A 769 μW Battery-Powered Single-Chip SoC With BLE for Multi-Modal Vital Sign Monitoring Health Patches

A 62.2dB SNDR Event-Driven Level-Crossing ADC With SAR-Assisted Delay Compensation Loop for Time-Sparse Biomedical Signal Acquisition

This paper proposed an event-driven clockless level-crossing ADC (LC-ADC) suitable for biomedical applications. Thanks to the LC loop, the sampling rate of the converter automatically adapts to the input activities. Activity-dependent power consumption and data compression can thus be realized, saving system power, especially during time-sparse signal acquisition. Meanwhile, a SAR-assisted loop is exploited to resolve the loop-delay-induced distortion in conventional LC-ADC. Therefore, the resolution and power efficiency of the LC-ADC are improved effectively while maintaining the event-driven feature. Implemented in a 55nm process, the proposed LC-ADC achieves a scalable power consumption and a peak SNDR of 62.2dB for a 20kHz input. It also achieves a Walden FoM of 29.7fJ/conv.-step and a Schreier FoM of 158.6dB, which is best in class, without using off-chip calibration. Sub µW power is realized when the input frequency is below 1.5kHz. The proposed LC-ADC is also verified by simulated electrocardiogram (ECG), neural spike, and electromyogram (EMG) signals. It provides a ∼7X data compression for ECG input, providing an attractive solution for time-sparse signal acquisition in biomedical applications.

Compact Vital-Sensing Band with Uninterrupted Power Supply for Core Body Temperature and Pulse Rate Monitoring

Although wearable devices for continuous monitoring of vital signs have undergone significant advancements, their need for frequent recharging precludes continuous operation, potentially leading to adverse outcomes being overlooked. Additionally, the scattered locations of the sensors hamper wearability. Herein, we present a compact vital-sensing band with uninterrupted power supply designed for continuous monitoring of core body temperature (CBT) and pulse rate. The band─which comprises two sensors, a power source (i.e., a flexible thermoelectric generator (TEG) and a battery), and a flexible circuit─is worn on the forearm. The CBT is calculated by measuring the skin temperature and heat flux, while a triboelectric nanogenerator-based self-powered pressure sensor is utilized for pulse rate monitoring. The TEG is a flexible unit that converts body heat into electricity, accumulating a total energy of 314 mJ (100%). Out of this total energy, only 43.2 mJ (7.2%) is utilized for CBT measurements, while the remaining 270.80 mJ (92.8%) is stored in the battery. This enables reliable and continuous operation of the vital-sensing band, highlighting its potential for use in healthcare applications.

A Wireless Multimodal Physiological Monitoring ASIC for Animal Health Monitoring Injectable Devices

Utilizing injectable devices for monitoring animal health offers several advantages over traditional wearable devices, including improved signal-to-noise ratio (SNR) and enhanced immunity to motion artifacts. We present a wireless application-specific integrated circuit (ASIC) for injectable devices. The ASIC has multiple physiological sensing modalities including body temperature monitoring, electrocardiography (ECG), and photoplethysmography (PPG). The ASIC fabricated using the CMOS 180 nm process is sized to fit into an injectable microchip implant. The ASIC features a low-power design, drawing an average DC power of 155.3 µW, enabling the ASIC to be wirelessly powered through an inductive link. To capture the ECG signal, we designed the ECG analog frontend (AFE) with 0.3 Hz low cut-off frequency and 45-79 dB adjustable midband gain. To measure PPG, we employ an energy-efficient and safe switched-capacitor-based (SC) light emitting diode (LED) driver to illuminate an LED with milliampere-level current pulses. A SC integrator-based AFE converts the current of photodiode with a programmable transimpedance gain. A resistor-based Wheatstone Bridge (WhB) temperature sensor followed by an instrumentation amplifier (IA) provides 27-47 °C sensing range with 0.02 °C inaccuracy. Recorded physiological signals are sequentially sampled and quantized by a 10-bit analog-to-digital converter (ADC) with the successive approximation register (SAR) architecture. The SAR ADC features an energy-efficient switching scheme and achieves a 57.5 dB signal-to-noise-and-distortion ratio (SNDR) within 1 kHz bandwidth. Then, a back data telemetry transmits the baseband data via a backscatter scheme with intermediate-frequency assistance. The ASIC's overall functionality and performance has been evaluated through an in vivo experiment.

A 2.3-5.7 μW Tri-Modal Self-Adaptive Photoplethysmography Sensor Interface IC for Heart Rate, SpO2, and Pulse Transit Time Co-Monitoring

This paper presents a tri-modal self-adaptive photoplethysmography (PPG) sensor interface IC for concurrently monitoring heart rate, SpO2, and pulse transit time, which is a critical intermediate parameter to derive blood pressure. By implementing a highly-reconfigurable analog front-end (AFE) architecture, flexible signal chain timing control, and flexible dual-LED drivers, this sensor interface provides wide operating space to support various PPG-sensing use cases. A heart-beat-locked-loop (HBLL) scheme is further extended to achieve time-multiplexed dual-input pulse transit time extraction based on two PPG sensors placed at fingertip and chest. A self-adaptive calibration scheme is proposed to automatically match the chip's operating point with the current use case, guaranteeing a sufficient signal-to-noise ratio for the user while consuming minimum system power. This paper proposes a DC offset cancellation (DCOC) approach comprised by a logarithmic transimpedance amplifier and an 8-bit SAR ADC, achieving a measured 38 nA residue error and 8.84 μA maximum input current. Fabricated in a 65nm CMOS process, the proposed tri-modal PPG sensor interface consumes 2.3-5.7 μW AFE power and 1.52 mm2 die area with 102dB (SpO2 mode), 110-116 dB (HR & PTT mode) dynamic range. A SpO2 test case and a HR & PTT test case are both demonstrated in the paper, achieving 18.9 μW and 43.7 μW system power, respectively.

