This paper presents a fully-CMOS, tunable impulse radio ultra-wideband (IR-UWB) transmitter for high-density implantable neural recording systems. Fabricated in 28 nm CMOS technology, the transmitter features an ultra-compact edge-combining architecture based on a digitally tunable impulse response filter (IRF) occupying only$0.0027~ {\mathrm {\text {m}\text {m} ^{2}}}$. The measured output exhibits a central frequency of$4.5~ {\mathrm {\text {G}\text {Hz} }}$with a 10-dB bandwidth of$3.6~ {\mathrm {\text {G}\text {Hz} }}$, providing robust spectral performance. It achieves$1~ {\mathrm {\text {G}\text {Hz} }}$frequency tunability by adjusting pulse widths from$650~ {\mathrm {\text {p}\text {s} }}$to$800~ {\mathrm {\text {p}\text {s} }}$, ensuring compliance with FCC spectral masks. A current-starved ring VCO (CSRVCO), with a compact$150~ {\mathrm {\mu \text {m} ^{2}}}$layout, serves as the clock source, offering wide frequency tuning, low power consumption, and enhanced output power. The transmitter employs On-Off Keying (OOK) modulation and delivers a peak output amplitude of$660~ {\mathrm {\text {m}\text {V} }}$with up to 5.1% energy efficiency. At a$20~ {\mathrm {\text {M}\text {Hz} }}$pulse repetition rate, the transmitter consumes only$78~ {\mathrm {\mu \text {W} }}$while producing -24.5dBm of output power. Measurement results confirm compliance with FCC regulations and suitability for deep implantation in biomedical applications. The design achieves a competitive Figure-of-Merit (FoM) of$0.017~({\mathrm {\text {m}\text {m} ^{2}}} \cdot {\mathrm {\text {p}\text {J} }}) / (\text {b} \cdot {\mathrm {\text {V}}})$, demonstrating its scalability and efficiency compared to prior state-of-the-art solutions.

This paper presents a 0.13-μm CMOS system-on-chip (SoC) designed for multilead ECG recording in low-power wearable applications. The fully integrated system features eight fully differential recording channels, capable of extracting 11 ECG leads, a fully differential 10-bit capacitive successive approximation register (SAR) analog-to-digital converter (ADC), a digital controller with a custom master SPI interface, low-dropout regulators (LDOs), and level shifters to facilitate integration. The ECG recording channels are implemented using a novel fully differential current mirror-based bioamplifier circuit, which offers tunable gain and intrinsic band-pass filtering. The bioamplifier achieves an input-referred noise of 1.8 μVrmsover a 0.3-500 Hz bandwidth, a total harmonic distortion (THD) of 0.234%, and consumes 4.24 μA. The ADC achieves an effective number of bits (ENOB) of 9.18 while consuming 11 μA. Overall, the SoC draws 347 μW from a 1.2-V supply and demonstrates successful 11-lead ECG recordings and PQRST wave identification.

Photoplethysmography (PPG) sensors provide accurate measurements of vital signs, including heart rate (HR), HR variability (HRV), and oxygen saturation (SpO2). PPG signal detection needs light-emitting diodes with sufficient brightness, consuming several mW in continuous monitoring. We present a PPG sensor that adaptively predicts peaks and valleys (PAVs), i.e., the maximum and minimum amplitudes of the signal, and extracts HR, HRV, SpO2, systolic, diastolic, and cardiac cycles with exceptionally low power consumption. The sensor activates only when PAVs occur, resulting in a power reduction of >87%, while maintaining a mean absolute error (MAE) of2, and <3 ms for HRV, systolic, diastolic, and cardiac cycles. Our prototype was tested on the fingertip, wrist, arm, chest, neck, earlobe, and forehead of a 29-year-old subject.

This paper presents an ultra-wideband (UWB) transmitter based on capacitive array that decreases dependency of data rate to pulse repetition frequency, as well as power consumption and complexity. The entire system includes several delay stages, a capacitive array circuit, a Schmitt trigger, an impulse generator, a cross-coupled oscillator, and an antenna driver. A sequence of 5-bit parallel data is applied to the capacitive array, providing ramp signals with 32 equally separated slopes. This returns a variable pulsewidth at the output of the Schmitt trigger circuit, which corresponds to a specific sequence of input data. Post-layout simulation results show that the proposed circuit provides a linear time change in the pulsewidth with an accuracy of 176 ps in average for every input data LSB. Furthermore, the entire circuit consumes only 190 µW from a 0.6-V supply. The proposed transmitter achieves a significantly low energy consumption of 950 fJ/bit at 200 Mbps within the Federal Communications Commission spectral mask which addresses the design challenges of ultralow-power internet-of-things devices. The circuit is designed in TSMC 65-nm standard CMOS technology and occupies 0.0525 mm2of die area.

The Design of efficient, safe, and reliable circuits is a prime objective in high-voltage (HV) electronic systems, such as switched-mode power amplifiers (PAs). One of the main causes of efficiency degradation and reliability problems, in these amplifiers, is the shoot-through current from the HV power supply to the ground. To eliminate such current, a dead time generator (DTG) is used to modify the signals propagating through the high-side and low-side gate drivers by adding a fixed dead time between them. However, any delay mismatch between these gate drivers can reduce the dead time to the point that it becomes negative. In this paper, an HV-DTG architecture is introduced. The architecture mitigates the effects of delay mismatch variations in gate drivers, which can result from parameters mismatch, fabrication process variations, and temperature variations. An HV switched-mode class-D power amplifier is used to illustrate the performance of the DTG. The amplifier is implemented in a low-cost$0.35~\mu m$HV CMOS process. The total area of the PA is$0.5~mm^{2}$, where the DTG covers an area of$0.066~mm^{2}$. A measured system’s efficiency of 95.14% is achieved with the shortest dead time of 10.8 ns, which is 1.38x smaller than the generated dead time in comparable state-of-the-art HV dead time generators.

