A Flexible Capacitive Tactile Sensor Array With CMOS Readout Circuits for Pulse Diagnosis

Peng, J. G.; Lu, Michael S.-C.

doi:10.1109/jsen.2014.2360777

Cited by 18 publications

(4 citation statements)

References 29 publications

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“…It can be seen from this comparison table that our work showcases the highest number of array cells. Compared with [10,11,17], our sensor outputs the highest SNR in comparison with an analog output. The resolution is also the highest among these works.…”

Section: Measurementmentioning

confidence: 99%

“…Furthermore, none of these have a matching signal readout circuit, so these disadvantages limit the practical application of tactile sensors. In [11], a capacitive tactile sensor for pulse diagnosis with a custom CMOS readout circuit was developed. The sensitivity of the sensor could reach 1.57 mV/mmHg, and the minimum detectable pressure and capacitance were at sub-millimeter mercury and sub-fF levels, respectively.…”

Section: Introductionmentioning

confidence: 99%

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A Low-Noise and Monolithic Array Tactile Sensor Based on Incremental Delta-Sigma Analog-to-Digital Converters

Huang

Shen

et al. 2022

Electronics

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A low-noise and monolithic array tactile sensor, in which a tactile sensing unit, a low-noise analog front end (AFE), and a high-resolution delta-sigma analog-to-digital converter (ΔΣ ADC) are fully integrated, is presented in this paper. In this proposed system, compared with a discrete-device-based board-level system, the parasitic effect of a long cable connection can be reduced, and results are more accurate. Furthermore, a smaller system area and a lower power consumption can be achieved in this monolithic system. A discrete-continuous mixed mode bandpass AFE is proposed to filter out low-frequency flicker noise and high-frequency white noise. In order to improve the quantization rate of the sensor readout circuit and further suppress the high-frequency noise, a two-way alternate sample-and-hold circuit scheme is adopted in this design. The proposed tactile sensor is designed and fabricated in a 0.5-μm CMOS (Complementary metal oxide semiconductor)mixed-signal process with a 16 × 16 array and a total chip area of 1.9 × 1.9 cm2. This chip consumes 33.5 mW from a 5 V supply. The measurement results showed that the signal-to-noise and distortion rate (SNDR) was 65.2894 dB and that the effective number of bits (ENoB) was 10.553 dB. Moreover, this sensor could achieve a pressure measurement range of 0.002–0.5 N with a resolution of 0.4 mN.

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Section: Measurementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Low-Noise and Monolithic Array Tactile Sensor Based on Incremental Delta-Sigma Analog-to-Digital Converters

Huang

Shen

et al. 2022

Electronics

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“…Doctors can detect the pulse strength, width, length, and frequency of the blood vessel through their fingers, and analyze a patient's physical state. However, this method lacks one quantifiable standard [4,5]. Objective research on pulse diagnosis has emerged at home and abroad.…”

Section: Introductionmentioning

confidence: 99%

Interference Signal Identification of Sensor Array Based on Convolutional Neural Network and FPGA Implementation

Huang

Geng

et al. 2021

Electronics

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The pulse carries important physiological and pathological information about the human body. The piezoresistive sensor used to capture vascular pulsation information has transitioned from a single-point to a sensor array. However, the interference signal between channels has become a key bottleneck restricting the development of the sensor array pulse diagnosis equipment. The sensor in contact with vascular pulsation obtains the pulse signal. When some sensors are displaced due to vascular pulsation, other sensors will be driven to move, which will produce interference signals. Signal interference is a common problem for sensor arrays, but few people have analyzed this problem from the perspective of the algorithm. In this paper, an interference signal recognition algorithm of the sensor array based on a convolutional neural network (CNN) is proposed. Firstly, a simple mechanical structure model was established to analyze the generation mechanism of interference signals in one MEMS sensor array acquisition system. Then, a CNN model with fewer parameters was designed for identifying interference signals. Finally, the CNN model was implemented on a field-programmable gate array (FPGA). The results show that the CNN algorithm could identify interference signals well, and the accuracy of the algorithm was 99.3%. The power consumption of the CNN accelerator was 0.673 W at a working frequency of 100 MHz. The interference signal identification algorithm is proposed to ensure the accurate analysis of array signals. FPGA implementation lays the foundation for the miniaturization and portability of the equipment.

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“…Hu et al [15] present a sensor probe consisting of a capacitive array sensor with 12 sensing points to determine the optimal pulse-taking position. Peng and Lu [16] introduce a flexible 5 × 5 capacitive pressure sensor array based on flexible printed circuit boards and integrated CMOS switched capacitor readout circuits for determining pulse patterns. In Chang’s study [17], a 9-channel sensing probe based on piezoelectric PVDF sensors is described for collecting pulse patterns.…”

Section: Introductionmentioning

confidence: 99%

Pulse wave response characteristics for thickness and hardness of the cover layer in pulse sensors to measure radial artery pulse

et al. 2018

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BackgroundPiezo-resistive pressure sensors are widely used for measuring pulse waves of the radial artery. Pulse sensors are generally fabricated with a cover layer because pressure sensors without a cover layer are fragile when they come into direct contact with the skin near the radial artery. However, no study has evaluated the dynamic pulse wave response of pulse sensors depending on the thickness and hardness of the cover layer. This study analyzed the dynamic pulse wave response according to the thickness and hardness of the cover layer and suggests an appropriate thickness and hardness for the design of pulse sensors with semiconductor device-based pressure sensors.MethodsPulse sensors with 6 different cover layers with various thicknesses (0.8 mm, 1 mm, 2 mm) and hardnesses (Shore type A; 30, 43, 49, 71) were fabricated. Experiments for evaluating the dynamic pulse responses of the fabricated sensors were performed using a pulse simulator to transmit the same pulse wave to each of the sensors. To evaluate the dynamic responses of the fabricated pulse sensors, experiments with the pulse sensors were conducted using a simulator that artificially generated a constant pulse wave. The pulse wave simulator consisted of a motorized cam device that generated the artificial radial pulse waveform by adjusting the stroke of the cylindrical air pump and an air tube that conveyed the pulse to the artificial wrist.ResultsThe amplitude of the measured pulse pressure decreased with increasing thickness and hardness of the cover layer. Normalized waveform analysis showed that the thickness rather than the hardness of the cover layer contributed more to waveform distortion. Analysis of the channel distribution of the pulse sensor with respect to the applied constant dynamic pressure showed that the material of the cover layer had a large effect.ConclusionsIn this study, in-line array pulse sensors with various cover layers were fabricated, the dynamic pulse wave responses according to the thickness and the hardness of the cover layer were analyzed, and an appropriate thickness and hardness for the cover layer were suggested. The dynamic pulse wave responses of pulse sensors revealed in this study will contribute to the fabrication of improved pulse sensors and pulse wave analyses.

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A Flexible Capacitive Tactile Sensor Array With CMOS Readout Circuits for Pulse Diagnosis

Cited by 18 publications

References 29 publications

A Low-Noise and Monolithic Array Tactile Sensor Based on Incremental Delta-Sigma Analog-to-Digital Converters

A Low-Noise and Monolithic Array Tactile Sensor Based on Incremental Delta-Sigma Analog-to-Digital Converters

Interference Signal Identification of Sensor Array Based on Convolutional Neural Network and FPGA Implementation

Pulse wave response characteristics for thickness and hardness of the cover layer in pulse sensors to measure radial artery pulse

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