Towards Touch-to-Access Device Authentication Using Induced Body Electric Potentials

This paper presents TouchAuth, a new touch-to-access device authentication approach using induced body electric potentials (iBEPs) caused by the indoor ambient electric field that is mainly emitted from the building's electrical cabling. The design of TouchAuth is based on the electrostatics of iBEP generation and a resulting property, i.e., the iBEPs at two close locations on the same human body are similar, whereas those from different human bodies are distinct. Extensive experiments verify the above property and show that TouchAuth achieves high-profile receiver operating characteristics in implementing the touch-to-access policy. Our experiments also show that a range of possible interfering sources including appliances' electromagnetic emanations and noise injections into the power network do not affect the performance of TouchAuth. A key advantage of TouchAuth is that the iBEP sensing requires a simple analog-to-digital converter only, which is widely available on microcontrollers. Compared with existing approaches including intra-body communication and physiological sensing, TouchAuth is a low-cost, lightweight, and convenient approach for authorized users to access the smart objects found in indoor environments.Comment: 16 pages, accepted to the 25th Annual International Conference on Mobile Computing and Networking (MobiCom 2019), October 21-25, 2019, Los Cabos, Mexic

A Simple Method for Synchronising Multiple IMUs Using the Magnetometer

This paper presents a novel method to synchronise multiple IMU (inertial measurement units) devices using their onboard magnetometers. The method described uses an external electromagnetic pulse to create a known event measured by the magnetometer of multiple IMUs and in turn used to synchronise these devices. The method is applied to 4 IMU devices decreasing their de-synchronisation from 270ms when using only the RTC (real time clock) to 40ms over a 1 hour recording. It is proposed that this can be further improved to approximately 3ms by increasing the magnetometer’s sample frequency from 25Hz to 300Hz

Crocs: Cross-Technology Clock Synchronization for WiFi and ZigBee

Clock synchronization is a key function in embedded wireless systems and networks. This issue is equally important and more challenging in IoT systems nowadays, which often include heterogeneous wireless devices that follow different wireless standards. Conventional solutions to this problem employ gateway-based indirect synchronization, which suffers low accuracy. This paper for the first time studies the problem of cross-technology clock synchronization. Our proposal called Crocs synchronizes WiFi and ZigBee devices by direct cross-technology communication. Crocs decouples the synchronization signal from the transmission of a timestamp. By incorporating a barker-code based beacon for time alignment and cross-technology transmission of timestamps, Crocs achieves robust and accurate synchronization among WiFi and ZigBee devices, with the synchronization error lower than 1 millisecond. We further make attempts to implement different cross-technology communication methods in Crocs and provide insight findings with regard to the achievable accuracy and expected overhead

Open electronics for medical devices: State-of-art and unique advantages

A wide range of medical devices have significant electronic components. Compared to open-source medical software, open (and open-source) electronic hardware has been less published in peer-reviewed literature. In this review, we explore the developments, significance, and advantages of using open platform electronic hardware for medical devices. Open hardware electronics platforms offer not just shorter development times, reduced costs, and customization; they also offer a key potential advantage which current commercial medical devices lack—seamless data sharing for machine learning and artificial intelligence. We explore how various electronic platforms such as microcontrollers, single board computers, field programmable gate arrays, development boards, and integrated circuits have been used by researchers to design medical devices. Researchers interested in designing low cost, customizable, and innovative medical devices can find references to various easily available electronic components as well as design methodologies to integrate those components for a successful design

Low-power Wearable Healthcare Sensors

Advances in technology have produced a range of on-body sensors and smartwatches that can be used to monitor a wearer’s health with the objective to keep the user healthy. However, the real potential of such devices not only lies in monitoring but also in interactive communication with expert-system-based cloud services to offer personalized and real-time healthcare advice that will enable the user to manage their health and, over time, to reduce expensive hospital admissions. To meet this goal, the research challenges for the next generation of wearable healthcare devices include the need to offer a wide range of sensing, computing, communication, and human–computer interaction methods, all within a tiny device with limited resources and electrical power. This Special Issue presents a collection of six papers on a wide range of research developments that highlight the specific challenges in creating the next generation of low-power wearable healthcare sensors

ON THE INTERPLAY BETWEEN BRAIN-COMPUTER INTERFACES AND MACHINE LEARNING ALGORITHMS: A SYSTEMS PERSPECTIVE

Today, computer algorithms use traditional human-computer interfaces (e.g., keyboard, mouse, gestures, etc.), to interact with and extend human capabilities across all knowledge domains, allowing them to make complex decisions underpinned by massive datasets and machine learning. Machine learning has seen remarkable success in the past decade in obtaining deep insights and recognizing unknown patterns in complex data sets, in part by emulating how the brain performs certain computations. As we increase our understanding of the human brain, brain-computer interfaces can benefit from the power of machine learning, both as an underlying model of how the brain performs computations and as a tool for processing high-dimensional brain recordings. The technology (machine learning) has come full circle and is being applied back to understanding the brain and any electric residues of the brain activity over the scalp (EEG). Similarly, domains such as natural language processing, machine translation, and scene understanding remain beyond the scope of true machine learning algorithms and require human participation to be solved. In this work, we investigate the interplay between brain-computer interfaces and machine learning through the lens of end-user usability. Specifically, we propose the systems and algorithms to enable synergistic and user-friendly integration between computers (machine learning) and the human brain (brain-computer interfaces). In this context, we provide our research contributions in two interrelated aspects by, (i) applying machine learning to solve challenges with EEG-based BCIs, and (ii) enabling human-assisted machine learning with EEG-based human input and implicit feedback.Ph.D