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. The proposed sensor system is integrated on 278cm2 printed circuit board. The sensor system consumes 0.35 W/channel, and outstanding channel-to-channel isolation is expected by coupling factor performance of -77.7 dB. In addition, modified electrode configuration for BPG at chest drastically reduces baseline wandering by respiratory motion artifact, thereby further facilitating long-term, continuous, and non-invasive PWV measurement. As a result, long-term, continuous, and non-invasive PWV measurement more than 95 minutes is successfully performed to pave the way for developing pulse transit time (PTT)-based cuff-less blood pressure (BP) estimation technique.

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. On the basis of flexible organic light-emitting diodes and organic photodiodes designed via an optical simulation of color-sensitive light propagation within human skin, the proposed monolithically integrated organic pulse oximetry sensor heads exhibit successful operation at electrical power as low as 24 µW on average. We thereby demonstrate that organic devices not only have form factor advantages for such applications but also hold great promise as enablers for all-day wearable health monitoring systems.

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. The CR-RSSI utilizes stacked amplifier-rectifier-cell (AR-cell) to reuse the supply current of each blocks. As a result, the power consumption becomes 540 [Formula: see text] with +/-2 dB accuracy and 75 dB dynamic range. The replica bias circuit and calibration unit are adopted to increase the reliability of CR-RSSI. In addition, the duty controller turns off the RSSI when it is not required, and this function leads 70% power reduction. At last, the gm-boosting reconfigurable LNA can adaptively vary its noise and linearity performance with respect to input signal strength. Fro current reusing technique m this feature, we achieve 62% power reduction in the LNA. Thanks to these schemes, compared to the previous works, we can save 70% of power in RSSI and LNA.

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 5.94% to 1.84% in the preprocessing step. Largest Lyapunov exponents (LLE), as well as linear features such as mean and standard variation and sub-band power, are calculated with NCA acceleration. Measurements with mental task protocols result in confidence level of 95%. Thanks to the hardware acceleration, the chaos-processor fabricated in 0.13 µm CMOS technology consumes only 259.6 µW.

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. The measured data is transmitted to the mobile device and the lung EIT images are reconstructed and displayed with up to 20 frames/s real-time. From the lung EIT image, the measured lung air volume ratio can be used as an indicator of the lung-health, and other various parameters can be extracted to monitor lung status. The proposed wearable system achieves the user convenience for lung-health monitoring which can be used personally at home. The proposed system is fully implemented and verified on both in-vitro and in-vivo tests.

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. The multi-loop low-noise amplifier (MLLNA) achieves over 130 dB CMRR for EEG sensing and the capacitive correlated-double sampling transimpedance amplifier (CCTIA) has low-noise characteristics for HEG and HRV sensing. Measured three-physiology domains such as neural, vascular and autonomic domain signals are combined with canonical correlation analysis (CCA) and temporal kernel canonical correlation analysis (tkCCA) algorithm to find the neural-vascular-autonomic coupling. It supports highly accurate classification with the 19% maximum improvement with multimodal monitoring. For the multi-channel stimulation functionality, after-effects maximization monitoring and sympathetic nerve disorder monitoring, the stimulator is designed as reconfigurable. The 3.37 × 2.25 mm(2) chip has 2-channel EEG sensor front-end, 2-channel NIRS sensor front-end, NIRS current driver to drive dual-wavelength VCSEL and 6-b DAC current source for tES mode. It dissipates 24 mW with 2 mA stimulation current and 5 mA NIRS driver current.

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. This paper presents a SoC implementation to enable deep learning applications to run with low cost platforms such as mobile or portable devices. Different from conventional works which have adopted massively-parallel architecture, this work adopts task-flexible architecture and exploits multiple parallelism to cover complex functions of convolutional deep belief network which is one of popular deep learning/inference algorithms. In this paper, we implement the most energy-efficient deep learning and inference processor for wearable system. The implemented 2.5 mm × 4.0 mm deep learning/inference processor is fabricated using 65 nm 8-metal CMOS technology for a battery-powered platform with real-time deep inference and deep learning operation. It consumes 185 mW average power, and 213.1 mW peak power at 200 MHz operating frequency and 1.2 V supply voltage. It achieves 411.3 GOPS peak performance and 1.93 TOPS/W energy efficiency, which is 2.07× higher than the state-of-the-art.

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. Its dimension is 14cm × 7cm with 5mm thickness covering the chest band for convenient usage. In addition, with 3.7V 500mAh battery, its lifetime is at least 10 hours. For user's convenience, the system is interfacing to smart phones through Bluetooth communication. With the features of the HRV and Beck depression inventory (BDI), the smart phone application trains and classifies the user's depression scale with 71% of accuracy.

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. Owing to the hardwired accelerating functions, the NFS dissipates only 4.45 mW to yield 16 independent components. For non-invasive neuro-modulation, tES stimulation up to 2 mA is implemented on the SoC. The NFS is fabricated in 130-nm CMOS technology.

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.

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. It can achieve the user convenience and the low manufacturing cost at once by removing the wire connections. The EA controller IC programs the stimulation current and also monitors the electromyography (EMG) and the skin temperature during the EA stimulation. The measured data can be wirelessly transmitted to the external EA analyzer through the body channel communication with low power consumption. The external EA analyzer can check the patient's status, such as the muscle fatigue and the change of the skin temperature, and the practitioner can change the stimulation parameters for the optimal curative value. The proposed compact EA system is fully implemented and tested on the human body.