This Special Issue is a selection of the best papers presented at the 20th IEEE International NEWCAS Conference (NEWCAS) 2022, which was held in Quebec City, Canada, on June 19–22, 2022. As an interregional flagship conference of the IEEE Circuits and Systems Society (CASS), this conference covers a wide spectrum of topics, research, and practices in the fields of circuits and systems and offers an international forum for exchanging ideas and results.

This work presents a system for measuring and analyzing motion, by a portable electronic device and a flexible fiber sensor. The fiber is composed of multi-walled carbon nanotubes (MWCNTs) for its conductivity and polydimethylsiloxane elastomer (PDMS) for its elasticity. A new sensor interface circuit was designed in this study to interface with the fiber and measure its impedance. The measured impedance data are sampled and transmitted via Bluetooth to a laptop. The characteristics of the fiber and a wireless measurement system allow an easy integration into a smart garment to monitor various vital signs and motion markers (e.g angle, step). The system was assessed on a robotic arm before being put in realistic situations through various exercises (flexion/extension knee movements, standing multi-joint movements and walk/run on treadmill) on 5 participants for its ability to measure angle, number of movements, rate and speed. In addition, fibers measurement endurance capacities over months were observed. An assessment of the fiber impedance measurement circuit was performed (minimum resolution of$25~\Omega $, relative error of 2.82% on the estimated value of resistance). Tests carried out over a period of several months show that the fiber maintained good measurement performance when tested on a robotic arm, given an average correlation of 0.85 between angle and fiber impedance. The relative error (RE) made on the number of detected movements was 6.57% in average. In realistic workout situations, these values respectively reached between 0.58 and 73.95% for flexion/extension knee movements. A correlation factor of 0.76 was obtained when the participants were walking on a treadmill at a given speed. Otherwise, RE on number of movements was 8.33% for treadmill exercise, 12.84% on standing exercise. For these exercises and for the movement rate, the average correlation calculated with the reference was between 0.75 and 0.33. Finally, RE on estimated speed was 23.3% in average for the treadmill exercise. The system (sensor interface circuit and Fiber) allows to properly monitor human motion in various activities.

This paper presents a 4-level pulse-amplitude modulation (PAM4) reference-less half-baud-rate clock and data recovery (CDR) incorporated with a frequency acquisition scheme to achieve 14-Gb/s data rate while extending locking range to +/-450-MHz. A combination of PAM4 data recovery units (DRUs) and integrators minimizes the clock generation and distribution overhead as well as eliminates additional front-end samplers for the edge detection. We introduce an elegant way to distill frequency information from random data without a reference clock. The proposed CDR is implemented in a 65-nm CMOS technology, consuming 30-mW that translates to 2.14 pJ/bit from a 1.2-V supply voltage, while achieving a sensitivity of 20-mV and RMS recovered clock jitter of 0.5-ps.

This paper presents a tunable new deadtime control circuit providing an optimal delay for power converter optimization. Our method can reduce the deadtime loss while improving the efficiency and power density of a given power converter. The circuit presents a reconfigurable delay element to generate a wide range of deadtime for different power conversion applications with varying loads and input voltages. The optimal deadtime equation for buck converters is derived, and its dependency on the input voltage and load is discussed. Experimental results show that the presented circuit can provide a wide range of deadtime delays, ranging from 9.2 ns to 1000 ns. The power consumption of the presented circuit is measured for different capacitive loads ($\text{C}_{\mathrm {L}}$) and operating frequencies (${f}_{\mathrm {s}}$). The circuit consumed a power between 610$\mu \text{W}$and$850~\mu \text{W}$across the measured deadtime ranges while$\text{C}_{\mathrm {L}} =12$pF,$\text{V}_{\mathrm {dd}} =3.3$V, and$\text{f}_{\mathrm {s}}=200$kHz. The proposed deadtime generator can operate up to 18 MHz when the minimum deadtime of 9.2 ns is selected. The presented circuit occupies an area of$150\mu $m$\times 260\mu \text{m}$. The fabricated chip is connected to a buck converter to validate the operation of the proposed circuit. The efficiency of a typical buck converter with minimum$\text{T}_{\mathrm {DLH}}$and optimal$\text{T}_{\mathrm {DHL}}$at$\text{I}_{\mathrm {Load}} =25$mA is improved by 12% compared to a converter with a fixed deadtime of$\text{T}_{\mathrm {DLH}} =\,\,\text{T}_{\mathrm {DHL}} =12$ns.

In this study, a dead-time control circuit is proposed to generate independent delays for the high and low sides of half-bridge converter switches. In addition to greatly decreasing the losses of power converters, the proposed method mitigates the shoot-through current through the application of superimposed power switches. The circuit presented here comprises a switched capacitor architecture and is implemented in AMS 0.35$\mu \text{m}$technology. In the implementation, the proposed dead-time control circuit occupies a silicon area of$70\,\,\mu \text{m}\,\,\times 180\,\,\mu \text{m}$. To realize the technique, a two-sided wide swing current source is employed. Each sides of the current source comes with two capacitors, two Schmitt triggers, and three transmission gates. Results show that the low and high sides of the projected half-bridge converter switches respectively require delays of 35 and 62 ns. The performance of the proposed dead-time circuit is evaluated by assembling it with the half-bridge converter. The proposed dead-time prototype achieves a 40% drop in power losses in the half-bridge circuit.

This paper proposes a methodology based on machine learning to find apparent causal relations between performance targets and design variables in analog circuits. Diversified filtering and wrapping variable selection algorithms are utilized to construct a causal graph that identifies the major circuit design parameters that can be used to optimize the performance of analog circuits. Based on the constructed causal graph, a sequence of design procedures can be extracted and followed to optimize the performance of a design. The proposed methodology is validated using a two-stage op-amp. The obtained causal graph agrees with analytical design equations published in the literature for the selected two-stage op-amp. The results also show that the proposed methodology can accelerate the circuit design process and effectively help designers understand the reasoning behind different design decisions.

