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 Wirelessly Powered Scattered Neural Recording Wearable System.

This paper introduces a wirelessly powered scattered neural recording wearable system that can facilitate continuous, untethered, and long-term electroencephalogram (EEG) recording. The proposed system, including 32 standalone EEG recording devices and a central controller, is incorporated in a wearable form factor. The standalone devices are sparsely distributed on the scalp, allowing for flexible placement and varying quantities to provide extensive spatial coverage and scalability. Each standalone device featuring a low-power EEG recording application-specific integrated circuit (ASIC) wirelessly receives power through a 60 MHz inductive link. The low-power ASIC design (84.6 µW) ensures sufficient wireless power reception through a small receiver (Rx) coil. The 60 MHz inductive link also serves as the data carrier for wireless communication between standalone devices and the central controller, eliminating the need for additional data antennas. All these efforts contribute to the miniaturization of standalone devices with dimensions of 12 × 12 × 5 mm3, enhancing device wearability. The central controller applies the pulse width modulation (PWM) scheme on the 60 MHz carrier, transmitting user commands at 4 Mbps to EEG recording ASICs. The ASIC employs a novel synchronized PWM demodulator to extract user commands, operating signal digitization and data transmission. The analog frontend (AFE) amplifies the EEG signal with a gain of 45 dB and applies band-pass filtering from 0.03 Hz to 400 Hz, with an input-referred noise (IRN) of 3.62 µVRMS. The amplified EEG signal is then digitized by a 10-bit successive approximation register (SAR) analog-to-digital converter (ADC) with a peak signal-to-noise and distortion ratio (SNDR) of 55.4 dB. The resulting EEG data is transmitted to an external software-defined radio (SDR) Rx through load-shift-keying (LSK) backscatter at 3.75 Mbps. The system's functionality is fully evaluated in human experiments.

An Ultrasonic Energy Harvesting IC Providing Adjustable Bias Voltage for Pre-Charged CMUT.

Ultrasonic wireless power transmission (WPT) using pre-charged capacitive micromachined ultrasonic transducers (CMUT) is drawing great attention due to the easy integration of CMUT with CMOS techniques. Here, we present an integrated circuit (IC) that interfaces with a pre-charged CMUT device for ultrasonic energy harvesting. We implemented an adaptive high voltage charge pump (HVCP) in the proposed IC, which features low power, overvoltage stress (OVS) robustness, and a wide output range. The ultrasonic energy harvesting IC is fabricated in the 180 nm HV BCD process and occupies a 2 × 2.5 mm2 silicon area. The adaptive HVCP offers a 2× - 12× voltage conversion ratio (VCR), thereby providing a wide bias voltage range of 4 V-44 V for the pre-charged CMUT. Moreover, a VCR tunning finite state machine (FSM) implemented in the proposed IC can dynamically adjust the VCR to stabilize the HVCP output (i.e., the pre-charged CMUT bias voltage) to a target voltage in a closed-loop manner. Such a closed-loop control mechanism improves the tolerance of the proposed IC to the received power variation caused by misalignments, amount of transmitted power change, and/or load variation. Besides, the proposed ultrasonic energy harvesting IC has an average power consumption of 35 µW-554 µW corresponding to the HVCP output from 4 V-44 V. The CMUT device with a local surface acoustic intensity of 3.78 mW/mm2, which is well below the FDA limit for power flux (7.2 mW/mm2), can deliver sufficient power to the IC.

A highly stable electrode with low electrode-skin impedance for wearable brain-computer interface.

To date, brain-computer interfaces (BCIs) have proved to play a key role in many medical applications, for example, the rehabilitation of stroke patients. For post-stroke rehabilitation, the BCIs require the EEG electrodes to precisely translate the brain signals of patients into intended movements of the paralyzed limb for months. However, the gold standard silver/silver-chloride electrodes cannot satisfy the requirements for long-term stability and preparation-free recording capability in wearable EEG devices, thus limiting the versatility of EEG in wearable BCI applications over time outside the rehabilitation center. Here, we design a long-term stable and low electrode-skin interfacial impedance conductive polymer-hydrogel EEG electrode that maintains a lower impedance value than gel-based electrodes for 29 days. With this technology, EEG-based long-term and wearable BCIs could be realized in the near future. To demonstrate this, our designed electrode is applied for a wireless single-channel EEG device that detects changes in alpha rhythms in eye-open/eye-close conditions. In addition, we validate that the designed electrodes could capture oscillatory rhythms in motor imagery protocols as well as low-frequency time-locked event-related potentials from healthy subjects, with similar or better performance than gel-based electrodes. Finally, we demonstrate the use of the designed electrode in online BCI-based functional electrical stimulation, which could be used for post-stroke rehabilitation.

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 pARMS 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 µVRMS 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.