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. The adaptive stimulator IC can form a closed current loop for even a single needle, and measure the electromyography (EMG) and the skin temperature to analyze the stimulation status as well as supply programmable stimulation current (40 µA-1 mA) with 5 different modes. The large time constant (LTC) sample and hold (S/H) current matching technique achieves the high-precision charge balancing ( <;10 nA) for the patient's safety. The measured data can be wirelessly transmitted to the external EA analyzer through the body channel communication (BCC) transceiver for the low power consumption. The external EA analyzer can show the patient's status, such as the muscle fatigue and the change of the skin temperature. Based on these analyses, the practitioner can adaptively change the stimulation parameters for the optimal treatment value. A 12.5 mm(2) 0.13 µm RF CMOS stimulator chip consumes 6.8 mW at 1.2 V supporting 32 different current levels. The proposed compact EA system is fully implemented and tested on the human body.

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. For the mental health monitoring two specific functions (independent component analysis (ICA) and nonlinear chaotic analysis (NCA)) are implemented into CMOS integrated circuits. ICA extracts heart rate variability (HRV) from EEG, and then NCA extracts the largest lyapunov exponent (LLE) as physiological marker to identify mental stress and states. The extracted HRV is only 1.84% different from the HRV obtained by simple ECG measurement system. With the help of EEG signals, the proposed headband mental monitoring system shows 90% confidence level in stress test, which is better than the test results of only HRV.

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. As a complement, a dry electrode can be used. In this work, fabric-based dry electrodes are introduced. Planar-Fabric Circuit Board (P-FCB) technology enables low cost and uniform productions of such electrodes; electrical properties of the electrodes with various materials, sizes, and time are shown. Both the strengths and drawbacks of the fabric-based electrodes are also discussed.

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%. The cells are implemented by 6T static random access memory-based register files and dynamic shift registers to enable a small area of 4.5 mm(2). The bus connections between PEs and cells are optimized to minimize power consumption. The VAE is integrated within an object-recognition system-on-chip (SoC) fabricated in the 0.13- µm complementary metal-oxide-semiconductor process. It achieves 24 GOPS peak performance and 22 GOPS sustained performance at 200 MHz enabling one CNN iteration on an 80 × 60 pixel image to be completed in just 4.3 µs. With VA enabled using the VAE, the workload of the object-recognition SoC is significantly reduced, resulting in 83% higher frame rate while consuming 45% less energy per frame without degradation of recognition accuracy.

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. The data display program is developed on the external PC so that the user can check the monitoring result after wake-up. The proposed sleep monitoring system is fully implemented and tested on the human during normal sleep.

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. The second is the wirelessly powered patch sensor WBSN (Type 2); the proposed patch adopts Planar Fashionable Circuit Board (P-FCB) for pervasiveness and safety, and a base station controls an array of inductors that automatically configure sensor positions around the body. The Type 2 WBSN fits for long-term healthcare. Finally, a snap fastener patch sensor WBSN (Type 3) is proposed. This is the most secure method among 3. Again, P-FCB increases pervasiveness. A snap fastener provides secure power and data channels between sensors and a base station.

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. The second wearable cardiac heathcare sensor, fabricated in the form of 4-layer compact smart poultice type including flexible battery, can extend to monitor bio-impedance together with ECG signals at 16 different sites of the heart with 25 reconfigurable electrodes. It also provides cm-range inductive coupled remote system start-up and duty-cycled data transmission using body as communication channel for a low energy wireless interconnectivity. Both sensors exploit dry fabric electrodes to minimize skin irritation during clinical long term operation.

ECG signal compression and classification algorithm with quad level vector for ECG holter system.

An ECG signal processing method with quad level vector (QLV) is proposed for the ECG holter system. The ECG processing consists of the compression flow and the classification flow, and the QLV is proposed for both flows to achieve better performance with low-computation complexity. The compression algorithm is performed by using ECG skeleton and the Huffman coding. Unit block size optimization, adaptive threshold adjustment, and 4-bit-wise Huffman coding methods are applied to reduce the processing cost while maintaining the signal quality. The heartbeat segmentation and the R-peak detection methods are employed for the classification algorithm. The performance is evaluated by using the Massachusetts Institute of Technology-Boston's Beth Israel Hospital Arrhythmia Database, and the noise robust test is also performed for the reliability of the algorithm. Its average compression ratio is 16.9:1 with 0.641% percentage root mean square difference value and the encoding rate is 6.4 kbps. The accuracy performance of the R-peak detection is 100% without noise and 95.63% at the worst case with -10-dB SNR noise. The overall processing cost is reduced by 45.3% with the proposed compression techniques.

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. The 3 pulse-based transceivers for BCC are reviewed in this paper, and their interference issues are discussed. To enhance BCC robustness, an adaptive frequency hopping scheme is applied. Fabric Area Network (FAN) is introduced with a low energy inductive coupling transceiver and a fault-tolerant switch to realize intra- and inter-layer WBSN at once. Unique wearable environment issues and the adaptation technique to overcome those issues are discussed.