Taehwan Roh

A 3.9 mW 25-Electrode Reconfigured Sensor for Wearable Cardiac Monitoring System

A low power highly sensitive Thoracic Impedance Variance (TIV) and Electrocardiogram (ECG) monitoring SoC is designed and implemented into a poultice-like plaster sensor for wearable cardiac monitoring. 0.1 Ω TIV detection is possible with a sensitivity of 3.17 V/Ω and SNR > 40 dB. This is achieved with the help of a high quality (Q-factor > 30) balanced sinusoidal current source and low noise reconfigurable readout electronics. A cm-range 13.56 MHz fabric inductor coupling is adopted to start/stop the SoC remotely. Moreover, a 5% duty-cycled Body Channel Communication (BCC) is exploited for 0.2 nJ/b 1 Mbps energy efficient external data communication. The proposed SoC occupies 5 mm × 5 mm including pads in a standard 0.18 μm 1P6M CMOS technology. It dissipates a peak power of 3.9 mW when operating in body channel receiver mode, and consumes 2.4 mW when operating in TIV and ECG detection mode. The SoC is integrated on a 15 cm × 15 cm fabric circuit board together with a flexible battery to form a compact wearable sensor. With 25 adhesive screen-printed fabric electrodes, detection of TIV and ECG at 16 different sites of the heart is possible, allowing optimal detection sites to be configured to accommodate different user dependencies.

A Planar MICS Band Antenna Combined With a Body Channel Communication Electrode for Body Sensor Network

A 2.5 times 1.8 cm2 medical implant communication service band antenna is combined with an electrode for body channel communication. The proposed design enables a body sensor network controller to communicate with health-care devices located on and inside a patients body. The spiral microstrip antenna with its radiating body and ground plane placed side-by-side has the thickness of 2 mm and can be attached to human skin conveniently. The propagation loss of the body channel is measured when the proposed antenna is used as the skin interface for BCC in the 10-70-MHz band, and the results are compared with the cases of Ag/AgCl and circular dry electrodes. The equivalent-circuit model of the antenna as the electrode is also derived from the measured impedance characteristics. The LC resonance structure to drive the on-body antenna with its capacitance increased due to the skin contact reduces the power consumption of the TX buffer by >50%. TheS 11-parameter of the on-body antenna, its radiation pattern, and the signal loss inside the human body are investigated.

A 5.5mW IEEE-802.15.6 wireless body-area-network standard transceiver for multichannel electro-acupuncture application

In this paper, we present a state-of-the-art WBAN transceiver satisfying all of the specifications for the IEEE 802.15.6 standard. Especially, the driver active-digital-bandpass filter (ADF) is proposed to fulfill the tight spectral mask requirement without using external components. Moreover, the WBAN transceiver SoC is applied to multichannel electro-acupuncture, which is one of the most prominent emerging medical applications and is useful to verify the successful operation of the transceiver [6].

A 75

A light-weighted body area network using 3-layer coin-sized fabric patches is proposed for sleep monitoring systems. It consists of two ICs, a network controller (NC) and a sensor node (SN), and also Wearable Band (W-Band) to connect them. The Continuous Data Transmission (CDT) protocol is proposed for low power and real-time scalability by in-order data transmission using W-Band among several sensors. Based on this protocol, Linked List based network Manager (LLM) and Adaptive Dual Mode Controller (ADMC) in NC, and low swing data transmitter (D-TX) and its own back-end circuits are introduced. The LLM and ADMC can adaptively change the network configuration according to the dynamic network variances in real-time ( <;500 ms), and D-TX can make low energy data transmitter of 0.33 pJ/b with 20 Mbps data rate for W-Band interface. These two low power ICs, which are implemented in 0.18 μm CMOS process and operates with 1.5 V supply, consume 75 μW and 25 μW, respectively.

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Recently, wearable heart monitoring systems have been developed for cardiovascular-related disease [1] with wearable body sensor network (WBSN) [2–3]. The WBSN introduced in [3] monitored ECG at maximum 48 points, and transferred data using arrayed inductive link for cm-range wireless inter-connectivity. However, most of the previous attempts were limited to sense only ECG signals at limited points [2] on the body with limited network coverage [3]. Thoracic impedance variance (TIV) from the change of aortic blood volume and velocity at each cardiac cycle provides important hemodynamic information (stroke volume, cardiac output). Combined with ECG signals from more than 6 points, it enables the early detection of abnormal symptoms of pandemic diseases like hypertension and heart failure so that the patients can take prophylactic measures [6]. In spite of its importance, the TIV detection was not realized in WBSN due to its requirement of high impedance (≪0.2Ω) detection sensitivity which needs to detect AM signal with modulation depth as low as less than 3%. A pure single tone sinusoidal current signal at 1kHz–100kHz [6] is required to realize such a high sensitivity, and only a bulky implementation was reported so far [7]. In this paper, we report a 3.9mW low power SoC with body-channel-transceiver (BCT), which can detect TIV (0.1Ω) and ECG (up to 8 points) concurrently. The chip is integrated on a 4-layer fabric circuit board with thin flexible battery as a poultice-like plaster. In addition, it can reconfigure the 25-electrode array and optimize them in-situ to automatically consider the user dependency of the TIV/ECG signals. The recorded data is transmitted at 1Mbps through body-channel-communication (BCC) [8] with duty cycle modification to extend battery life time and enlarge the network coverage.

W Real-Time Scalable Body Area Network Controller and a 25

A wearable neuro-feedback system is proposed with a low-power neuro-feedback SoC (NFS), which supports mental status monitoring with electroencephalography (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.

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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.

W ExG Sensor IC for Compact Sleep Monitoring Applications

In this paper, we present a wearable mental health measurement system incorporating the nonlinear analysis of physiological rhythm including HRV and EEG signals together for high accuracy. The proposed system is implemented in a 31g headband that measures scalp signals and performs nonlinear-chaotic analysis to measure the stress levels. Using a 1.2V 40mAhr coin-battery (11.7χ5.35mm21.7g), the proposed system is able to operate for more than 7 days.

A 3.9mW 25-electrode reconfigured thoracic impedance/ECG SoC with body-channel transponder

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

A Wearable Neuro-Feedback System With EEG-Based Mental Status Monitoring and Transcranial Electrical Stimulation

Recently, mental diseases have been successfully treated by neuro-feedback therapy based on Quantitative EEG (QEEG) and Event Related Potential (ERP) online data measurements. The U.S. Food and Drug Administration (FDA) approved the first EEG test for diagnosing attention deficit hyperactivity disorder (ADHD) in 2013 [1]. The EEG signals are measured by an EEG cap and analyzed by a high performance computer to extract not only the EEG power at a predetermined frequency and site combinations, but also the degree of coherence between all sites. Based on these results, brain stimulation is performed to modulate brain rhythms (EEG) toward the normal values for the therapy.