Energy-Efficient Power Management Interface With Adaptive HV Multimode Stimulation for Power-Sensor Integrated Patch-Type Systems

An energy-efficient power management interface (PMI) with adaptive high-voltage (HV) stimulation capability is presented for patch-type healthcare devices where power management and sensor readout circuits are integrated. For efficient power supply, it proposes a multimode buck converter with an adaptive mode controller, delivering 95.6% peak power conversion efficiency and over 90% efficiency across a wide 4-440 mA output current range. For energy-efficient stimulation, a HV stimulation system is designed to perform mode-adaptive on/off control, where the charge pump (CP) is adopted for periodic power saving. The CP output is adaptively tuned to minimize the stimulator's power waste by utilizing a bio-impedance path in the sensor circuit. The stimulation core supports multimode functionality of current-/voltage-controlled stimulations with monopolar and bipolar modes, providing ten kinds of various stimulation waveform shape. For efficient system operation, battery interface circuits are included to monitor state-of-charge (SOC) conditions, and a device power adjustment scheme is proposed to provide SOC-based maximum 28% power reduced optimal operation of high-resolution and low-power. The power-sensor integrated circuits were fabricated in a 0.18-μm CMOS process, and the proposed schemes were experimentally verified. For system-level feasibility, a patch-type device prototype was manufactured, and both power and bio-signal interfaces were functionally demonstrated.

A 4.5 μW Miniaturized 3-Channel Wireless Intra-Cardiac Acquisition System

This article presents a chip designed for wireless intra-cardiac monitoring systems. The design consists of a three-channel analog front-end, a pulse-width modulator featuring output-frequency offset and temperature calibration, and inductive data telemetry. By employing a resistance boosting technique in the instrumentation amplifier feedback, the pseudo-resistor exhibits lower non-linearity, leading to a total harmonic distortion of below 0.1%. Furthermore, the boosting technique enhances the feedback resistance, leading to a reduction in the size of the feedback capacitor and, consequently, the overall size. To make the modulator's output frequency resilient to temperature and process changes, coarse and fine-tuning algorithms are used. The front-end channel is capable of extracting the intra-cardiac signal with an effective number of bits of 8.9, while exhibiting an input-referred noise of less than 2.7 μVrms, and consuming 200 nW per channel. The front-end output is encoded by an ASK-PWM modulator, which drives an on-chip transmitter at 13.56 MHz. The proposed System-on-Chip (SoC) is fabricated in a 0.18 μm standard CMOS technology and consumes 4.5 μW while occupying 1.125 mm2.

A 1.5 mm2 4-Channel EEG/BIOZ Acquisition ASIC With 15.2-Bit 3-Step ADC Based on a Signal-Dependent Low-Power Strategy

This article presents a multichannel EEG/BIOZ acquisition application specific integrated circuit (ASIC) with 4 EEG channels and a BIOZ channel, a switch resistor low-pass filter (SR-LPF). Each EEG channel includes a frontend, and a 4-channel multiplexed analog-to-digital converter (ADC), while the BIOZ channel features a pseudo sine current generator and a pair of readout paths with multiplexed SR-LPF and ADC. The ASIC is designed for size and power minimization, utilizing a 3-step ADC with a novel signal-dependent low power strategy. The proposed ADC operates at a sampling rate of 1600 S/s with a resolution of 15.2 bits, occupying only 0.093 mm2. With the help of the proposed signal-dependent low-power strategy, the ADC's power dissipation drops from 32.2 μW to 26.4 μW, resulting in an 18% efficiency improvement without performance degradation. Moreover, the EEG channels deliver excellent noise performance with a NEF of 7.56 and 27.8 nV/√Hz at the expense of 0.16 mm2 per channel. In BIOZ measurement, a 5-bit programmable current source is used to generate pseudo sine injection current ranging from 0 to 22 μApp, and the detection sensitivity reaches 2.4 mΩ/√Hz. Finally, the presented multichannel EEG/BIOZ acquisition ASIC has a compact active area of 1.5 mm2 in an 180nm CMOS technology.

A Low-Power High Input Range PPG Readout Amplifier with a Current Buffer Input. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023;2023:1-4. [PMID: 38083121 DOI: 10.1109/embc40787.2023.10340264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

This paper presents ultra-low power photoplethysmography (PPG) readout circuits. The proposed system architecture uses a current buffer between the photodiode (PD) and the transimpedance amplifier (TIA) to isolate the large parasitic capacitance of the PD leading to improves the power consumption of the TIA. A class AB topology is exploited at the output of the amplifier, which allows for increased drive capability without the use of auxiliary circuits. The maximum input current range of the TIA is 160 µA, so the large DC current of the input signal does not saturate the circuit. In the LED driver circuit, by varying the duty cycle of a pulse wave modulation (PWM) signal, the ON and OFF times of the circuits. The amplifier and LED driver are manufactured in the 130 nm TSMC CMOS process. The power consumption of the circuits with a duty cycle of 1% is 3.28 µW (at VDD = 1.2V).Clinical Relevance- Vital signs are becoming a very important research topic due to the recent prevalence of COVID-19 and other respiratory diseases. This research aims to develop and interface circuits to monitor vital signs including blood pressure, heart rate, and respiratory rate to study respiratory disease, drug safety, and efficacy.

Portable, wearable and implantable artificial kidney systems: needs, opportunities and challenges

Haemodialysis is life sustaining but expensive, provides limited removal of uraemic solutes, is associated with poor patient quality of life and has a large carbon footprint. Innovative dialysis technologies such as portable, wearable and implantable artificial kidney systems are being developed with the aim of addressing these issues and improving patient care. An important challenge for these technologies is the need for continuous regeneration of a small volume of dialysate. Dialysate recycling systems based on sorbents have great potential for such regeneration. Novel dialysis membranes composed of polymeric or inorganic materials are being developed to improve the removal of a broad range of uraemic toxins, with low levels of membrane fouling compared with currently available synthetic membranes. To achieve more complete therapy and provide important biological functions, these novel membranes could be combined with bioartificial kidneys, which consist of artificial membranes combined with kidney cells. Implementation of these systems will require robust cell sourcing; cell culture facilities annexed to dialysis centres; large-scale, low-cost production; and quality control measures. These challenges are not trivial, and global initiatives involving all relevant stakeholders, including academics, industrialists, medical professionals and patients with kidney disease, are required to achieve important technological breakthroughs.

