• Journal of Biosystems Engineering
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• v.38 no.2
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• pp.163-170
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• 2013

Purpose: The purpose of this study is to provide analysis of physiological, biomechanical responses occurring from the operation to lifting or twist lifting task appears frequently in agricultural work. Methods: This study investigated the changes of physiological factors such as heart rate, heart rate variability (HRV) and biomechanical factors such as physical activity and kinetic analysis in the task of twisting at the waist while lifting. Results: Heart rates changed significantly with the workload. The result indicated that the workload of 2 kg was light intensity work, and the workload of 12 kg was hard intensity work. Physical activity increased as the workload increased both on wrist and waist. Besides, stress index of the worker increased with the workload. Dynamic load to herniated discs was analyzed using inertial sensor, and the angular acceleration and torque increased with the workload. The proposed measurement system can measure the recipient's physiological and physical signals in real-time and analyzed 3-dimensionally according to the variety of work load. Conclusions: The system we propose will be a new method to measure agricultural workers' multi-dimensional signals and analyze various farming tasks.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.31-37
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• 2010

This study was developed the home network system for the home stay care by bio-sensor system to translate the physical signal algorithm. The composition algorithm has five functions for a input function, frequency variable, displacement point input function, axial Variable, axial Sway Displacement to search a max and min point with adjustment of 0.01 unit in the reference level. There were checked physical condition of body balance to compounded a measurement such as a heart rate, temperature, weight. The algorithm of home network system can be used to support health care management system through health assistants in health care center and central health care system. It was expected to monitor a physical parameter for health management system.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1395-1405
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• 2017

In this paper, we analyses heart beat data to identify subjects stress state (binary) using heart rate variability (HRV) features extracted from heart beat data of the subjects and implement supervised machine learning techniques to create the mental stress classifier. There are four steps need to be done: data acquisition, data processing (HRV analysis), features selection, and machine learning, before doing performance measurement. There are 56 features generated from the HRV Analysis module with several of them are selected (using own algorithm) after computing the Pearson Correlation Matrix (p-values). The results of the list of selected features compared with all features data are compared by its model error after training using several machine learning techniques: support vector machine, decision tree, and discriminant analysis. SVM model and decision tree model with using selected features shows close results compared to using all recording by only 1% difference. Meanwhile, the discriminant analysis differs about 5%. All the machine learning method used in this works have 90% maximum average accuracy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.300-302
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• 2021

Usage of a face mask has become mandatory in many countries after the COVID-19. This paper described to develop a IoT based smart mask system for monitoring face mask. The system developed in this paper has two main units, a sensor module, and a smartphone application. The sensor module consists of four components: temperature and humidity sensor, a heart rate sensor, and a BLE chip. This components work as a unit to collect data and stream them through an I2C port over BLE to a connected mobile device. The smartphone application is an Android application developed for smart phones. It enables the Android device to communicate with the sensor to receive sensor data, process, store and display results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.605-606
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• 2016

Recently utilizing various wearable device has been going research to provide new services. Conventional wearable devices provide a service to a user by measuring the biological information. However, by measuring the biometric information such a situation the value of the algorithm, the user state and insufficient technology. In this paper, by utilizing an acceleration sensor and the rate sensor set a threshold for measuring the biological information, and heart rate and movement in order to solve this problem. And it proposes an algorithm to cope with the user's status and identifying emergency situations.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.257-269
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• 2021

The purpose of this study is to collect biosignal data in a non-invasive and non-restrictive manner using a BCG (Ballistocardiogram) sensor, and utilize artificial intelligence machine learning algorithms in ICT and high-performance computing environments. And it is to present and study a method for developing and validating a data-based blood glucose prediction model. In the blood glucose level prediction model, the input nodes in the MLP architecture are data of heart rate, respiration rate, stroke volume, heart rate variability, SDNN, RMSSD, PNN50, age, and gender, and the hidden layer 7 were used. As a result of the experiment, the average MSE, MAE, and RMSE values of the learning data tested 5 times were 0.5226, 0.6328, and 0.7692, respectively, and the average values of the validation data were 0.5408, 0.6776, and 0.7968, respectively, and the coefficient of determination (R²) was 0.9997. If research to standardize a model for predicting blood sugar levels based on data and to verify data set collection and prediction accuracy continues, it is expected that it can be used for non-invasive blood sugar level management.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.172-174
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• 2019