This paper presents the design and analysis of combining input-voltage feedforward and proportional-integral (PI) controllers to regulate the output voltage of DC-DC Buck converter subject to input line disturbances. Non-idealities of the Buck converter such as passive and active components parasitics are included in the mathematical model obtained by the statespace averaging (SSA) technique for accurate control. The stability boundary locus approach is used to graphically analyze the system stability. It guides the design of the PI controller gains and the feedforward scaling factor to achieve desired phase and gain margins. Analysis shows that the feedforward scaling factor affects the stability regions of the closed-loop system and can limit the possible PI controller gains for certain phase and gain margins; 75oand 9.54 dB in our case. The results are verified by a Simulink model developed for the Buck converter system.

A novel level down shifter intended for translation of signals with different amplitudes in System-on-Chip (SoC) applications is presented. This new single supply down-shifter architecture, implemented in a 0.35μm AMS CMOS technology provides multiple reconfigurable levels. A diode connected circuit structure, a current source, five transmission gates, a diode- supercapacitor combination, and input/output buffers are employed to implement this reconfigurable level shifter. The circuit receives a pulse shaped signal with an amplitude of 3.3 V, and provides three different signals with nominal amplitudes of 1.2 V, 1.8 V, and 2.5 V depends on the circuit configuration. The proposed circuit successfully drives a range of capacitive loads between 10 fF and 350 pF. The presented circuit consumes a static and a dynamic power consumptions of 62.37 pW and 108μW, respectively from a 3.3V supply, at an operating frequency of 1 MHz and a capacitive load of 10 pF. Post-layout simulation results show that the fall and rise propagation delays of the three configurations are in the range of 0.54 ns-26.5 ns and 11.2 ns-117.2 ns, respectively. It occupies an area of 80 μmx100 μm.

In this paper, we propose a novel design methodology for a deadtime generator for high-efficiency power amplifiers. It consists of a two-phase non-overlapping clock circuit and level down shifters. A 3% improvement in efficiency is achieved with a maximum efficiency of 94% in a class-D power amplifier circuit. The proposed design generates a deadtime as low as 16.7ns and eliminates the problem of propagation delay mismatch between high side and low side gate drivers. The circuit is implemented in 50V AMS 0.35 μm CMOS technology. The deadtime generator consumes 16.5 mW, while occupying a total area of 0.068 mm2.

This paper presents a wireless electro-optic headstage for parallel electrophysiological recording of neural action potential (AP), local field potentials (LFP) and electromyography (EMG), while performing multichannel optical stimulation. The headstage is designed for enabling experiments with freely moving rodents, like small laboratory mice. It can record electrophysiological activity on up to 32 channels and stimulate with up to 4 optical channels using only commercial off the shelf (COTS) components. An embedded digital field-programmable gate array (FPGA) is used to allow real time digital separation of AP and LFP waveform and the recording of EMG. It also performs data reduction through AP detection and compression allowing to collect and transmit data wirelessly from 32 parallel channels. The digital filters are optimized to reduce latency so the 32 channels can be time multiplexed on a single data path to decrease power consumption and optimize resource utilisation. Performing signal separation allows the compression of the AP and the decimation of the LFP/EMG. This reduces the amount of data to transmit by a factor of 7.77 (for a firing rate of 50 AP/second) and allow a higher channel count for both signals. The headstage only weights 3.8 g w/ a 100 mAh battery and 4.68 g with the plastic packaging and on/off switch.

In this paper, a wide range, area efficient, and high-precision pulse generator is presented. The proposed architecture exploits a CMOS thyristor delay element, and benefits from its decent advantages. The proposed circuit generates an input-independent, stable, and accurate pulse width signal. The pulse-width can be tuned continuously in the range of 1.5 ns to 45 ms. This novel structure is a part of a control circuit intended for recognizing the eventual faults and errors in industrial sensor interfaces. The presented circuits have been implemented in 0.35 μm standard CMOS process. It consumes 0.11 to 7.42 mW power from a 3.3 V supply. The total occupied area is about 0.018 mm2and the maximum operation frequency of the proposed pulse generator is 331 MHz.

A novel non-overlapping signal generator intended for power-converters operation is presented. This switched capacitor circuit architecture-based sensor and actuator interface is implemented in AMS-H35B4D3 technology and consumes a power of 51.8 mW from a 3.3V-supply at 1 Mbps. Two-sided wide swing current source, two capacitors, Schmitt triggers and three transmission gates are employed on each side of the current source for implementing this versatile building block. The circuit provides needed dead-time to the power amplifier for high and low voltage applications. The time period of the master CLK is 1μs. The proposed circuit successfully generates two outputs with 0.4813 μs (~half of period) non-overlapping delay between the phases. It occupies an area of 70 μm×180 μm from the total half bridge area of 800 μm×1370 μm.

This paper presents a CMOS optoelectronic lock-in amplifier (LIA) with semi-digital automatic phase alignment for optical sensing applications. The LIA incorporates a phase sensitive detection (PSD) channel and a phase alignment channel. A phase alignment loop generates the LIA reference clock, and automatically aligns the relative phase between the reference and the input signal through a phase interpolation providing unlimited (modulo 2π) phase shift. A finite state machine (FSM) is implemented in the digital domain to control the loop with low-power consumption and small chip area. The LIA is optimized to operate at a 50-kHz modulation frequency. The LIA is fully characterized optically and electrically, and measurement results are reported in this paper. The measured dynamic reserve and the sensitivity of the LIA are 35.37 dB and 296 mV/μW, respectively for a detection bandwidth of 50 Hz. The proposed LIA consumes an average power of 214 μW from a 1.8/3.3-V DC power supply.

In recent years, researchers have explored human body posture and motion to control robots in more natural ways. These interfaces require the ability to track the body movements of the user in three dimensions. Deploying motion capture systems for tracking tends to be costly and intrusive and requires a clear line of sight, making them ill adapted for applications that need fast deployment. In this article, we use consumer-grade armbands, capturing orientation information and muscle activity, to interact with a robotic system through a state machine controlled by a body motion classifier. To compensate for the low quality of the information of these sensors, and to allow a wider range of dynamic control, our approach relies on machine learning. We train our classifier directly on the user to recognize (within minutes) which physiological state his or her body motion expresses. We demonstrate that on top of guaranteeing faster field deployment, our algorithm performs better than all comparable algorithms, and we detail its configuration and the most significant features extracted. As the use of large groups of robots is growing, we postulate that their interaction with humans can be eased by our approach. We identified the key factors to stimulate engagement using our system on 27 participants, each creating his or her own set of expressive motions to control a swarm of desk robots. The resulting unique dataset is available online together with the classifier and the robot control scripts.

