• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.515-517
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• 2016

In this paper, we develope a stretchable wearable device containing services for processing physiological signals to extract emotion information. The emotion extracting algorithm conducts to recognize emotion from EDR, SKT, and HRV signals measured with the fabric sensors. In addition, the suggested wearable device can also solve the problems faced with today's many other wearable devices: 1) limited battery life 2) the lack of compatibility and expandability due to run on internal components designed for smart phone 3) the design has always been a crucial factor in determining the success of main stream consumer wearable devices.

• Journal of Physiology & Pathology in Korean Medicine
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• v.29 no.3
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• pp.218-222
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• 2015

Facial diagnosis based on quantitative facial features has been studied in many Korean medicine fields, especially in Sasang constitutional medicine. By the rapid growing of 3D measuring technology, generic and cheap 3D sensors, such as Microsoft Kinect, is popular in many research fields. In this study, the possibility of using Kinect in facial diagnosis is examined. We introduce the development of facial feature extraction system and verify its accuracy and repeatability of measurement. Furthermore, we compare Sasang constitution diagnosis results between DSLR-based system and the developed Kinect-based system. A Sasang constitution diagnosis algorithm applied in the experiment was previously developed by a huge database containing 2D facial images acquired by DSLR cameras. Interrater reliability analysis result shows almost perfect agreement (Kappa = 0.818) between the two systems. This means that Kinect can be utilized to the diagnosis algorithm, even though it was originally derived from 2D facial image data. We conclude that Kinect can be successfully applicable to practical facial diagnosis.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.6
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• pp.271-280
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• 2017

In this paper, we propose a new safety system composed of wearable devices, driver's seat belt, and integrating controllers. The wearable device and driver's seat belt capture driver's biological information, while the integrating controller analyzes captured signal to alarm the driver or directly control the car appropriately according to the status of the driver. Previous studies regarding driver's safety from driver's seat, steering wheel, or facial camera to capture driver's physiological signal and facial information had difficulties in gathering accurate and continuous signals because the sensors required the upright posture of the driver. Utilizing wearable sensors, however, our proposed system can obtain continuous and highly accurate signals compared to the previous researches. Our advanced wearable apparatus features a sensor that measures the heart rate, skin conductivity, and skin temperature and applies filters to eliminate the noise generated by the automobile. Moreover, the acceleration sensor and the gyro sensor in our wearable device enable the reduction of the measurement errors. Based on the collected bio-signals, the criteria for identifying the driver's condition were presented. The accredited certification body has verified that the devices has the accuracy of the level of medical care. The laboratory test and the real automobile test demonstrate that our proposed system is good for the measurement of the driver's condition.

• Journal of Biosystems Engineering
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• v.35 no.5
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• pp.357-365
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• 2010

Workers in the agricultural industry have been exposed to many work-related musculoskeletal disorders. So, our objectives in this study were to measure and analyze worker's physiological bio-information to reduce musculoskeletal disorders in relation to agricultural works. We investigated worker's bio-information of physiological signals during the repeated lifting work such as body temperature, heart rate, blood pressure, physical activity, and heart rate variability. Moreover, we analyzed the workloads of lumbar spine during the repeated lifting work using the 3-axis acceleration and angular velocity sensors. The changes of body temperature was not significant, but the mean heart rate increased from 90/min to 116/min significantly during 30 min of repeated lifting work (p<0.05). The average worker's physical activity(energy consumption rate) was 206 kcal/70kg/h during the repeated lifting work. The workers' acute stress index was more than 80, which indicated a stressful work. Also, the maximum shear force on the disk (L5/S1) of a worker's lumbar spine in static state was 500N, and the maximum inertia moment was 139 $N{\cdot}m$ in dynamic state.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.1
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• pp.72-76
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• 2009

Recently, as the interest of ubiquitous computing has been increased there has been lots of research about recognizing human activities to provide services in this environment. Especially, in mobile environment, contrary to the conventional vision based recognition researches, lots of researches are sensor based recognition. In this paper we propose to recognize the user's activity with multi-modal sensors using hierarchical dynamic Bayesian networks. Dynamic Bayesian networks are trained by the OVR(One-Versus-Rest) strategy. The inferring part of this network uses less calculation cost by selecting the activity with the higher percentage of the result of a simpler Bayesian network. For the experiment, we used an accelerometer and a physiological sensor recognizing eight kinds of activities, and as a result of the experiment we gain 97.4% of accuracy recognizing the user's activity.