Analog Gated Recurrent Unit Neural Network for Detecting Chewing Events

We present a novel gated recurrent neural network to detect when a person is chewing on food. We implemented the neural network as a custom analog integrated circuit in a 0.18 μm CMOS technology. The neural network was trained on 6.4 hours of data collected from a contact microphone that was mounted on volunteers' mastoid bones. When tested on 1.6 hours of previously-unseen data, the analog neural network identified chewing events at a 24-second time resolution. It achieved a recall of 91% and an F1-score of 94% while consuming 1.1 μW of power. A system for detecting whole eating episodes-like meals and snacks-that is based on the novel analog neural network consumes an estimated 18.8 μW of power.

A Multilevel Synchronized Optical Pulsed Modulation for High Efficiency Biotelemetry

The paper describes the design, implementation, and characterization of a novel multilevel synchronized pulse position modulation paradigm for high efficiency optical biotelemetry links. The entire optoelectronic architecture has been designed with the aim to improve the efficiency of the data transmission and decrease the overall power consumption that are key factors for the fabrication of implantable and wearable medical devices. By employing specially designed digital architectures, the proposed modulation technique automatically transmits more than one bit per symbol together with the reference clock signal enabling the decoding process of the received coded data. In the present case, the paper demonstrates the capability of the modulation technique to transmit symbols composed by 3 and 4 bits. This has been achieved by developing a prototype of an optical biotelemetry system implemented on an FPGA board that, making use of 500 ps laser pulses, operates under the following two working conditions: (i) 40 MHz clock signal corresponding to a baud rate of 40 Mega symbol per second for symbols composed by 3 bits; (ii) 30 MHz clock signal corresponding to a baud rate of 30 Mega symbol per second for symbols composed by 4 bits. Thus, for both these two configurations the transmission data rate is 120 Mbps and the measured BER was lower than 10^-10. Finally, the power consumption was found to be 1.95 and 1.8 mW and the resulting energy efficiencies were 16.25 and 15 pJ/bit for transmitted symbols composed by 3 and 4 bits/symbol, respectively.

An 18.6-$\mu $W/Ch TDM-Based 8-Channel Noncontact ECG Recording IC With Common-Mode Interference Suppression

This paper presents an 8-channel electrocardiogram (ECG) monitoring integrated circuit (IC) controlled by time-division multiplexing (TDM). The proposed TDM compensates the electrode DC offsets by forming an individual discrete-time feedback loop per channel while sharing an analog frontend. This enables a chopping-free open-loop amplification, achieving a high input impedance suitable for a noncontact ECG monitoring. In addition, a common-mode interference (CMI) cancellation scheme is also introduced in the proposed TDM schedule for the realization of a pseudo-driven-right leg (DRL) in a multichannel environment. The designed system is implemented in 180 nm CMOS. The chip dissipates 18.6 μW/channel including the power consumption by ADC. It shows the total-CMRR of 100 dB against CMI voltage swing up to 20 VPP. The chip is verified in noncontact 8-channel ECG using conventional passive electrodes.

A Sparse Sampling Sensor Front-End IC for Low Power Continuous SpO 2 & HR Monitoring

Photoplethysmography (PPG) is an attractive method to acquire vital signs such as heart rate and blood oxygenation and is frequently used in clinical and at-home settings. Continuous operation of health monitoring devices demands a low power sensor that does not restrict the device battery life. Silicon photodiodes (PD) and LEDs are commonly used as interface devices in PPG sensors; however, using of flexible organic devices can enhance the sensor conformality and reduce the cost of fabrication. In most PPG sensors, most of system power consumption is concentrated in powering LEDs, traditionally consuming mWs. Using organic devices further increases this power demand since these devices exhibit larger parasitic capacitances and typically need higher drive voltages.This work presents a sensor IC for continuous SpO ₂ and HR monitoring that features an on-chip reconstruction-free sparse sampling algorithm to reduce the overall system power consumption by  ∼ 70% while maintaining the accuracy of the output information. The designed frontend is compatible with a wide range of devices from silicon PDs to organic PDs with parasitic capacitances up to 10 nF. Implemented in a 40 nm HV CMOS process, the chip occupies 2.43 mm ² and consumes 49.7 μW and 15.2 μW of power in continuous and sparse sampling modes respectively. The performance of the sensor IC has been verified in vivo with both types of devices and the results are compared against a clinical grade reference. Less than 1 bpm and 1% mean absolute errors were achieved in both continuous and sparse modes of operation.

Modeling Energy Aware Photoplethysmography for Personalized Healthcare Applications

The rise of wearable health monitoring has largely incorporated photoplethysmography (PPG), an optical sensing modality, to determine heart rate and blood oxygen saturation metrics by reflecting light through a user's skin. Due to its optical nature, this sensing method is strongly impacted by the skin type, body mass index (BMI), and general physiological composition of the user. In the context of self-powering, there is a need for these devices to consume ultra-low power, to not be dependent on batteries and regular charging, enabling continuous monitoring. This paper presents a novel PPG sensing model for both a custom, ultra-low power (ULP) AFE and the Texas Instruments (TI) AFE4404 which is used to demonstrate the design tradeoffs between system power and SNR. The models also incorporate a novel human skin reflectance component to analyze the effect of the user's skin phototype and BMI on these tradeoffs with the goal of demonstrating inclusive, accurate ULP PPG sensing. Measured results on both devices from 23 participants are included to emphasize the limited design space for enabling self-powered, continuous monitoring wearables.