Robotic devices can be leveraged to raise the abilities of humans to perform demanding and complex tasks with less effort. Although the first priority of such human-robot interaction (HRI) is safety, robotic devices must also be intuitive and efficient in order to be adopted by a broad range of users. One challenge in the control of such assistive robots is the management of the end-effector orientation, that is not always intuitive for the human operator, especially for neophytes. This paper presents a novel orientation control algorithm designed for robotic arms in the context of HRI. This paper aims at making the control of the robot's orientation easier and more intuitive for the user, both in the fields of rehabilitation (in particular individuals living with upper limb disabilities) and industrial robotics. The performance and intuitiveness of the proposed orientation control algorithm is assessed and improved through two experiments with a JACO assistive robot with 25 able-bodied subjects, an online survey with 117 respondents via the Amazon Mechanical Turk and through two experiments with a UR5 industrial robot with 12 able-bodied subjects.

Implantable wireless fiber biophotometry is one of the most effective technique to monitor specific cell types through Ca2+fluorescence sensing in live animals by avoiding fiber tethering and risks of breakage and injury which can occur in conventional apparatus. This demonstration will show the visitors a novel low-power, light-weight, and minimally invasive wireless optoelectronic interface enabling chronic brain fiber biophotometry in freely moving laboratory mice. The proposed device incorporates a custom integrated CMOS biosensor and off-the-shelf optical and electronic components such as a wireless transmitter, a LED as excitation light source, a microprocessor, and a miniaturized 3D-printed housing. The CMOS biosensor includes a two-step analog-to-digital converter (ADC) with a noise cancellation scheme enabling wide dynamic range and high-energy efficiency photocurrent quantization. A fiber biophotometry head-mountable 3D-printed housing holds in place the optical and electronic components and allows the utilization of a single mutlimode fiber to convey the excitation light and to collect the evoked fluorescence light.

This paper presents a high-sensitivity incremental discrete timeΣΔ analog-to-digital converter (ADC) with variable clock and on-chip decimation for biophotometry sensing. A digital correlated double sampling (CDS) scheme is merged with the decimation filter for providing energy- and area-efficient mean to suppress low-frequency noise. Chopper modulation and differential sensing using a dummy photodiode along with charge transfer switches are used as well to allow for the detection of ultra low-input low fluorescence light, down to a few femttowatt, by suppressing the low-frequency noise of theΣΔ modulator and the dark current of the photodetector. A variable clock scheme is used across the sensor operating phases (namely the reset conversion phase and the signal conversion phase) to decrease the biosensor conversion time. To decrease illumination time and save the excitation light source energy, the biosensor uses a short sensing duty cycle of 1.2 ms (12%) for a sampling period of 10 ms. The proposed optoelectronic biosensor is implemented in a 0.18-μm CMOS technology, consuming 56 μW from a 3.3/1.8-V supply voltage, while achieving a high signal-to-noise and distortion ratio of 101 dB and a minimum detectable current of <; 100-fArms, within conversion time of 1.2 ms. The proposed biosensor presents a FOM of 0.73 pJ/conv., which is among the best reported performances compared to previous solutions.

This paper presents a fully wireless neuroscience platform for enabling uninterrupted optogenetic experiments with live laboratory rodents. The system includes a wireless power transmission (WPT) home-cage using a 4-coil resonant link, a motion tracking system, a multichannel optogenetic headstage and a base station. The WPT home-cage uses a new hybrid parallel power transmitter (TX) coil array and segmented multicoil resonators to achieve high power transmission efficiency (PTE) and deliver high power across distances as high as 20 cm. The multicoil power receiver (RX) uses a RX coil with a diameter of 1.0 cm and a resonator coil with a diameter of 1.5 cm. The WPT home-cage average power transfer efficiency is 29.4%, at a nominal distance of 7 cm, for a power carrier frequency of 13.56 MHz. It has maximum and minimum PTE of 50% and 12% along the Z axis, and can deliver a constant power of 74 mW to supply the miniature neural headstage. The neural headstage includes 1 optical stimulation channel and 4 recording channels. We show that the hybrid WPT home-cage can properly power up the headstage without interruption, while the motion tracking system can track the activity of the animal in real time for enabling simultaneous behavioural and physiological assessment.

This paper presents a new autonomous wireless sensor platform intended for the monitoring of microorganisms and molecules found in harsh environments, like in the northern climates. The EcoChip includes a layered multiwell plate that allows the growth of single strain microorganisms, within a well of the plate, isolated from environmental samples from Northern habitats. It can be deployed in the field for continuous monitoring of microbiological growth within 96 individual wells through a multichannel electro-chemical impedance monitoring circuit. Additional sensors are provided for monitoring luminosity, humidity, temperature, pH, and CO2 release. The embedded electronic board is equipped with a flash memory to accumulate and store sensor data for long periods of time, as well as with a low-power micro-controller, and a power management unit to control and supply all electronic building blocks. When a receiver is located within the transmission range of the EcoChip, a low-power wireless transceiver allows transmission of sensor data stored from on-board memory. We report the measured performance of the system, and we present experimental results obtained in the field during a pilot study performed with the EcoChip deployed in the village of Kuujjuarapik, at a latitude of 55 degrees, in Northern Canada.

This paper presents an FPGA implementation of a DNA assembly algorithm, called Ray, initially developed to run on parallel CPUs. The OpenCL language is used and the focus is placed on modifying and optimizing the original algorithm to better suit the new parallelization tool and the radically different hardware architecture. The results show that the execution time is roughly one fourth that of the CPU and factoring energy consumption yields a tenfold savings.