• Smart Structures and Systems
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• v.19 no.1
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• pp.1-10
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• 2017

The operation of subway trains induces secondary structure-borne vibrations in the nearby underground spaces. The vibration, along with the associated noise, can cause annoyance and adverse physical, physiological, and psychological effects on humans in dense urban environments. Traditional tethered instruments restrict the rapid measurement and assessment on such vibration effect. This paper presents a novel approach for Wireless Smart Sensor (WSS)-based autonomous evaluation system for the subway train-induced vibrations. The system was implemented on a MEMSIC's Imote2 platform, using a SHM-H high-sensitivity accelerometer board stacked on top. A new embedded application VibrationLevelCalculation, which determines the International Organization for Standardization defined weighted acceleration level, was added into the Illinois Structural Health Monitoring Project Service Toolsuite. The system was verified in a large underground space, where a nearby subway station is a good source of ground excitation caused by the running subway trains. Using an on-board processor, each sensor calculated the distribution of vibration levels within the testing zone, and sent the distribution of vibration level by radio to display it on the central server. Also, the raw time-histories and frequency spectrum were retrieved from the WSS leaf nodes. Subsequently, spectral vibration levels in the one-third octave band, characterizing the vibrating influence of different frequency components on human bodies, was also calculated from each sensor node. Experimental validation demonstrates that the proposed system is efficient for autonomously evaluating the subway train-induced ambient vibration of underground spaces, and the system holds the potential of greatly reducing the laboring of dynamic field testing.

• Journal of Industrial Technology
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• v.16
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• pp.71-81
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• 1996

This paper discusses a physiological approach motivated by the study of human hands for robot hand force control. It begins with an analysis of the human's grasping behavior to see how humans determine the grasp forces. The human controls the grasp force by sensing the friction force, that is, the weight of the object which is felt on his hand, but when slip is detected by sensing skin acceleration, the grasp force becomes much greater than the minimum force required for grasping by adding the force which is proportional to the acceleration. And two methods that can predict when and how fingers will slip upon a grasped object are considered. To emulate the human's capabilities, we propose a method for determination of as grasp force, which uses the change in the friction force. Experimental results show that the proposed method can be applied to control of robot hands to grasp objects of arbitrary weight stably without skin-like slip sensors.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.3
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• pp.38-47
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• 1995

A new automatic cardiac output control algorithm for the motor-driven electromechanical total artificial heart(TAH) was developed based on the motor current waveform analysis without using any extra transducer. The basic control requirements of artificial heart can be described in terms of three features : preload sensitivity, afterload insensitivity, and balanced ventricular outputs. In the previous studies, many transducers were utilized to obtain informations of hemodynamic states for the automatic cardiac output control, But such automatic control systems with sensors have had reliability problems. We proposed a new sensorless automatic cardiac output control algorithm providing adequate cardiac output to the time-varying physiological demand without causing right atrial collapse, which is one of the critical problem in an active-filling type device. In-vitro tests were performed on a mock circulation system to evaluate the performance of the developed algorithm and the results show that the new algorithm satisfied the basic control requirements on the cardiac output response and the possibility of application of the developed algorithm to in vivo experiments.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.72-73
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• 1998

A new method for enzyme immobilization has been developed to remove interference by potential interferents in body fluids. Instead of using electron mediators, we chose direct hydrogen peroxide measurement route. Extremely hydrogen peroxide-selective polymer was coated as an inner membrane to exclude interferents and then glucose oxidase(GOx) was entrapped by electropolymerization of inert monomers. There was no solvent casting step throughout the whole fabrication procedure but all membranes on Pt-Ir electrode were formed by electropolymerization. Thus, membrane thickness, quantity of enzyme loaded and can be controlled by electrochemical parameters. As a result, reproducibility of biosensor characteristics becomes remarkably improved in terms of mass production.

• Journal of Communications and Networks
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• v.13 no.2
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• pp.125-131
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• 2011

Rapid strides are being made to bring to reality the technology of wearable sensors for monitoring patients' physiological data.We study the problem of automatically detecting anomalies in themeasured blood glucose levels. The normal daily measurements of the patient are used to train a hidden Markov model (HMM). The structure of the HMM-its states and output symbols-are selected to accurately model the typical transitions in blood glucose levels throughout a 24-hour period. The learning of the HMM is done using historic data of normal measurements. The HMM can then be used to detect anomalies in blood glucose levels being measured, if the inferred likelihood of the observed data is low in the world described by the HMM. Our simulation results show that our technique is accurate in detecting anomalies in glucose levels and is robust (i.e., no false positives) in the presence of reasonable changes in the patient's daily routine.