ECG Arrhythmia Classification on an Ultra-Low-Power Microcontroller

Wearable biomedical systems allow doctors to continuously monitor their patients over longer periods, which is especially useful to detect rarely occurring events such as cardiac arrhythmias. Recent monitoring systems often embed signal processing capabilities to directly identify events and reduce the amount of data. This work is the first to document a complete beat-to-beat arrhythmia classification system implemented on a custom ultra-low-power microcontroller. It includes a single-channel analog front-end (AFE) circuit for electrocardiogram (ECG) signal acquisition, and a digital back-end (DBE) processor to execute the support vector machine (SVM) classification software with a Cortex-M4 CPU. The low-noise instrumentation amplifier in the AFE consumes 1.4 μW and has an input-referred noise of 0.9 μV RMS. The all-digital time-based ADC achieves 10-bit effective resolution over a 250-Hz bandwidth with an area of only 900 μm ². The classification software reaches a sensitivity of 82.6% and 88.9% for supraventricular and ventricular arrhythmias respectively on the MIT-BIH arrhythmia database. The proposed system has been prototyped on the SleepRider SoC, a 28-nm fully-depleted silicon on insulator (FD-SOI) 3.1-mm ² chip. It consumes 13.1 μW on average from a 1.8-V supply.

On-Chip Sinusoidal Signal Generators for Electrical Impedance Spectroscopy: Methodological Review

This paper reviews architectures and circuit implementations of on-chip sinusoidal signal generators (SSGs) for electrical impedance spectroscopy (EIS) applications. In recent years, there have been increasing interests in on-chip EIS systems, which measure a target material's impedance spectrum over a frequency range. The on-chip implementation allows EIS systems to have low power and small form factor, enabling various biomedical applications. One of the key building blocks of on-chip EIS systems is on-chip SSG, which determines the frequency range and the analysis precision of the whole EIS system. On-chip SSGs are generally required to have high linearity, wide frequency range, and high power and area efficiency. They are typically composed of three stages in general: waveform generation, linearity enhancement, and current injection. First, a sinusoidal waveform should be generated in SSGs. The generated waveform's frequency should be accurately adjustable over a wide range. The firstly generated waveform may not be perfectly linear, including unwanted harmonics. In the following linearity-enhancement step, these harmonics are attenuated by using filters typically. As the linearity of the waveform is improved, the precision of the EIS system gets ensured. Lastly, the filtered voltage waveform is now converted to a current by a current driver. Then, the current sinusoidal signal is injected into the target impedance. This review discusses the principles, advantages, and disadvantages of various techniques applied to each step in state-of-the-art on-chip SSGs. In addition, state-of-the-art designs are compared and summarized.

A New RF Energy Harvesting System Based on Two Architectures to Enhance the DC Output Voltage for WSN Feeding

In this paper, a new RF Energy Harvesting (RF-EH) system for Wireless Sensor Network (WSN) feeding is proposed. It is based on two different monitored architectures using switch circuits controlled by the input powers. One architecture is more adapted to high input powers and the other to low input powers. The two different architectures and the system are designed and realized on Teflon glass substrate with a relative permittivity of 2.1 and thickness of 0.67 mm. They are tested separately as a function of the distance from the relay antenna. A new multiband antenna with a size of 40 × 30 mm2 is used for both architectures and the system. The measured antenna gains are 2.7 dB, 2.9 dB, and 2.55 dB for the frequencies of 1.8 GHz, 2.1 GHz, and 2.66 GHz corresponding to the mobile communication networks, respectively. The rectifier consists of two Schottky diodes forming a full-wave rectifier and voltage doubler. The maximum measured RF-to-DC conversion efficiency is 71.5%. The proposed RF-EH system provides a maximum DC output voltage of 5.6 V and 3.15 V for an open and 2 kΩ resistance load, respectively.

Wireless Photometry Prototype for Tri-Color Excitation and Multi-Region Recording

Visualizing neuronal activation and neurotransmitter release by using fluorescent sensors is increasingly popular. The main drawback of contemporary multi-color or multi-region fiber photometry systems is the tethered structure that prevents the free movement of the animals. Although wireless photometry devices exist, a review of literature has shown that these devices can only optically stimulate or excite with a single wavelength simultaneously, and the lifetime of the battery is short. To tackle this limitation, we present a prototype for implementing a fully wireless photometry system with multi-color and multi-region functions. This paper introduces an integrated circuit (IC) prototype fabricated in TSMC 180 nm CMOS process technology. The prototype includes 3-channel optical excitation, 2-channel optical recording, wireless power transfer, and wireless data telemetry blocks. The recording front end has an average gain of 107 dB and consumes 620 μW of power. The light-emitting diode (LED) driver block provides a peak current of 20 mA for optical excitation. The rectifier, the core of the wireless power transmission, operates with 63% power conversion efficiency at 13.56 MHz and a maximum of 87% at 2 MHz. The system is validated in a laboratory bench test environment and compared with state-of-the-art technologies. The optical excitation and recording front end and the wireless power transfer circuit evaluated in this paper will form the basis for a future miniaturized final device with a shank that can be used in in vivo experiments.

A 1.8 V Low-Power Low-Noise High Tunable Gain TIA for CMOS Integrated Optoelectronic Biomedical Applications

This paper reports on a novel solution for a transimpedance amplifier (TIA) specifically designed as an analog conditioning circuit for low-voltage, low-power, wearable, portable and implantable optoelectronic integrated sensor systems in biomedical applications. The growing use of sensors in all fields of industry, biomedicine, agriculture, environment analysis, workplace security and safety, needs the development of small sensors with a reduced number of electronic components to be easily integrated in the standard CMOS technology. Especially in biomedicine applications, reduced size sensor systems with small power consumption are of paramount importance to make them non-invasive, comfortable tools for patients to be continuously monitored even with personalized therapeutics and/or that can find autonomous level of life using prosthetics. The proposed new TIA architecture has been designed at transistor level in TSMC 0.18 μm standard CMOS technology with the aim to operate with nanoampere input pulsed currents that can be generated, for example, by Si photodiodes in optical sensor systems. The designed solution operates at 1.8 V single supply voltage with a maximum power consumption of about 36.1 μW and provides a high variable gain up to about 124 dBΩ (with fine- and coarse-tuning capabilities) showing wide bandwidth up to about 1.15 MHz and low-noise characteristics with a minimum noise floor level down to about 0.39 pA/Hz. The overall circuit is described in detail, and its main characteristics and performances have been analyzed by performing accurate post-layout simulations.