This paper presents a novel particle swarm optimization (PSO) algorithm that combines the strengths of several PSO variants into a single competitive algorithm. This novel algorithm, named Time Adaptive Dual Particle Swarm Optimization (TAD-PSO), is comprised of two specialized populations, with one focusing on exploration of the search space and the other on exploitation. The main population, specialized in exploration, uses orthogonal learning to create information-rich exemplars which intelligently guide particle movement throughout the search space. The auxiliary population uses a PSO variant known for its very fast convergence speed, and thus very high performance on unimodal problems. This population is specialized in exploitation of the interesting local minima. The main population size decays linearly, to foster exploration early and convergence in the later stages of the optimization procedure. Additionally, TAD-PSO does not have the topological structure of the swarm as an algorithm hyper-parameter, making it a fast and simple algorithm to apply to new problems. TAD-PSO was tested extensively and compared to 6 widely used PSO variants on 19 benchmark problems, for 10, 30 and 100 dimensions. TAD-PSO consistently ranked first in each dimensional space, making it a competitive optimization algorithm on both unimodal and multimodal problems.

Multimodal body-machine interfaces play an important role in providing severely impaired with interaction solutions that can adapt to their functional capacities [1]. This demonstration will allow the visitors to experience an intuitive and wearable control interface, designed for people with upper body disabilities, that translates head motion, shoulder elevation and surface electromyography into appropriate commands for controlling assistive devices, like robotic arms. Visitors will be invited to interact with JACO using the proposed interface, a 6 degree-of-freedom assistive robotic arm developed by Kinova Robotics, within a control task that will consist in moving items or stack objects at specific locations on a table.

This paper presents a new multimodal control interface for people living with upper-body disabilities based on a wearable wireless sensor network. The proposed body-machine interface is modular and can be easily adapted to the residual functional capacities (RFCs) of different users. A custom data fusion algorithm has been developed for emulating a joystick control using head motion measured with a lightweight wireless inertial sensor enclosed in a headset. The wearable network can include up to six modular sensor nodes which can be used simultaneously to read different RFCs including gesture and muscular activity, and translate them into commands. Sensor data fusion is performed inside the sensor nodes in order to free the wireless link and the base station, and decrease power consumption. Requirements of such an interface are established for people using powered-wheelchairs, and a proof of concept system is implemented and used to control an assistive robotic arm. It is shown that the performance of the system compares well to conventional control systems like the joystick controller, while being potentially more suitable for the severely disabled.

This paper presents different miniature wireless brain computer interfaces (BCI) enabling synchronized optogenetics and electrophysiology recording for various experimental conditions. These devices, which are entirely built using commercial off-the-shelf components, are validated in-vivo with small transgenic mice. First, a system including 32 electrophysiological recording channels and up to 32 high-power optical stimulation channels is presented. It can process 32 neuronal signals in parallel with high compression ratio using an embedded digital field-programmable gate array (FPGA) signal processor performing spike detection and data compression in-situ. Then, an advanced version featuring equivalent characteristics, but having the third of the size and the weight is presented. The design of a third system dedicated to small freely moving animals is presented. It includes 4 electrophysiological recording channels and 1 high-power optical stimulation channel. In-vivo result obtained in freely moving mice with this system are reported. This latter system performs aggressive data reduction using a resource optimized spike detector running on a low-power microcontroller unit. Finally, a multichannel optical stimulator operating within a network of up to 6 active devices is presented for enabling optogenetic behavioral experiments with several mice at once. These different wireless BCI all provide a multimodal access to brain activity through optogenetics and large-scale electrophysiology.

The demonstration will presents a battery powered multichannel wireless optogenetic headstage providing neural recording and optical stimulation capabilities simultaneously. The proposed headstage, which is entirely built using commercial off-the-shelf components, includes 32 electrophysiological recording channels and up to 32 high-power optical stimulation channels. It can process 32 neuronal signals in real-time with high compression ratio using an FPGA performing spike detection and data compression in-situ for fitting 32 channels over a low-power 2.4-GHz ISM data link (compression ratio > 500), hence greatly decreasing power and complexity. The presented headstage is small and lightweight enough for enabling optogenetic in-vivo experiments with freely moving transgenic rodents.

This paper presents a new wireless device for performing combined heart optogenetics and electrocardiography (ECG) recording. While optogenetics is extensively used in brain research, the designed prototype aims at expanding this ground-breaking experimental approach to research and applications on heart diseases. Such a system has a multitude of applications ranging from laboratory research to the development of non-invasive optogenetic pacemaker with ECG feedback for heart resynchronization. The proposed prototype is designed for experimental research with small freely moving animals enabling photo-stimulation of genetically modified cells using three different wavelengths (470, 615 and 625 nm) with four intensities by varying LED driving currents between 79 and 198 mA for a maximum optical power of 208.5 mW. The system can record the heart ECG through four electrodes positioned as specified by the Einthoven's triangle. It is controlled wirelessly through a host computer where the recorded data can be displayed in real-time. The device weights 1.12 g without battery and has an autonomy of 3 hours of continuous stimulation and recording using a 100-mAh battery. Results from in-vivo trials with laboratory mice show that the system can successfully record ECG.

This paper presents a new biophotometry sensor embedding two individual building blocks, namely a low-noise sensing front-end and a 2ndorder continuous-time ΣΔ modulator (CTSDM), into a single module for enabling high-sensitivity and high energy-efficiency photo-sensing. In particular, a differential CMOS photodetector associated with a differential capacitive transimpedance amplifier (DCTIA)-based sensing front-end is merged with an incremental 2nd order 1-bit CTSDM to achieve a large dynamic range, low hardware complexity, and high energy-efficiency. The proposed optoelectronic biosensor is implemented in a 0.18-μm CMOS technology, consuming 8.23 μW from a 1.8-v supply voltage while achieving a peak SNDR of 62.5 dB, a dynamic range of 86 dB over a 50-Hz input bandwidth, a minimum detectable current of 90-fArms, and at a 25.6-KS/s sampling frequency. The proposed biosensor presents the best FOM of 0.0197 pJ/conv. among recently published biosensors.

