Simultaneous Electrical Bio-Impedance Plethysmography at Different Body Parts: Continuous and Non-Invasive Monitoring of Pulse Wave Velocity.

A simultaneous and time-synchronized electrical bio-impedance plethysmography (BPG) sensor system is implemented for long-term, continuous, and non-invasive measurement of arterial pulse wave velocity (PWV). The proposed BPG sensor system electrically separates each ground plane of two BPG channels and controller, and the two different BPG channels are time-synchronized by the controller transmitting periodic pulse signal to the two BPG channels. Furthermore, net parasitic capacitance between the ground planes is minimized by removing isolated DC-DC converter, limiting the number of digital capacitive isolators, and adopting optimal layout of the ground planes.

Toward all-day wearable health monitoring: An ultralow-power, reflective organic pulse oximetry sensing patch.

Pulse oximetry sensors have been playing a key role as devices to monitor elemental yet critical human health states. Conventional pulse oximetry sensors, however, have relatively large power consumption, impeding their use as stand-alone, continuous monitoring systems that can easily be integrated with everyday life. Here, we exploit the design freedom offered by organic technologies to realize a reflective patch-type pulse oximetry sensor with ultralow power consumption.

An adaptive DC-balanced and multi-mode stimulator IC with 1GΩ output impedance for compact electro-acupuncture system.

A compact and low-power electro-acupuncture (EA) IC and its Premeasurement System as off-chip board for verification are proposed for medical treatment and experiment. The multi-mode stimulator EA IC can perform adaptive charge balancing with various load condition as well as programmable supply voltage for 5 different stimulation modes. The proposed EA IC occupies 2.

A 274µW clock synchronized wireless body area network IC with super-regenerative RSSI for biomedical ad-hoc network system.

A clock synchronized multi-node wireless body area network (WBAN) transceiver IC with super-regenerative RSSI is proposed for biomedical ad-hoc network system. The ad-hoc network makes stable communication even in the harsh human body channel condition (<;-50 dB attenuation) with low-power consumption. The proposed system consists of two types of IC - a hub and node IC.

A fabric wrist patch sensor for continuous and comprehensive monitoring of the cardiovascular system.

The wrist patch-type ECG/APW sensor system is proposed for continuous and comprehensive monitoring of the patient's cardiovascular system. The wrist patch-type ECG/APW sensor system is consists of ECG/APW sensor, ECG/APW electrodes, and base station for real-time monitoring of the patient's status. The ECG/APW sensor and electrodes are composed of wrist patch, bandage-type ECG electrode and fabric APW electrode, respectively so that the patient's cardiovascular system can be continuously monitored in daily life with free hand-movement.

Sticker-type ECG/PPG concurrent monitoring system hybrid integration of CMOS SoC and organic sensor device.

The sticker-type sensor system is proposed targeting ECG/PPG concurrent monitoring for cardiovascular diseases. The stickers are composed of two types: Hub and Sensor-node (SN) sticker. Low-power CMOS SoC for measuring ECG and PPG signal is hybrid integrated with organic light emitting diodes (OLEDs) and organic photo detector (OPD).

A 540-[Formula: see text] Duty Controlled RSSI With Current Reusing Technique for Human Body Communication.

An ultra-low-power duty controlled received signal strength indicator (RSSI) is implemented for human body communication (HBC) in 180 nm CMOS technology under 1.5 V supply. The proposed RSSI adopted 3 following key features for low-power consumption; 1) current reusing technique (CR-RSSI) with replica bias circuit and calibration unit, 2) duty controller, and 3) reconfigurable gm-boosting LNA.

An Energy-Efficient and Scalable Deep Learning/Inference Processor With Tetra-Parallel MIMD Architecture for Big Data Applications.

Deep Learning algorithm is widely used for various pattern recognition applications such as text recognition, object recognition and action recognition because of its best-in-class recognition accuracy compared to hand-crafted algorithm and shallow learning based algorithms. Long learning time caused by its complex structure, however, limits its usage only in high-cost servers or many-core GPU platforms so far. On the other hand, the demand on customized pattern recognition within personal devices will grow gradually as more deep learning applications will be developed.

A Wearable EEG-HEG-HRV Multimodal System With Simultaneous Monitoring of tES for Mental Health Management.

A multimodal mental management system in the shape of the wearable headband and earplugs is proposed to monitor electroencephalography (EEG), hemoencephalography (HEG) and heart rate variability (HRV) for accurate mental health monitoring. It enables simultaneous transcranial electrical stimulation (tES) together with real-time monitoring. The total weight of the proposed system is less than 200 g.

Wearable lung-health monitoring system with electrical impedance tomography.

The wearable lung-health monitoring system is proposed with an electrical impedance tomography (EIT). The proposed system has light belt-type form factor which is implemented with the EIT integrated circuit (IC) on the planar-fashionable circuit board (P-FCB) technology. The EIT IC provides programmable current stimulation which is optimally controlled by the results of contact impedance monitoring.

A multimodal stress monitoring system with canonical correlation analysis.

The multimodal stress monitoring headband is proposed for mobile stress management system. It is composed of headband and earplugs. Electroencephalography (EEG), hemoencephalography (HEG) and heart-rate variability (HRV) can be achieved simultaneously in the proposed system for user status estimation.

A 259.6 μW HRV-EEG Processor With Nonlinear Chaotic Analysis During Mental Tasks.

A system-on-chip (SoC) with nonlinear chaotic analysis (NCA) is presented for mental task monitoring. The proposed processor treats both heart rate variability (HRV) and electroencephalography (EEG). An independent component analysis (ICA) accelerator decreases the error of HRV extraction from 5.