본 논문에서는 사용자가 자신의 움직임 및 심장 박동 상태를 모니터링 하기 위한 생체 인터넷 (Internet of Biometry: IoB) 기반의 HW/SW (hardware/software) 플랫폼 (platform)을 제안한다. 제안하는 시스템은 모션 센서 (motion sensor) 또는 심박 (heart rate) 센서와 같이 사용자의 생체 정보를 수집할 수 있는 센서를 사용한다. 또한, 마이크로프로세서 (microprocessor)를 사용하여 센서로부터 수집된 데이터를 사용자에게 필요한 생체 정보로 변환하고, 블루투스 (Bluetooth) 통신을 이용하여 사용자의 스마트폰 앱 (smartphone application)으로 변환한 생체 정보를 전달한다. 스마트폰 앱은 수신한 생체 정보를 디스플레이 (display)함으로써, 사용자가 자신의 상태를 모니터링 (monitoring) 할 수 있다. 제안한 시스템을 사용하여 해양 레포츠 (leisure sports) 등과 같은 활동을 하는 사람들이 자신의 몸 상태를 스스로 확인할 수 있고, 사고 예방의 효과를 얻을 수 있다.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.5
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• pp.702-712
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• 2016

A wrist watch type wearable cardiovascular monitoring device is proposed for continuous and convenient monitoring of the patient's cardiovascular system. For comprehensive monitoring of the patient's cardiovascular system, the concurrent electrocardiogram (ECG) and arterial pulse wave (APW) sensor front-end are fabricated in $0.18{\mu}m$ CMOS technology. The ECG sensor frontend achieves 84.6-dB CMRR and $2.3-{\mu}Vrms$-input referred noise with $30-{\mu}W$ power consumption. The APW sensor front-end achieves $3.2-V/{\Omega}$ sensitivity with accurate bio-impedance measurement lesser than 1% error, consuming only $984-{\mu}W$. The ECG and APW sensor front-end is combined with power management unit, micro controller unit (MCU), display and Bluetooth transceiver so that concurrently measured ECG and APW can be transmitted into smartphone, showing patient's cardiovascular state in real time. In order to verify operation of the cardiovascular monitoring system, cardiovascular indicator is extracted from the healthy volunteer. As a result, 5.74 m/second-pulse wave velocity (PWV), 79.1 beats/minute-heart rate (HR) and positive slope of b-d peak-accelerated arterial pulse wave (AAPW) are achieved, showing the volunteer's healthy cardiovascular state.

• Journal of Sensor Science and Technology
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• v.24 no.1
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• pp.69-77
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• 2015

The Microsoft Kinect is a motion sensing input device which is widely used for many motion recognition applications such as fitness, sports, and rehabilitation. Until now, most of remote rehabilitation systems with the Microsoft Kinect have allowed the user or patient to do rehabilitation or fitness by following the motion of a video screen. However in this paper we propose a smart remote rehabilitation system with the Microsoft Kinect motion sensor and a wearable ECG sensor which can allow patients to offer monitoring of the individual's performance and personalized feedback on rehabilitation exercises. The proposed noble smart remote rehabilitation is able to monitor and measure the state of the patient's condition during rehabilitation exercise, and transmits it to the prescriber. This system can give feedback to a prescriber, a doctor and a patient for improving and recovering motor performance. Thus, the efficient rehabilitation training service can be provided to patient in response to changes of patient's condition during exercise.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.103-112
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• 2018

In this paper, we propose a method of recognizing emotions using PPG sensor which measures blood flow according to emotion. From the existing PPG signal, we use a method of determining positive emotions and negative emotions in the frequency domain through PSD (Power Spectrum Density). Based on James R. Russell's two-dimensional prototype model, we classify emotions as joy, sadness, irritability, and calmness and examine their association with the magnitude of energy in the frequency domain. It is significant that this study used the same PPG sensor used in wearable devices to measure the top four kinds of emotions in the frequency domain through image experiments. Through the questionnaire, the accuracy, the immersion level according to the individual, the emotional change, and the biofeedback for the image were collected. The proposed method is expected to be various development such as commercial application service using PPG and mobile application prediction service by merging with context information of existing smart phone.