This paper presents a wirelessly powered, fully-integrated, low-power full-duplex transceiver to support high-density and bidirectional neural implants. The transmitter (TX) uses impulse radio ultra-wide band based on an edge combining approach, and the receiver (RX) uses a 2.4-GHz on-off keying narrow band topology. The proposed transceiver provides dual band 500-Mbps TX uplink data rate and 100 Mbps RX downlink data rate, and it is fully integrated into standard TSMC 0.18-μm CMOS within a total size of 0.8 mm2. The total measured power consumption is 10.4 mW in full duplex mode (5 mW at 100 Mbps for RX, and 5.4 mW at 800 Mbps or 6.7 pJ/bit for TX). The transceiver is wirelessly powered up by a smart home-cage system based on overlapped multicoil arrays through a thin implantable multicoil receiver of 1×1 cm2of size, implanted bellow the scalp of a laboratory mouse, and integrated power management circuits. This inductive system is designed to deliver up to 35.5 mW of power delivered to the load from a 13.56-MHz carrier signal with an overall power transfer efficiency above 5% across a separation distance ranging from 3 cm to 5 cm.

In recent years, gesture-based interfaces have been explored in order to control robots in non-traditional ways. These require the use of systems that are able to track human body movements in 3D space. Deploying Mo-cap or camera systems to perform this tracking tend to be costly, intrusive, or require a clear line of sight, making them ill-adapted for artistic performances. In this paper, we explore the use of consumer-grade armbands (Myo armband) which capture orientation information (via an inertial measurement unit) and muscle activity (via electromyography) to ultimately guide a robotic device during live performances. To compensate for the drop in information quality, our approach rely heavily on machine learning and leverage the multimodality of the sensors. In order to speed-up classification, dimensionality reduction was performed automatically via a method based on Random Forests (RF). Online classification results achieved 88% accuracy over nine movements created by a dancer during a live performance, demonstrating the viability of our approach. The nine movements are then grouped into three semantically-meaningful moods by the dancer for the purpose of an artistic performance achieving 94% accuracy in real-time. We believe that our technique opens the door to aesthetically-pleasing sequences of body motions as gestural interface, instead of traditional static arm poses.

In the realm of surface electromyography (sEMG) gesture recognition, deep learning algorithms are seldom employed. This is due in part to the large quantity of data required for them to train on. Consequently, it would be prohibitively time consuming for a single user to generate a sufficient amount of data for training such algorithms. In this paper, two datasets of 18 and 17 able-bodied participants respectively are recorded using a low-cost, low-sampling rate (200Hz), 8-channel, consumer-grade, dry electrode sEMG device named Myo armband (Thalmic Labs). A convolutional neural network (CNN) is augmented using transfer learning techniques to leverage inter-user data from the first dataset and alleviate the data generation burden imposed on a single individual. The results show that the proposed classifier is robust and precise enough to guide a 6DoF robotic arm (in conjunction with orientation data) with the same speed and precision as with a joystick. Furthermore, the proposed CNN achieves an average accuracy of 97.81% on seven hand/wrist gestures on the 17 participants of the second dataset.

Assistive technology (AT) tools and appliances are being more and more widely used and developed worldwide to improve the autonomy of people living with disabilities and ease the interaction with their environment. This paper describes an intuitive and wireless surface electromyography (sEMG) based body-machine interface for AT tools. Spinal cord injuries at C5-C8 levels affect patients' arms, forearms, hands, and fingers control. Thus, using classical AT control interfaces (keypads, joysticks, etc.) is often difficult or impossible. The proposed system reads the AT users' residual functional capacities through their sEMG activity, and converts them into appropriate commands using a threshold-based control algorithm. It has proven to be suitable as a control alternative for assistive devices and has been tested with the JACO arm, an articulated assistive device of which the vocation is to help people living with upper-body disabilities in their daily life activities. The wireless prototype, the architecture of which is based on a 3-channel sEMG measurement system and a 915-MHz wireless transceiver built around a low-power microcontroller, uses low-cost off-the-shelf commercial components. The embedded controller is compared with JACO's regular joystick-based interface, using combinations of forearm, pectoral, masseter, and trapeze muscles. The measured index of performance values is 0.88, 0.51, and 0.41 bits/s, respectively, for correlation coefficients with the Fitt's model of 0.75, 0.85, and 0.67. These results demonstrate that the proposed controller offers an attractive alternative to conventional interfaces, such as joystick devices, for upper-body disabled people using ATs such as JACO.

Recently, robotics has been seen as a key solution to improve the quality of life of amputees. In order to create smarter robotic prosthetic devices to be used in an everyday context, one must be able to interface them seamlessly with the end-user in an inexpensive, yet reliable way. In this paper, we are looking at guiding a robotic device by detecting gestures through measurement of the electrical activity of muscles captured by surface electromyography (sEMG). Reliable sEMG-based gesture classifiers for end-users are challenging to design, as they must be extremely robust to signal drift, muscle fatigue and small electrode displacement without the need for constant recalibration. In spite of extensive research, sophisticated sEMG classifiers for prostheses guidance are not yet widely used, as systems often fail to solve these issues simultaneously. We propose to address these problems by employing Convolutional Neural Networks. Specifically as a first step, we demonstrate their viability to the problem of gesture recognition for a low-cost, low-sampling rate (200Hz) consumer-grade, 8-channel, dry electrodes sEMG device called Myo armband (Thalmic Labs) on able-bodied subjects. To this effect, we assessed the robustness of this machine learning oriented approach by classifying a combination of 7 hand/wrist gestures with an accuracy of ∼97.9% in real-time, over a period of 6 consecutive days with no recalibration. In addition, we used the classifier (in conjunction with orientation data) to guide a 6DoF robotic arm, using the armband with the same speed and precision as with a joystick. We also show that the classifier is able to generalize to different users by testing it on 18 participants.