A Multimodal Neural-Recording IC With Reconfigurable Analog Front-Ends for Improved Availability and Usability for Recording Channels

In this work, we present an 8-channel reconfigurable multimodal neural-recording IC, which provides improved availability and usability of recording channels in various experiment scenarios. Each recording channel changes its configuration depending on whether the channel is assigned to record voltage or current signal. As a result, although the total number of channels is fixed by design, the channels utilized for voltage and current recording can be set freely and optimally for given experiment targets, scenarios, and circumstances, maximizing the availability and usability of recording channels.The proposed concept was demonstrated by fabricating the IC using a standard 180-nm CMOS process.Using the IC, we successfully performed an in vivo experiment from the hippocampal area of a mouse brain. The measured input noise of the reconfigurable front-end is 4.75 μV_rms at voltage-recording mode and 7.4 pA_rms at current-recording mode while consuming 5.72 μW/channel.

High Dynamic Range Photocurrent Sensory Circuit with a Multi-Transistor Background Light Cancellation Loop for Photoplethysmography Sensing

In this article, we present a new photocurrent sensory circuit with a three-transistor background light cancellation. We describe our innovative photocurrent sensor-based blood pressure measuring device using a resistor-based current-to-voltage converter with a background light cancellation (BLC) loop. The photocurrent sensor is implemented using 0.35 μm standard CMOS technology and has zero average power consumption. The post-layout simulation for the photocurrent sensor shows a 1.3 MΩ transimpedance gain, a referred input noise current of 11 pA, and can reject a DC photocurrent up to 200 μA. This high DC rejection has been achieved due to the newly proposed multi-transistor BLC loop integrated with the sensor.

Towards a Self-Powered ECG and PPG Sensing Wearable Device

This paper presents a multifunctional sensor interface system-on-chip (SoC) for developing self-powered Electrocardiography (ECG) and Photoplethysmography (PPG) sensing wearable devices. The proposed SoC design consists of switch-capacitor-based LED driver and analog front-end (AFE) for PPG sensing, ECG sensing AFE, and power management unit for energy harvesting from Thermoelectric Generator (TEG), all integrated on a 2×2.5 mm² chip fabricated in 0.18μm standard CMOS process. We have performed post-layout simulation to verify the functionality and performance of the SoC. The LED driver employs the switch-capacitor-based architecture, which charges a storage capacitor up to 2.1 V and discharges accumulated charge to pass instantaneous current up to 40 mA through a selected LED. The PPG AFE converts the resulting photodiode (PD) current to voltage output with adjustable gain of 114-120 dBΩ and input-referred noise of 119 pA_RMS within 0.4 Hz-10 kHz. The ECG AFE provides adjustable mid-band gain of 47-63 dB, low-cut frequency of 1.5-6.3 Hz, and input-referred noise of 7.83 µV_RMS within 1.5 Hz- 1.2 kHz to amplify/filter the recorded ECG signals. The power management unit is able to perform sufficient energy harvesting with the TEG output voltage as low as 350 mV.

Multi-Level Optimization of an Ultra-Low Power BrainWave System for Non-Convulsive Seizure Detection

We present a systematic evaluation and optimization of a complex bio-medical signal processing application on the BrainWave prototype system, targeted towards ambulatory EEG monitoring within a tiny power budget of 1 mW. The considered BrainWave processor is completely programmable, while maintaining energy-efficiency by means of a Coarse-Grained Reconfigurable Array (CGRA). This is demonstrated through the mapping and evaluation of a state-of-the-art non-convulsive epileptic seizure detection algorithm, while ensuring real-time operation and seizure detection accuracy. Exploiting the CGRA leads to an energy reduction of 73.1%, compared to a highly tuned software implementation (SW-only). A total of 9 complex kernels were benchmarked on the CGRA, resulting in an average 4.7 × speedup and average 4.4 × energy savings over highly tuned SW-only implementations. The BrainWave processor is implemented in 28-nm FDSOI technology with 80 kB of Foundry-provided SRAM. By exploiting near-threshold computing for the logic and voltage-stacking to minimize on-chip voltage-conversion overhead, additional 15.2% and 19.5% energy savings are obtained, respectively. At the Minimum-Energy-Point (MEP) (223 μW, 8 MHz) we report a measured state-of-the-art 90.6% system conversion efficiency, while executing the epileptic seizure detection in real-time.

MBioTracker: Multimodal Self-Aware Bio-Monitoring Wearable System for Online Workload Detection

Cognitive workload affects operators' performance principally in high-risk or time-demanding situations and when multitasking is required. An online cognitive workload monitoring system can provide valuable inputs to decision-making instances, such as the operator's state of mind and resulting performance. Therefore, it can allow potential adaptive support to the operator. This work presents a new design of a wearable embedded system for online cognitive workload monitoring. This new wearable system consists of, on the hardware side, a multi-channel physiological signals acquisition (respiration cycles, heart rate, skin temperature, and pulse waveform) and a low-power processing platform. Further, on the software side, our wearable embedded system includes a novel energy-aware bio-signal processing algorithm. We also use the concept of application self-awareness to enable energy-scalable embedded machine learning algorithms and methods for online subjects' cognitive workload monitoring. Our results show that this new wearable system can continuously monitor multiple bio-signals, compute their key features, and provide reliable detection of high and low cognitive workload levels with a time resolution of 1 minute and a battery lifetime of 14.58 h in our experimental conditions. It achieves a detection accuracy of 76.6% (2.6% lower than analogous offline computer-based analysis) with a sensitivity of 77.04% and a specificity of 81.75%, on a simulated drone rescue mission task. Moreover, by applying our self-aware monitoring to exploit different energy-scalable modes, we can increase battery lifetime by 51.6% (up to 22.11 hours) while incurring an insignificant accuracy loss of 1.07%.

