Intra-Body Communications for Nervous System Applications: Current Technologies and Future Directions

The Internet of Medical Things (IoMT) paradigm will enable next generation healthcare by enhancing human abilities, supporting continuous body monitoring and restoring lost physiological functions due to serious impairments. This paper presents intra-body communication solutions that interconnect implantable devices for application to the nervous system, challenging the specific features of the complex intra-body scenario. The presented approaches include both speculative and implementative methods, ranging from neural signal transmission to testbeds, to be applied to specific neural diseases therapies. Also future directions in this research area are considered to overcome the existing technical challenges mainly associated with miniaturization, power supply, and multi-scale communications.Comment: https://www.sciencedirect.com/science/article/pii/S138912862300163

Human Body–Electrode Interfaces for Wide-Frequency Sensing and Communication: A Review

Several on-body sensing and communication applications use electrodes in contact with the human body. Body–electrode interfaces in these cases act as a transducer, converting ionic current in the body to electronic current in the sensing and communication circuits and vice versa. An ideal body–electrode interface should have the characteristics of an electrical short, i.e., the transfer of ionic currents and electronic currents across the interface should happen without any hindrance. However, practical body–electrode interfaces often have definite impedances and potentials that hinder the free flow of currents, affecting the application’s performance. Minimizing the impact of body–electrode interfaces on the application’s performance requires one to understand the physics of such interfaces, how it distorts the signals passing through it, and how the interface-induced signal degradations affect the applications. Our work deals with reviewing these elements in the context of biopotential sensing and human body communication

Revealing the Secrets of Radio-Enabled Embedded Systems: on extraction of raw information from any on-board signal through RF

In this work we are interested in evaluating the possibility of extracting information from radio-enabled embedded-systems from a long distance. That is, our focus is capturing information from sources in the micrometer to tens of centimeters scale, such as intra- or inter- device busses, board-level routing traces etc. Moreover, we focus on distances in the range of millimeters to tens of centimeters from the (on-chip or on-board) embedded-system Tx Antenna to the signal source. Side-channels denotes presence of information in illegitimate channels. Side-channel analysis (SCA) attacks typically require statistical analysis and many leakage traces, focusing on micrometer level signals (sources) which emanate direct Near-Field information up to centimeters-level distances. In the same context (Near-Field and micrometer-level) simple power analysis (SPA) like attacks typically extract either direct raw information from one or few leakages or utilize statistical analysis on various samples from the same trace, similarly to horizontal attacks. Lately, radio-enabled systems were shown to emanate to a large distance (Far-Field), information from micrometer level sources, such as CPU processing, through the RF Tx Antenna: so far, SCA-like statistical analysis were shown. On the other hand, various reports exist on direct information eavesdropping/ sniffing or data exfiltration, emanated from centimeter to tens of centimeters scale sources, e.g., SATA, USB, Power-lines, Serial interface, Air-Gap systems, Screens and even optical fibers. All these elements are typically being used as a source and a direct Tx Antenna (huge, several to tens of centimeters) of the sensitive information. These antennas typically transmit information to short distances and the decay is very steep (proportional to $r^{-2}$-$r^{-3}$ depending on various factors and models). To the best of our knowledge, we report here for the first time an alarming security challenge: any signal in the embedded system, from serial ports, DMA-controlled memory-access, JTAG and SPI interfaces, on-board signals with galvanic connection to the Tx Antenna-chip and \emph{on-board signals without galvanic connection to the Tx Antenna-chip itself, all leak direct information up to tens of centimeters from source to the Tx Antenna}. This alarming situation induce signal-integrity implications within the embedded system, and significant implications relating to device-isolation and user-isolation, it may also affect standards and specifications for e.g., electromagnetic compatibility (EMC), on-board signal shielding, electromagnetic and RF interference (EMI, RFI), cross-talk, and generally design-for-manufacturing (DFM) guidelines for both intra-IC and PCB board. We demonstrate such direct readout of signals with commercial and low-cost equipment indicating how problematic the situation is. The existence of such leakage is demonstrated both over an ultra-low-cost platform such as the nRF52832(nRF) embedded-system and on a more advanced ESP32-c3-devkitc-02 board which is far more widespread in ISM radio applications and meets certification like FCC and CE (as compared to the nRF device). We have constructed an experiment to demonstrate leakage scenarios from (1) on- and (2) off-chip, on-board or (3) signals without galvanic connection to the RF front-end chip, showing the severity of the leakage, repetitively and systematic nature of the phenomena over various devices. We further demonstrate how sophisticated adversaries can build a code-injection Gadget which can carry sensitive-data and modulate it to be best extracted by the RF-channel. The main observation we push forward is that unless concrete interference and isolation standards appear with security metrics in mind, which are significantly different than ones needed for communication, it would be hard to prevent such leakages

On the Recognition of Emotion from Physiological Data

This work encompasses several objectives, but is primarily concerned with an experiment where 33 participants were shown 32 slides in order to create ‗weakly induced emotions‘. Recordings of the participants‘ physiological state were taken as well as a self report of their emotional state. We then used an assortment of classifiers to predict emotional state from the recorded physiological signals, a process known as Physiological Pattern Recognition (PPR). We investigated techniques for recording, processing and extracting features from six different physiological signals: Electrocardiogram (ECG), Blood Volume Pulse (BVP), Galvanic Skin Response (GSR), Electromyography (EMG), for the corrugator muscle, skin temperature for the finger and respiratory rate. Improvements to the state of PPR emotion detection were made by allowing for 9 different weakly induced emotional states to be detected at nearly 65% accuracy. This is an improvement in the number of states readily detectable. The work presents many investigations into numerical feature extraction from physiological signals and has a chapter dedicated to collating and trialing facial electromyography techniques. There is also a hardware device we created to collect participant self reported emotional states which showed several improvements to experimental procedure