Proper acquisition of the photoplethysmography signals is essential in a pulse oximetry system and sensor placement plays an important role in this respect. Due to the complex structure of the finger tissue, inadequate sensor placement will have an adverse effect on the light path and high signal quality may become impossible to achieve [1]. In this paper, we present a ring shaped oximeter that uses six sets of light emitting diodes and photodetectors, uniformly distributed around the finger to identify the best signal path, thus making the signal acquisition immune to ring position. Moreover it uses a radio transceiver to eliminate the connection wires to a base station. In this proof of concept study, this novel ring oximeter was implemented with commercial low power consumption off-the-shelf components mounted on a rigid-flex board that connects to a remote host for signal processing and oxygen level calculation.

This paper presents the in vivo performances of a resource-optimized digital action potential (AP) detector featuring an adaptive threshold based on a new Sigma-delta control loop. The proposed AP detector is optimized for utilizing low hardware resources, which makes it suitable for real-time implementation on most common low-power microcontroller units (MCU). The adaptive threshold is calculated using a digital control loop based on a Sigma-delta modulator that precisely estimates the standard deviation of the neuronal signal amplitude. The detector was demonstrated using a common MCU from MSP430 family, incorporated into a small wireless platform for combined optogenetics and neura recording. The system has been fully characterized experimentally within in vivo experiments on a freely-moving transgenic mouse expressing ChannelRhodospin (Thy1::ChR2-YFP line4. The results demonstrate that the proposed AP detector can be used to achieve overall data reduction ratios above 11 hen transmitting only the detected APs. A comparison of the obtained results with other thresholding approaches shows that the pr posed detector provides similar performances to those significantly more resource demanding approaches.

In this Paper, we propose a new method for safe electrical neural stimulation. Current mode digital-to-analog converters are used to generate the cathodic and the anodic stimulation phases. A sample-and-hold and a window comparator circuit are used to compare the voltage of the electrode and the tissue with a target value within a safe voltage range of -50 mV to +50 mV. When the electrode voltage falls below the lower bound or above the upper bound of such a safe voltage range, the anodic stimulation pulse width is modified in such a way that the electrode voltage remains in the safe range. High-level pulse shrinking (HLPS) and low-level pulse shrinking (HLPS) elements are used in digital part to modify anodic pulse width. Simulation results for the proposed circuit implemented in a 0.18μm 1P6M CMOS technology confirm proper functioning and show that the proposed circuit requires extremely low power compared to other charge balancing schemes, with a power consumption of 4.2 μW.

In this work, we present a clinical prototype with a wearable patient interface for microwave breast cancer detection. The long-term aim of the prototype is a breast health monitoring application. The system operates using multistatic time-domain pulsed radar, with 16 flexible antennas embedded into a bra. Unlike the previously reported, table-based prototype with a rigid cup-like holder, the wearable one requires no immersion medium and enables simple localization of breast surface. In comparison with the table-based prototype, the wearable one is also significantly more cost-effective and has a smaller footprint. To demonstrate the improved functionality of the wearable prototype, we here report the outcome of daily testing of the new, wearable prototype on a healthy volunteer over a 28-day period. The resulting data (both signals and reconstructed images) is compared to that obtained with our table-based prototype. We show that the use of the wearable prototype has improved the quality of collected volunteer data by every investigated measure. This work demonstrates the proof-of-concept for a wearable breast health monitoring array, which can be further optimized in the future for use with patients with various breast sizes and tissue densities.

In this work, we present the emissive performance of wearable radio-frequency (RF) textiles made from multi-material fibers, for both on-body and off-body scenarios, for body area network applications through ISM (2.4 GHz) bands. It is shown that the emissive performance of the RF textiles in terms of return loss (S11), radiation pattern, and efficiency (gain) were similar to commercial router antennas, while the center frequency shift and band broadening were reduced due in part to the small form factor of the fiber antennas. The RF textiles were fabricated by integrating unobtrusive polymer-glass-metal fiber composites into a textile host using conventional weaving process. This approach provided good RF emissive performance in compliance with safety regulations while preserving the mechanical and cosmetic properties of the garments.

Spike detection is a signal processing technique that can enable significant data rate reduction and resource savings in wireless brain monitoring. In these systems, energy-efficient spike detection algorithms are sought for enabling realtime signal processing while consuming low-power. As several spike detectors are based on ASIC, FPGA or low-power microcontroller unit (MCU), such algorithms must add little overhead to the entire system, while ensuring low error rate. In this paper, we present a comparative study of three different spike detection algorithms targeted toward implementation into low-power resource-constrained electronic systems. As practical validation, all candidate algorithms have been implemented on a popular low-power MCU and were fully characterized experimentally using previously recorded neural signals with different signal-to-noise ratios. A cost function based on detection rates, execution times, power consumption and resource utilization have been created and employed for comparing the detectors. The performances of all candidates are reported, and the best detector is identified. All candidate detectors present detection rate above 95% at high SNR, and above 78% for low SNR and can reduce the power consumption by up to 22.7%. This paper is the first to demonstrate the performances and hardware limitations of spike detectors on a low-power MCU system.

In this paper, we present a light-weight, wireless optogenetic headstage which provides optical neural stimulation and electrophysiological recording alongside on-the-fly neural signal processing. The proposed headstage is suitable to conduct long terms in-vivo experiments with small freely moving transgenic rodents, and features two implantable LED-coupled optical fibers and two electrophysiological recording channels while being powered by a small Lithium-ion battery. The headstage can transmit the raw neuronal signals or only spike waveforms after applying on-the-fly spike detection, which reduces power consumption by up to 14.5%. The headstage is entirely built using commercial off-the-shelf components, and the miniature design, using rigid-flex PCBs, results into a lightweight (7.4g) and compact device (25×20×15 mm). Low-power consumption is achieved by using on-the-fly spike detection alongside a real-time operating system which brings the headstage autonomy to 3h25 in full operation, including high-output power optical stimulation, micro-volts neuronal signal amplification and wireless transmission of the acquired waveforms.