CAFS: Cost-Aware Features Selection Method for Multimodal Stress Monitoring on Wearable Devices

OBJECTIVE

Today, stress monitoring on wearable devices is challenged by the tension between high-detection accuracy and battery lifetime driven by multimodal data acquisition and processing. Limited research has addressed the classification cost on multimodal wearable sensors, particularly when the features are cost-dependent. Thus, we design a Cost-Aware Feature Selection (CAFS) methodology that trades-off between prediction-power and energy-cost for multimodal stress monitoring.

METHODS

CAFS selects the most important features under different energy-constraints, which allows us to obtain energy-scalable stress monitoring models. We further propose a self-aware stress monitoring method that intelligently switches among the energy-scalable models, reducing energy consumption.

RESULTS

Using CAFS methodology on experimental data and simulation, we reduce the energy-cost of the stress model designed without energy constrains up to 94.37%. We obtain 90.98% and 95.74% as the best accuracy and confidence values, respectively, on unseen data, outperforming state-of-the-art studies. Analyzing our interpretable and energy-scalable models, we showed that simple models that use only heart rate (HR) or skin conductance level (SCL), confidently predict stress for HR >93.30 BPM and non-stress for SCL <6.42S, but, outside these values, a multimodal model using respiration and pulse waves features is needed for confident stress classification. Our self-aware stress monitoring proposal saves10x energy and provides 88.72% of ac-curacy on unseen data.

CONCLUSION

We propose a comprehensive solution for the design of cost-aware stress monitoring addressing the problem of selecting an optimal feature subset considering their cost-dependency and cost-constrains. Significant: Our design framework enables long-term, confident, and accurate stress monitoring on wearable devices.

Review of wearable technologies and machine learning methodologies for systematic detection of mild traumatic brain injuries

Mild traumatic brain injuries (mTBIs) are the most common type of brain injury. Timely diagnosis of mTBI is crucial in making 'go/no-go' decision in order to prevent repeated injury, avoid strenuous activities which may prolong recovery, and assure capabilities of high-level performance of the subject. If undiagnosed, mTBI may lead to various short- and long-term abnormalities, which include, but are not limited to impaired cognitive function, fatigue, depression, irritability, and headaches. Existing screening and diagnostic tools to detect acute andearly-stagemTBIs have insufficient sensitivity and specificity. This results in uncertainty in clinical decision-making regarding diagnosis and returning to activity or requiring further medical treatment. Therefore, it is important to identify relevant physiological biomarkers that can be integrated into a mutually complementary set and provide a combination of data modalities for improved on-site diagnostic sensitivity of mTBI. In recent years, the processing power, signal fidelity, and the number of recording channels and modalities of wearable healthcare devices have improved tremendously and generated an enormous amount of data. During the same period, there have been incredible advances in machine learning tools and data processing methodologies. These achievements are enabling clinicians and engineers to develop and implement multiparametric high-precision diagnostic tools for mTBI. In this review, we first assess clinical challenges in the diagnosis of acute mTBI, and then consider recording modalities and hardware implementation of various sensing technologies used to assess physiological biomarkers that may be related to mTBI. Finally, we discuss the state of the art in machine learning-based detection of mTBI and consider how a more diverse list of quantitative physiological biomarker features may improve current data-driven approaches in providing mTBI patients timely diagnosis and treatment.

A Noninvasive Miniaturized Transcutaneous Oxygen Monitor

Transcutaneous monitoring is a noninvasive method to continuously measure the partial pressures of oxygen and carbon dioxide that diffuse through the skin and correlate closely with changes in blood gases. However, the contemporary commercially available electrochemical-based technology requires a heating mechanism and a bulky, corded, and expensive sensing unit. This study aims to demonstrate a prototype noninvasive, miniaturized monitor that uses luminescence-based technology to measure the partial pressure of transcutaneous oxygen, a surrogate of the partial pressure of arterial oxygen. To be able to build a robust measurement system, we conducted experiments to understand the temperature and humidity dependence of oxygen-sensitive platinum-porphyrin films. We performed a detailed analysis of both intensity and lifetime measurement techniques. To verify the performance, we tested the prototype in a small ex-vivo experiment involving three healthy human volunteers. We measured variations in the partial pressure of transcutaneous oxygen values due to pressure-induced arterial and venous occlusions on the volunteers' fingertips. The system resolves changes in the partial pressure of oxygen from 0 to 418 mmHg in the lab bench-top testing, covering the medically relevant range of 50-150 mmHg. Under fixed humidity, temperature, and the partial pressure of oxygen conditions, the sensor shows a 2% drift over 60 hours. The prototype consumes 9 mW of power from a 2.2 V external DC power supply.

Simultaneous Voltage and Current Measurement Instrumentation Amplifier for ECG and PPG Monitoring

An instrumentation amplifier (IA) capable of sensing both voltage and current at the same time has been introduced and applied to electrocardiogram (ECG) and photoplethysmogram (PPG) measurements for cardiovascular health monitoring applications. The proposed IA can switch between the voltage and current sensing configurations in a time–division manner faster than the ECG and PPG bandwidths. The application-specific integrated circuit (ASIC) of the proposed circuit design was implemented using 180 nm CMOS fabrication technology. Input-referred voltage noise and current noise were measured as 3.9 µVrms and 172 pArms, respectively, and power consumption was measured as 34.9 µA. In the current sensing configuration, a current noise reduction technique is applied, which was confirmed to be a 25 times improvement over the previous version. Using a single IA, ECG and PPG can be monitored in the form of separated ECG and PPG signals. In addition, for the first time, a merged ECG/PPG signal is acquired, which has features of both ECG and PPG peaks.

