Sunjoo Hong

18.4 A 4.9mΩ-sensitivity mobile electrical impedance tomography IC for early breast-cancer detection system

A mobile electrical impedance tomography (EIT) IC is proposed for early breast cancer detection personally at home. To assemble the entire system into a simple brassiere shape, EIT IC is integrated via a multi-layered fabric circuit board which includes 90 EIT electrodes and two reference electrodes for current stimulation and voltage sensing. The IC supports three operating modes; gain scanning, contact impedance monitoring, and EIT modes for the clear EIT image. A differential sinusoidal current stimulator (DSCS) is proposed for injection of low-distortion programmable current which has harmonics less than -59 dBc at a load impedance of 2 kΩ. To get high sensitivity, a 6-channel voltage sensing amplifier can adaptively control the gain up to a maximum of 60 dB, and has low input referred noise, 36 nV/ √Hz. The 2.5 × 5 mm chip is fabricated in a 0.18 μm 1P6M CMOS process and consumes 53.4 mW on average. As a result, a sensitivity of 4.9 mΩ is achieved which enables the detection of a 5 mm cancer mass within an agar test phantom.

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].

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 patients 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 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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Electro-acupuncture (EA), a combination of acupuncture and electrical stimulation, has been widely used for its effectiveness in pain relief since the 1970s [1] and later for treatment of various diseases such as depression, addiction, and gastrointestinal disorders [2], and non-medical applications including obesity treatment [3]. For stimulation, most EA systems use a pair of needles with long, thick wires connected to an external power supply to form a closed current loop. The thin (φ=2mm) needle may suffer from the inconvenient and unstable connection to the thick wire and if there are many needles, it is difficult to supply power to all needles [4]. Recently, a wirelessly-powered EA system was proposed in [4] to remove the wire connections for convenient treatment, but its wireless power harvesting generated only 8μW which is not enough for various applications [1-3]. Most EA systems use bi-phase stimulation to reduce tissue damage, electrolysis, and electrolytic degradation [5]. However, the high-precision balancing of a bi-phase current pulse is difficult to achieve because the required offset, <;10nA [6], is only on the order of 10<;sup>;-5<;/sup>; of the stimulation current level, ~1mA. Furthermore, none of the previous EA systems have any feedback mechanism to enable adaptive stimulation by showing the real-time status of the EA stimulation to the patient.

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

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.

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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.

W ExG Sensor IC for Compact Sleep Monitoring Applications

An electrical impedance tomography (EIT) SoC is proposed for the portable real-time lung ventilation monitoring system. The proposed EIT SoC is integrated into belt-typefabric system with 32 electrodes and can show the dynamic images of the lung ventilation on the mobile devices. To get high fidelity images, a T-switch is adopted for high off-isolation between electrodes more than 60 dB, and I/Q signal generation and demodulation can obtain both real and imaginary part of images. For the real-time imaging, an on-chip fast demodulation scheme is proposed, and it can also reduce speed requirements of ADC for low-power consumption. The proposed EIT SoC of 5.0 mm × 5.0 mm is fabricated in 0.18 μm CMOS technology, and consumes only 10.4 mW with 1.8 V supply. As a result, EIT images were reconstructed with 97.3% of accuracy and up to 20 frames/s real-time lung images can be displayed on the mobile devices.

A sub-10nA DC-balanced adaptive stimulator IC with multimodal sensor for compact electro-acupuncture system

A motion artifact reduction multi-channel bio-signal sensor is proposed for sleep monitoring system with the help of independent component analysis (ICA). To prevent signal saturation due to large motion artifacts, adaptive DC level control (ADLC) is adopted to adjust the DC level of the signal. The current-controlled level shifter (CCLS) is used in ADLC for the shifting voltage step as small as 3mV with low power consumption. The shifted voltages and the motion artifacts are recovered by the ICA. The ICA also detects only the channels suffered from motion artifact and enables ADLC circuit of those selected channels to reduce standby power dissipation. The proposed 4-channel sleep monitoring sensor is implemented in a 0.13μm CMOS process, and consumes only 46μW. With the proposed ADLC, 17.7 times larger motion artifact cannot saturate signal.

A 259.6μW nonlinear HRV-EEG chaos processor with body channel communication interface for mental health monitoring

A low-power parallel multiplier based on optimized bypassing architecture (OBA) is proposed. The proposed OBA has two kinds of adder cells to reduce power consumption by 15.7 %. One is the two-dimensional bypassing adder (TDBA) which performs both row and column bypassing scheme simultaneously, and the other is the modified row-bypassing adder (MRBA) for the proposed row-bypassing scheme. In the proposed TDBA and MRBA, the logic evaluation is partially activated by internal tri-state buffers (ITBs) in order to save the switching power dissipation up to 33.7 % and 32.0 %, respectively. Implemented in 0.13 µm CMOS process, the proposed 8×8 parallel multiplier consumes only 145 µW.