• Journal of Korea Society of Industrial Information Systems
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• v.19 no.3
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• pp.41-48
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• 2014

This paper proposes a new sensor circuit using a 32 nm carbon nanotube FET (CNFET) technology for artificial skin. For future robotic and prosthetic applications, it is essential to develop a robust and low power artificial skin for detecting the environment through touch. Therefore, a sensor circuit for the artificial skin also has to be developed to detect the sensor signals and convert them into digital bits. The artificial skin sensor is based on a mesh of sensors consisting of a nxn matrix using CNFET, and the sensor outputs are connected to a current monitoring circuit proposed as the sensor circuit. The proposed sensor provides pressure measurements and shape information about pressure distribution.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1314-1317
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• 1997

For realizing artificial skin sensing as a final goal, a mono-material pressure-conductive rubber sensor which is also sensitive for temperature is described. Firstly, discimination of the hardness and the thermal property of material using a proposed sensor is presented. Furthermore, a tactile sensor constints of four pressure-conductive rubber sensor to discriminate surface model which imitaties the surface roughness of material is proposed.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.3
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• pp.188-192
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• 2012

In this study, a carbon nanotube (CNT) flexible strain sensor was fabricated with CNT based epoxy and rubber composites for tactile sensing. The flexible strain sensor can be fabricated as a long fibrous sensor and it also may be able to measure large deformation and contact information on a structure. The long and flexible sensor can be considered to be a continuous sensor like a dendrite of a neuron in the human body and we named the sensor as a biomimetic artificial neuron. For the application of the neuron in biomimetic engineering, an ANMS (Artificial Neuron Matrix System) was developed by means of the array of the neurons with a signal processing system. Moreover, a strain positioning algorithm was also developed to find localized tactile information of the ANMS with Labview for the application of an artificial e-skin.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.694-697
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• 1996

The aim of this work for 5 years from 1994 is to develop a multi-fingered robot hand and its control system for grasp and manipulation of objects dexterously. Since the robot hand is still being developed, a commercialized robot hand from Barrett Company is utilized to implement a hand controller and control algorithm. For this, VME based motion control and interface boards are developed and multi-sensors such as encoder, force/torque sensor, dynamic sensor and artificial skin sensor are partly developed and employed for the grasping control algorithm. In oder to handle uncertainties such as mechanical idleness and backlash, a fuzzy rule based grasping algorithm is also considered and tested with the developed control system.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.39 no.2
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• pp.147-152
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• 2015

We designed, fabricated, and tested the capacitive microsensors for skin piloerection monitoring. The performance of the skin piloerection monitoring sensor was characterized using the artificial bump, representing human skin goosebump; thus, resulting in the sensitivity of $-0.00252%/{\mu}m$ and the nonlinearity of 25.9 % for the artificial goosebump deformation in the range of $0{\sim}326{\mu}m$. We also verified two successive human skin piloerection having 3.5 s duration on the subject's dorsal forearms, thus resulting in the capacitance change of -6.2 fF and -9.2 fF compared to the initial condition, corresponding to the piloerection intensity of $145{\mu}m$ and $194{\mu}m$, respectively. It was demonstrated experimentally that the proposed sensor is capable to measure the human skin piloerection objectively and quantitatively, thereby suggesting the quantitative evaluation method of the qualitative human emotional state for cognitive human-machine interfaces applications.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.11
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• pp.29-34
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• 2009

• Journal of Sensor Science and Technology
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• v.31 no.6
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• pp.423-427
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• 2022

A pressure sensor that mimics the sensing ability of human skin has emerged as high-profile technology because it shows remarkable applications in numerous fields such as robotics, human health monitoring, and artificial prosthetics. Whereas recent pressure sensors have achieved high sensitivity similar to that of human skin, they still show limited detection bandwidth. Moreover, once these e-skin are fabricated, their sensitivity and stiffness are fixed; therefore, they can be used for only limited applications. Our study proposes a new adaptive pressure sensor built with uniform gallium microdroplet-elastomer composite. Based on the phase transition of gallium microdroplets, the proposed sensor undergoes mode transformation, enabling it to have a higher sensitivity and wider detection bandwidth compared with those of human skin. In addition, we succeeded in extending a single adaptive pressure sensor to sensor arrays based on its high uniformity, reproducibility, and large-scale manufacturability. Finally, we designed an adaptive e-skin with the sensor array and demonstrated its applications on health monitoring tasks including blood pulse and body weight measurements.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.587-592
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• 1994

To realize artificial device with sensing ability of the human skin, a mono-material tactile sensor with three sensing functions made of some elastic thin electro-conductive rubber sheet with eight latticed patch elements is proposed. This trial sensor provides the information of three kinds of model material characteristics such as thermal property, hardness property and the surface situation of materials by setting up three kinds of surface models as test materials. It can be finally expected to estimate unknown model materials by analyzing the data of the sensor.

• ETRI Journal
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• v.44 no.3
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• pp.426-437
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• 2022

To understand the multilateral characteristics of human behavior and physiological markers related to physical, emotional, and environmental states, extensive lifelog data collection in a real-world environment is essential. Here, we propose a data collection method using multimodal mobile sensing and present a long-term dataset from 22 subjects and 616 days of experimental sessions. The dataset contains over 10 000 hours of data, including physiological, data such as photoplethysmography, electrodermal activity, and skin temperature in addition to the multivariate behavioral data. Furthermore, it consists of 10 372 user labels with emotional states and 590 days of sleep quality data. To demonstrate feasibility, human activity recognition was applied on the sensor data using a convolutional neural network-based deep learning model with 92.78% recognition accuracy. From the activity recognition result, we extracted the daily behavior pattern and discovered five representative models by applying spectral clustering. This demonstrates that the dataset contributed toward understanding human behavior using multimodal data accumulated throughout daily lives under natural conditions.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1272-1274
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• 1996

This paper presents the development of tactile sensor systems for robot hand which are truly usable, robust, reliable and cheap system. The sensor incorporates multiple sensing subsystems for detecting distributed contact forces and surface characteristics. The fabrication and experimental evaluation of the tactile system and its electric interfaces are described. The results indicate that the system provides reasonable performances for practical applications requiring manipulation with tactile feedback.