enabling prime applications in the fields of connected healthcare, electroceuticals, neuroscience, augmented and virtual reality. However, owing to such radiative nature of the traditional wireless communication, EM signals propagate in all directions, inadvertently allowing an eavesdropper to intercept the information. In this context, the human body, primarily due to its high water content, has emerged as a medium for low-loss transmission, termed human body communication (HBC), enabling energy-efficient means for wearable communication. However, conventional HBC implementations suffer from significant radiation which also compromises security. In this article, we present Electro-Quasistatic Human Body Communication (EQS-HBC), a method for localizing signals within the body using low-frequency carrier-less (broadband) transmission, thereby making it extremely difficult for a nearby eavesdropper to intercept critical private data, thus producing a covert communication channel, i.e. the human body. This work, for the first time, demonstrates and analyzes the improvement in private space enabled by EQS-HBC. Detailed experiments, supported by theoretical modeling and analysis, reveal that the quasi-static (QS) leakage due to the on-body EQS-HBC transmitter-human body interface is detectable up to <0.15
A 415 nW Physically and Mathematically Secure Electro-Quasistatic HBC Node in 65nm CMOS for Authentication and Medical ApplicationsApplications such as secure authentication, remote health monitoring require secure, low power communication between devices around the body. Radio wave communication protocols, such as Bluetooth, suffer from the problem of signal leakage and high power requirement. Electro Quasistatic Human Body Communication (EQS-UBC) is the ideal alternative as it confines the signal within the body and also operates at order of magnitude lower power. In this paper, we design a secure HBC SoC node, which uses EQS-UBC for physical security and an AES-256 core for mathematical security. The SoC consumes 415nW power with an active power of 108nW for a data rate of 1kbps, sufficient for authentication and remote monitoring applications. This translates to 100x improvement in power consumption compared to state-of-the-art HBC implementations while providing physical security for the first time.
BodyWire-HCI: Enabling New Interaction Modalities by Communicating Strictly During Touch Using Electro-Quasistatic Human Body CommunicationCommunication during touch provides a seamless and natural way of interaction between humans and ambient intelligence. Current techniques that couple wireless transmission with touch detection suffer from the problem of selectivity and security, i.e., they cannot ensure communication only through direct touch and not through close proximity. We present BodyWire-HCI , which utilizes the human body as a wire-like communication channel, to enable human–computer interaction, that for the first time, demonstrates selective and physically secure communication strictly during touch. The signal leakage out of the body is minimized by utilizing a novel, low frequency Electro-QuasiStatic Human Body Communication (EQS-HBC) technique that enables interaction strictly when there is a conductive communication path between the transmitter and receiver through the human body. Design techniques such as capacitive termination and voltage mode operation are used to minimize the human body channel loss to operate at low frequencies and enable EQS-HBC. The demonstrations highlight the impact of BodyWire-HCI in enabling new human–machine interaction modalities for variety of application scenarios such as secure authentication (e.g., opening a door and pairing a smart device) and information exchange (e.g., payment, image, medical data, and personal profile transfer) through touch (https://www.youtube.com/watch?v=Uwrig2XQIH8).
Obeid, I. (Ed.)The Neural Engineering Data Consortium (NEDC) is developing the Temple University Digital Pathology Corpus (TUDP), an open source database of high-resolution images from scanned pathology samples , as part of its National Science Foundation-funded Major Research Instrumentation grant titled “MRI: High Performance Digital Pathology Using Big Data and Machine Learning” . The long-term goal of this project is to release one million images. We have currently scanned over 100,000 images and are in the process of annotating breast tissue data for our first official corpus release, v1.0.0. This release contains 3,505 annotated images of breast tissue including 74 patients with cancerous diagnoses (out of a total of 296 patients). In this poster, we will present an analysis of this corpus and discuss the challenges we have faced in efficiently producing high quality annotations of breast tissue. It is well known that state of the art algorithms in machine learning require vast amounts of data. Fields such as speech recognition , image recognition  and text processing  are able to deliver impressive performance with complex deep learning models because they have developed large corpora to support training of extremely high-dimensional models (e.g., billions of parameters). Other fields that do notmore »