Noninvasive Assessment of Neuromechanical Coupling and Mechanical Efficiency of Parasternal Intercostal Muscle during Inspiratory Threshold Loading

This study aims to investigate noninvasive indices of neuromechanical coupling (NMC) and mechanical efficiency (MEff) of parasternal intercostal muscles. Gold standard assessment of diaphragm NMC requires using invasive techniques, limiting the utility of this procedure. Noninvasive NMC indices of parasternal intercostal muscles can be calculated using surface mechanomyography (sMMG_para) and electromyography (sEMG_para). However, the use of sMMG_para as an inspiratory muscle mechanical output measure, and the relationships between sMMG_para, sEMG_para, and simultaneous invasive and noninvasive pressure measurements have not previously been evaluated. sEMG_para, sMMG_para, and both invasive and noninvasive measurements of pressures were recorded in twelve healthy subjects during an inspiratory loading protocol. The ratios of sMMG_para to sEMG_para, which provided muscle-specific noninvasive NMC indices of parasternal intercostal muscles, showed nonsignificant changes with increasing load, since the relationships between sMMG_para and sEMG_para were linear (R² = 0.85 (0.75-0.9)). The ratios of mouth pressure (P_mo) to sEMG_para and sMMG_para were also proposed as noninvasive indices of parasternal intercostal muscle NMC and MEff, respectively. These indices, similar to the analogous indices calculated using invasive transdiaphragmatic and esophageal pressures, showed nonsignificant changes during threshold loading, since the relationships between P_mo and both sEMG_para (R² = 0.84 (0.77-0.93)) and sMMG_para (R² = 0.89 (0.85-0.91)) were linear. The proposed noninvasive NMC and MEff indices of parasternal intercostal muscles may be of potential clinical value, particularly for the regular assessment of patients with disordered respiratory mechanics using noninvasive wearable and wireless devices.

On the potential of wearable bioimpedance for longitudinal fluid monitoring in end-stage kidney disease

Bioimpedance spectroscopy (BIS) has proven to be a promising non-invasive technique for fluid monitoring in HD patients. While current BIS-based monitoring of pre- and post-dialysis fluid status utilizes benchtop devices, designed for intramural use, advancements in micro-electronics have enabled the development of wearable bioimpedance systems. Wearable systems meanwhile can offer a similar frequency range for current injection as commercially available benchtop devices. This opens opportunities for unobtrusive longitudinal fluid status monitoring, including transcellular fluid shifts, with the ultimate goal of improving fluid management, thereby lowering mortality and improving quality of life for HD patients. Ultra-miniaturized wearable devices can also offer simultaneous acquisition of multiple other parameters, including hemodynamic parameters. Combination of wearable BIS and additional longitudinal multiparametric data may aid in the prevention of both hemodynamic instability as well as fluid overload. The opportunity to also acquire data during interdialytic periods using wearable devices likely will give novel pathophysiological insights and the development of smart (predicting) algorithms could contribute to personalizing dialysis schemes and ultimately to autonomous (nocturnal) home dialysis. This review provides an overview of current research regarding wearable bioimpedance, with special attention to applications in ESKD patients. Furthermore, we present an outlook on the future use of wearable bioimpedance within dialysis practice.

A 280 μW, 108 dB DR PPG-Readout IC With Reconfigurable, 2nd-Order, Incremental ΔΣM Front-End for Direct Light-to-Digital Conversion

This paper reports on a low-power readout IC (ROIC) for high-fidelity recording of the photoplethysmogram (PPG) signal. The system comprises a highly reconfigurable, continuous-time, second-order, incremental delta-sigma modulator (I-ΔΣM) as a light-to-digital converter (LDC), a 2-channel 10b light-emitting diode (LED) driver, and an integrated digital signal processing (DSP) unit. The LDC operation in intermittent conversion phases coupled with digital assistance by the DSP unit allow signal-aware, on-the-fly cancellation of the dc and ambient light-induced components of the photodiode current for more efficient use of the full-scale input range for recording of the small-amplitude, ac, PPG signal. Fabricated in TSMC 0.18 μm 1P/6M CMOS, the PPG ROIC exhibits a high dynamic range of 108.2 dB and dissipates on average 15.7 μW from 1.5 V in the LDC and 264 μW from 2.5 V in one LED (and its driver), while operating at a pulse repetition frequency of 250 Hz and 3.2% duty cycling. The overall functionality of the ROIC is also demonstrated by high-fidelity recording of the PPG signal from a human subject fingertip in the presence of both natural light and indoor light sources of 60 Hz.

On-Device Reliability Assessment and Prediction of Missing Photoplethysmographic Data Using Deep Neural Networks

Photoplethysmographic (PPG) measurements from ambulatory subjects may suffer from unreliability due to body movements and missing data segments due to loosening of sensor. This paper describes an on-device reliability assessment from PPG measurements using a stack denoising autoencoder (SDAE) and multilayer perceptron neural network (MLPNN). The missing segments were predicted by a personalized convolutional neural network (CNN) and long-short term memory (LSTM) model using a short history of the same channel data. Forty sets of volunteers' data, consisting of equal share of healthy and cardiovascular subjects were used for validation and testing. The PPG reliability assessment model (PRAM) achieved over 95% accuracy for correctly identifying acceptable PPG beats out of total 5000 using expert annotated data. Disagreement with experts' annotation was nearly 3.5%. The missing segment prediction model (MSPM) achieved a root mean square error (RMSE) of 0.22, and mean absolute error (MAE) of 0.11 for 40 missing beats prediction using only four beat history from the same channel PPG. The two models were integrated in a standalone device based on quad-core ARM Cortex-A53, 1.2 GHz, with 1 GB RAM, with 130 MB memory requirement and latency ∼0.35 s per beat prediction with a 30 s frame. The present method also provides improved performance with published works on PPG quality assessment and missing data prediction using two public datasets, CinC and MIMIC-II under PhysioNet.