This paper presents a 2.4 GHz low-power CMOS On-Off Keying receiver front-end which contains a low noise amplifier with a novel down conversion mixer that is designed for wireless forward telemetry link in neural stimulation applications. The transceiver operates between 2.4 and 2.5 GHz to support the full industrial, scientific and medical band. The post-layout simulation results show that the fully integrated low-noise amplifier exhibits a gain of 15 dB, a noise figure of 1 dB at 2.4 GHz, and the input matching (S11) is -16 dB. The proposed mixer is resistor-less and designed based on current starved delay elements in a Gilbert topology. The transceiver implemented in a TSMC 0.18 μm CMOS technology uses a supply voltage of 1.2 V, supports a data rate of up to 100 Mbps, and consumes 7 mW. The total size of the proposed receiver front-end equals 0.38 mm2.

The ubiquitous real-time monitoring and recording of surface electromyography (sEMG) signal is essential to several rehabilitation applications, such as muscle recovery analysis. We present an inexpensive wireless sEMG sensor using a commercial off-the-shelf wireless microcontroller unit (MCU) incorporating a simplified adaptive differential pulse code modulation (ADPCM) routine for real-time data compression. In single-channel configuration, the presented approach reduces power consumption of a transmission subsystem by up to 69%, leading to longer operation life expectancy. Due to the excessive amount of data as well as the limited processing power of embedded MCUs, multi-channel configurations would normally not be feasible. However, the proposed compression method makes a multi-channel EMG sensor possible. The distortion induced by this approach on EMG signal is on the order of 1%. Test results from in vivo trials with humans are presented.

Tongue Drive System (TDS) is a tongue-operated, wireless assistive technology that infers its users' intentions by detecting their voluntary tongue motions, and translating them into user-defined commands. In this paper, we present the design of a low-power analog front-end (AFE) with configurable characteristics that tracks the tongue motion by reading four intra-oral 3-D magnetic sensors to indicate the position of a small magnet attached to the tongue. Duty cycling is employed as a key feature in this system to reduce power consumption in the sensors and in the low-noise interfacing circuits, allowing for energy savings as much as 92%, which greatly extends the battery life. The AFE has been implemented in a 0.5-μm CMOS process and consumes 7.1 μA per sensor readout from a 1.8/4.2 V supply at a minimum duty cycle of 2%. Post-layout simulations show that the readout channels typically feature a noise density of 57 nV/VHz and a CMRR above 120 dB within the frequency band of interest (DC-2.5 kHz). Moreover, it presents a typical THD of 0.025%, a maximum input-referred offset of 5 μV, and a gain that is independent of temperature and process variations.

We present the design and implementation of linear-phase delay filters for ultra-low power neural signal processing. The filters are intended to implement a low-distortion delay element for automatic biopotential detection in neural recording implants. Continuous-time OTA-C filters are used to realize a 9th-order equiripple transfer function presenting a constant group delay. This analog delay allows to process neural waveforms with reduced overhead compared with digital delays. An allpass transfer function is used to implement such analog delay because it achieves wider constant-delay bandwidth than all-pole does. Two filters realizations are compared for implementing it: the cascaded structure and the inverse follow-the-leader feedback filter. Their respective strengths and drawbacks are assessed by modeling parasitics and non-idealities of OTAs, and using transistor-level simulations. A power budget of 200 nA is used in both filters. Experimental measurements with the chosen topology are presented and discussed.

We present a low-power CMOS analog circuit for automatic detection of action potentials (APs) in extracellular recordings. The detector emphasizes neural APs by means of an energy-based preprocessor and locates them with a precision comparator. A linear-phase delay filter allows signal buffering to avoid truncated waveforms. The proposed detector isolates the identified waveforms in their entirety and completely preserves their features in order to improve shapes discrimination. The proposed circuit, implemented in a CMOS 0.18-mum process, achieves ultra low-power consumption as the whole detector dissipates only 781.5 nW. The detector has been validated in simulations with real neural signals and successfully detects APs from the underlying background activity.

Investigations on the electromagnetic behaviour of a low-power amplifier are led using the Extended-Integrated Circuit Emission Model (ICEM). This modeling approach is proposed on a mixed-signal (analog/digital) CMOS 0.18 μm circuit dedicated to neural signal recording. This ICEM allows coarse and fast studies of the electromagnetic compatibility of CMOS devices especially in characterizing the coupling phenomena that occurs at each building block inside the whole chip. ICEM simulations of power and ground bounces are more than 500 times faster than complete SPICE ones with a correct accuracy for first electromagnetic compatibility investigations. This quick modeling method allows for checking many different design or simulation configurations. For example, some simulation results show that substrate interactions and power/ground crosstalk increase the noise level of the low-noise amplifier, in particular in its low frequency domain.

Bandwidth of wireless multichannel neural recording systems is one of the most significant limitation to increase the number of channels monitored. Data compression is being efficiently used to process multichannel recordings. This paper explores discrete wavelet transform (DWT) processor architectures suited to compress ENGs and so, increase the number of channels. Low power consumption, low silicon area and specificity of multichannel neural recording systems are considered for this investigation. Six architectures were implemented and compared. All of them implement a 3 level Daubechies-4 wavelet decomposition. This comparative study allows to conclude that an excellent trade-off between power consumption and silicon area is obtained through a DWT polyphase structure using a careful balance of parallelism and folding. Also, it arises that multiplexing several channels toward a shared DWT processor provides the best savings for both, power and area

We present a bioamplifier suitable for massive integration in implantable recording medical devices. This amplifier achieves reduced size and lower power consumption, compared to previous designs, by means of a novel DC rejection scheme. DC rejection is achieved by an active integrator located in the feedback loop of the bioamplifier. It places a highpass cutoff frequency within the transfer function, which is set by a small capacitor and a MOS-Bipolar equivalent resistor. This configuration rejects large DC offset and drift that exist at the electrode-electrolyte interface without the need for input RC networks or area consuming capacitors feedback networks, thus preserving the bioamplifier's high input impedance and small size. The proposed bioamplifier, designed in a 0.18-mum CMOS process, provides a midband gain of 53 dB, passes the neural signal from 105 Hz to 9.2 kHz and achieves an input-referred noise of 5 muVrms. It occupies less than 0.064 mm2and dissipates 8.4muW