The rise of the Internet of Medical Things (IoMT) in healthcare brings benefits like continuous monitoring, remote patient care, and data-driven treatments. However, it also poses cybersecurity risks. While prior research has investigated this issue, it has not looked at advanced wearable sensor nodes that use combination of Bluetooth Low Energy (BLE) with other wireless protocols. In this paper we conduct a blackbox audit of wearable sensor nodes for exploring vulnerabilities associated with them. We use a systematic auditing approach to (1) investigate whether security attacks are effective against wearable sensor nodes, (2) group the vulnerabilities based on susceptibility to certain types of attacks, and (3) provide an in-depth gap analysis of the devices’ security behaviour. We develop and release an approach for semi-automated wearable sensor nodes experimentation to reveal their response to common security threats. We perform hundreds of experiments using popular commercial wearable sensor nodes when deployed in an IoMT testbed. Our results indicate not only that these devices are vulnerable to common security attacks, but also their critical security gaps jeopardize patient safety and data integrity.

This paper presents a system-on-chip (SoC) architecture for an active implantable microsystem that combines electrical recording with optogenetic stimulation for closed-loop neuromodulation. The SoC is designed to support a 4-shank optrode (opto-electrode) fork with 8 differential recording channels (0.1-5000Hz bandwidth, 10mVpp range, 12-bit resolution) to observe neural signals on electrodes and 32 driver circuits (2mA range, 6-bit current resolution with μs timing resolution) for microLED optical stimulation. Each SoC additionally integrates diagnostic instrumentation to measure electrical resistance across any of its I/O lines. The SoC features a custom 4-wire interface that provides power and data communication across multiple chips using a shared bus allowing for multiple forks to be stacked to form two dimensional optrode arrays. Each chip has an independent controller that receives, interprets and executes commands, and can transmit neural data while simultaneously controlling LED outputs. The circuit is implemented in a 180nm CMOS process, with each chip occupying a 5mm×2.45mm silicon footprint, designed specifically to mount on the base of the silicon optrode fork.

We present the first classifier based on a spiking neural network (SNN) that can decode individual finger movements from electrocorticography (ECoG) signals. The SNN has only six leaky integrate-and-fire neurons and uses carefully selected features: the local motor potential and the high gamma band power to analyse a publicly available ECoG dataset. Through the investigation of the key aspects affecting SNN performance (epoch length, lag time, number of concatenated epochs), the presented decoder achieves a 72.4% average classification accuracy across three subjects with an average training time of 3.55 s and a latency of only 1 ms. This work demonstrates how a simple SNN architecture can effectively decode complex motor intentions from ECoG signals, potentially enabling more efficient brain-computer interfaces.

A method for the detection of failure in gold-plated microelectrodes is presented. Three main failure mechanisms–layer detachment, biofouling and halide-mediated pas-sivation (HMP)–were induced on the microelectrodes, leading to electrode failure. Sequential admittance and sensitivity measurements were carried out on gold-plated microelectrodes on printed circuit boards (PCBs) and CMOS chips to develop and train the classifier. A two-step classifier is proposed that relies only on measuring the admittance magnitude (|Y|) at three specific frequencies within 0.6-800 Hz. The classifier achieves 88% accuracy on PCB microelectrodes and 92% accuracy on CMOS microelectrodes.

This work describes the implementation of SPACEMan, a wireless electrochemical system with concurrent potentiometric and amperometric sensing that can be utilised for saliva, sweat or point of care diagnostics. This system is designed with the vision of simpler interfaces for biofluid analysis. With a complete system-on-chip including electrochemical sensing, power management and data transmission, conventional interfaces like wirebonds will no longer be required in post-processing steps. The proposed architecture consists of a sensor front-end with four electrodes for concurrent amperometric and potentiometric sensing. This front-end outputs square wave signals mixed together with varying frequencies dependent on the sensed input, with the output type switchable with a state machine. A power management system consisting of a low dropout regulator (LDO) band gap reference (BGR), and a rectifier bridge is utilised for supplying power from an inductive link at 433MHz. Sensor data is transmitted wirelessly to a base station using LSK (Load-Shift Keying). The sensor front-end consumes 18μW, which the power management system more than adequately provides. The core area of the electronics without the coil is a conservative size of 0.41mm2.

DAPPER is a front end system capable of simultaneous amperometric and potentiometric sensing proposed for low-power multi-parameter analysis of bio-fluids such as saliva. The system consists of two oscillator circuits, generating a frequency relative to their sensed current and voltage signals. These signals are then mixed together to produce a single channel output that can be transmitted through backscattering (load-shift keying). The entire system consumes 40μW from a 1.4V supply. The linear ranges of potentiometry and amperometry circuits are 0.4V - 1V and 250pA - 5.6μA (87dB), and their input referred noise is 1.7μV and 44.6fA, respectively.

This paper presents a 64-bit on-chip identification system featuring low power consumption and randomness compensation for multi-node bio-implantable devices. A sense amplifier based bit-cell is proposed to realize the silicon physical unclonable function, providing a unique value whose probability has a uniform distribution and minimized influence from the temperature and supply variation. The entire system is designed and implemented in a typical 0.35 μm CMOS technology, including an array of 64 bit-cells, readout circuits, and digital controllers for data interfaces. Simulated results show that the proposed bit-cell design achieved a uniformity of 50.24% and a uniqueness of 50.03% for generated IDs. The system achieved an energy consumption of 6.0 pJ per bit with parallel outputs and 17.3 pJ per bit with serial outputs.

A fully-integrated differential biosensing platform on CMOS is presented for miniaturized enzyme-based electrochemical sensing. It enables sensor background current elimination and consists of a differential sensor array and a differential readout IC (DiRIC). The sensor array includes a four-electrode sensor for amperometric electrochemical sensing, as well as a differential ISFET-based pH sensor to calibrate the biosensors. The ISFET is biased in weak inversion and co-designed with DiRIC to enable pH measurements from 1 to 14 with resolution of 0.1 pH. DiRIC enables differential current measurement in the range of ±100 μA with more than 120dB dynamic range.

A full remotely powered system for metabolism monitoring of free-moving mice is presented here. The fully implantable sensing platform hosts two ASICs, one off-the-shelf micro-controller, four biosensors, two other sensors, a coil to receive power, and an antenna to transmit data. Proper enzymes ensure specificity for animal metabolites while Multi-Walled Carbon Nanotubes ensure the due sensitivity. The remote powering is indeed provided by inductive coils located under the floor of the mouse' cage. Two different approaches where investigated to ensure freedom of movement to the animal. The application to studies for personalized medicine is demonstrated by showing continuous monitoring of both glucose and paracetamol.

A power delivery system for implantable biosensors is presented. The system, embedded into a skin patch and located directly over the implantation area, is able to transfer up to 15 mW wirelessly through the body tissues by means of an inductive link. The inductive link is also used to achieve bidirectional data communication with the implanted device. Downlink communication (ASK) is performed at 100 kbps; uplink communication (LSK) is performed at 66.6 kbps. The received power is managed by an integrated system including a voltage rectifier, an amplitude demodulator and a load modulator. The power management system is presented and evaluated by means of simulations.

The objective of this work is the systematic study of the use of electrochemical readout for advanced diagnosis and drug monitoring. Whereas to date various electrochemical principles have been studied and successfully tested, they typically operate on a single target molecule and are not integrated in a full data analysis chain. The present work aims to view various sensing approaches and explore the design space for integrated realization of multi-target sensors and sensor arrays.