Respiratory Monitoring: Current State of the Art and Future Roads

In this article, we present current methodologies, available technologies, and demands for monitoring various respiratory parameters. We discuss the importance of noninvasive techniques for remote and continuous monitoring and challenges involved in the current "smart and connected health" era. We conducted an extensive literature review on the medical significance of monitoring respiratory vital parameters, along with the current methods and solutions with their respective advantages and disadvantages. We discuss the challenges of developing a noninvasive, wearable, wireless system that continuously monitors respiration parameters and opportunities in the field and then determines the requirements of a state-of-the-art system. Noninvasive techniques provide a significant amount of medical information for a continuous patient monitoring system. Contact methods offer more advantages than non-contact methods; however, reducing the size and power of contact methods is critical for enabling a wearable, wireless medical monitoring system. Continuous and accurate remote monitoring, along with other physiological data, can help caregivers improve the quality of care and allow patients greater freedom outside the hospital. Such monitoring systems could lead to highly tailored treatment plans, shorten patient stays at medical facilities, and reduce the cost of treatment.

Wearable health devices and personal area networks: can they improve outcomes in haemodialysis patients?

Digitization of healthcare will be a major innovation driver in the coming decade. Also, enabled by technological advancements and electronics miniaturization, wearable health device (WHD) applications are expected to grow exponentially. This, in turn, may make 4P medicine (predictive, precise, preventive and personalized) a more attainable goal within dialysis patient care. This article discusses different use cases where WHD could be of relevance for dialysis patient care, i.e. measurement of heart rate, arrhythmia detection, blood pressure, hyperkalaemia, fluid overload and physical activity. After adequate validation of the different WHD in this specific population, data obtained from WHD could form part of a body area network (BAN), which could serve different purposes such as feedback on actionable parameters like physical inactivity, fluid overload, danger signalling or event prediction. For a BAN to become clinical reality, not only must technical issues, cybersecurity and data privacy be addressed, but also adequate models based on artificial intelligence and mathematical analysis need to be developed for signal optimization, data representation, data reliability labelling and interpretation. Moreover, the potential of WHD and BAN can only be fulfilled if they are part of a transformative healthcare system with a shared responsibility between patients, healthcare providers and the payors, using a step-up approach that may include digital assistants and dedicated ‘digital clinics’. The coming decade will be critical in observing how these developments will impact and transform dialysis patient care and will undoubtedly ask for an increased ‘digital literacy’ for all those implicated in their care.

Combining Bioimpedance and Myographic Signals for the Assessment of COPD During Loaded Breathing

Chronic Obstructive Pulmonary Disease (COPD) is one of the most common chronic conditions. The current assessment of COPD requires a maximal maneuver during a spirometry test to quantify airflow limitations of patients. Other less invasive measurements such as thoracic bioimpedance and myographic signals have been studied as an alternative to classical methods as they provide information about respiration. Particularly, strong correlations have been shown between thoracic bioimpedance and respiratory volume. The main objective of this study is to investigate bioimpedance and its combination with myographic parameters in COPD patients to assess the applicability in respiratory disease monitoring. We measured bioimpedance, surface electromyography and surface mechanomyography in forty-three COPD patients during an incremental inspiratory threshold loading protocol. We introduced two novel features that can be used to assess COPD condition derived from the variation of bioimpedance and the electrical and mechanical activity during each respiratory cycle. These features demonstrate significant differences between mild and severe patients, indicating a lower inspiratory contribution of the inspiratory muscles to global respiratory ventilation in the severest COPD patients. In conclusion, the combination of bioimpedance and myographic signals provides useful indices to noninvasively assess the breathing of COPD patients.

Binary CorNET: Accelerator for HR Estimation From Wrist-PPG

Research on heart rate (HR) estimation using wrist-worn photoplethysmography (PPG) sensors have progressed rapidly owing to the prominence of commercial sensing modules, used widely for lifestyle monitoring. Reported methodologies have been fairly successful in mitigating the effect of motion artifacts (MA) in ambulatory environment for HR estimation. Recently, a learning framework, CorNET, employing two-layer convolution neural networks (CNN) and two-layer long short-term network (LSTM) was successfully reported for estimating HR from MA-induced PPG signals. However, such a network topology with large number of parameters presents a challenge, towards low-complexity hardware implementation aimed at on-node processing. In this paper, we demonstrate a fully binarized network (bCorNET) topology and its corresponding algorithm-to-architecture mapping and energy-efficient implementation for HR estimation. The proposed framework achieves a MAE of 6.67 ± 5.49 bpm when evaluated on 22 IEEE SPC subjects. The design, synthesized with ST65 nm technology library achieving 3 GOPS @ 1 MHz, consumes 56.1 μJ per window with occupied 1634K NAND2 equivalent cell area and had a latency of 32 ms when estimating HR every 2 s from PPG signals.

ECG Classification Algorithm Based on STDP and R-STDP Neural Networks for Real-Time Monitoring on Ultra Low-Power Personal Wearable Devices

This paper presents a novel ECG classification algorithm for inclusion as part of real-time cardiac monitoring systems in ultra low-power wearable devices. The proposed solution is based on spiking neural networks which are the third generation of neural networks. In specific, we employ spike-timing dependent plasticity (STDP), and reward-modulated STDP (R-STDP), in which the model weights are trained according to the timings of spike signals, and reward or punishment signals. Experiments show that the proposed solution is suitable for real-time operation, achieves comparable accuracy with respect to previous methods, and more importantly, its energy consumption in real-time classification of ECG signals is significantly smaller. In specific, energy consumption is 1.78  μJ per beat, which is 2 to 9 orders of magnitude smaller than previous neural network based ECG classification methods.