A wearable neuro-feedback system with EEG-based mental status monitoring and transcranial electrical stimulation.

A wearable neuro-feedback system is proposed with a low-power neuro-feedback SoC (NFS), which supports mental status monitoring with encephalography (EEG) and transcranial electrical stimulation (tES) for neuro-modulation. Self-configured independent component analysis (ICA) is implemented to accelerate source separation at low power. Moreover, an embedded support vector machine (SVM) enables online source classification, configuring the ICA accelerator adaptively depending on the types of the decomposed components.

Wearable depression monitoring system with heart-rate variability.

A wearable depression monitoring system is proposed with an application-specific system-on-chip (SoC) solution. The SoC is designed to accelerate the filtering and feature extraction of heart-rate variability (HRV) from the electrocardiogram (ECG). Thanks to the SoC solution and planar-fashionable circuit board (P-FCB), the monitoring system becomes a low-power wearable system.

Low power and self-reconfigurable WBAN controller for continuous bio-signal monitoring system.

The WBAN controller with Branched Bus (BB) topology and Continuous Data Transmission (CDT) protocol with low power consumption and self-reconfigurability is proposed for wearable healthcare applications. The BB topology and CDT protocol is a combination of conventional Bus and Star topology and a variation from TDMA protocol, respectively, while they are able to compensate for the electrical fault in bio-signal monitoring system caused by the electrode deformation. Thanks to them, the proposed WBAN controller enables more reliable operation in continuous bio-signal monitoring applications such as sleep monitoring.

A sub-10 nA DC-balanced adaptive stimulator IC with multi-modal sensor for compact electro-acupuncture stimulation.

A compact electro-acupuncture (EA) system is proposed for a multi-modal feedback EA treatment. It is composed of a needle, a compact EA patch, and an interconnecting conductive thread. The 3 cm diameter compact EA patch is implemented with an adaptive stimulator IC and a small coin battery on the planar-fashionable circuit board (P-FCB) technology.

Wearable mental-health monitoring platform with independent component analysis and nonlinear chaotic analysis.

The wearable mental-health monitoring platform is proposed for mobile mental healthcare system. The platform is headband type of 50 g and consumes 1.1 mW.

The compact electro-acupuncture system for multi-modal feedback electro-acupuncture treatment.

The compact electro-acupuncture (EA) system is proposed for the multi-modal feedback EA treatment. It is composed of a needle, a smart patch, and an interconnecting conductive thread. The 3cm diameter compact EA patch is implemented with the EA controller integrated circuit (IC) and the small coin battery on the planar-fashionable circuit board (P-FCB) technology.

The Smart Patches and Wearable Band (W-Band) for comfortable sleep monitoring system.

The Smart Patches and Wearable Band (W-Band) are proposed for comfortable sleep monitoring system which recognizes and diagnoses sleep disorders. By using Planar Fashionable Circuit Board (P-FCB) techniques, the Smart Patches are implemented with the plain fabric patch so that it can have light weight and small size. And the stretchability of the W-Band can achieve user convenience, low manufacturing cost, and low power consumption all at once.

Fabric circuit board-based dry electrode and its characteristics for long-term physiological signal recording.

This paper presents a dry fabric electrode and its characteristics. For long-term physiological signal monitoring, conventional wet type electrode such as an Ag/AgCl electrode may not be sufficient, because captured signal strength degrades over time as its electrolyte dehydrates. Moreover, the electrolyte may cause skin irritation over a period of time.

A wirelessly powered electro-acupuncture based on adaptive pulsewidth monophase stimulation.

A wirelessly powered electro-acupuncture (EA) system with adaptive-pulsewidth (APW) monophase stimulation is presented for convenient invasive medicine. The proposed system removes cumbersome wires connected between EA nodes and an EA controller in order to realize both patients' convenience and remedial values simultaneously. An ultra-low-power stimulator integrated circuit (IC) that is integrated on the flexible-printed-circuit board (F-PCB) is attached to the tip of a needle electrode.

Emerging low energy Wearable Body Sensor Networks using patch sensors for continuous healthcare applications.

Three emerging Wearable Body Sensor Networks (WBSN) using patch type sensors are examined and compared. Unique WBSN environment issues and techniques to overcome those issues are presented for continuous healthcare applications. The first is the battery powered RF patch sensor WBSN (Type 1); it maximizes user-convenience and is suitable for short-term healthcare.

Wireless fabric patch sensors for wearable healthcare.

Two novel wireless fabric patch sensors are introduced for low energy wearable healthcare. The first is a wirelessly powered patch sensor that can be attached to a patient to capture electrocardiogram (ECG) while consuming only 12 microW. By using fabric circuit board technology, the band-aid like sensor is implemented.

24-GOPS 4.5- mm² digital cellular neural network for rapid visual attention in an object-recognition SoC.

This paper presents the Visual Attention Engine (VAE), which is a digital cellular neural network (CNN) that executes the VA algorithm to speed up object-recognition. The proposed time-multiplexed processing element (TMPE) CNN topology achieves high performance and small area by integrating 4800 (80 × 60) cells and 120 PEs. Pipelined operation of the PEs and single-cycle global shift capability of the cells result in a high PE utilization ratio of 93%.

Low energy wearable body-sensor-network.

Wearable body sensor network (WBSN) is realized with wireless and wireline techniques. Body channel communication (BCC), which uses the human body as a signal transmission medium, can reduce energy consumption of a wireless on-body transceiver to less than 0.5nJ/b.