• Journal of Internet Computing and Services
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• v.12 no.3
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• pp.17-26
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• 2011

We have developed a wearable device that can convert sensor data into real-time step counts. Sensor data on gait were acquired using a triaxial accelerometer. A test was performed according to a test protocol for different walking speeds, e.g., slow walking, walking, fast walking, slow running, running, and fast running. Each test was carried out for 36 min on a treadmill with the participant wearing an Actical device, and the device developed in this study. The signal vector magnitude (SVM) was used to process the X, Y, and Z values output by the triaxial accelerometer into one representative value. In addition, for accurate step-count detection, we used three algorithms: an heuristic algorithm (HA), the adaptive threshold algorithm (ATA), and the adaptive locking period algorithm (ALPA). The recognition rate of our algorithm was 97.34% better than that of the Actical device(91.74%) by 5.6%.

• Journal of the Korea Society of Computer and Information
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• v.25 no.8
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• pp.81-87
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• 2020

In this paper, proposes a health status monitoring system for socially marginalized elderly households living alone. This system is implemented by collecting various PHR biometric signals and residential environment information through IoT devices. In addition, the company aims to establish a basic infrastructure that can understand the situation of lonely deaths and implement prevention programs by strengthening the predictive ability through data analysis of the DB server based on PHR and information collected from IoT sensors. The sensor consists of an environmental information collection sensor and a noncontact and wearable sensor for biometric signal collection. A gateway is required to transmit the collected data to the server, and the prototype is presented in this paper. The paper has a discussion purpose of policy task for expanding medical welfare service. The results of this study are believed to help expand services to the socially marginalized and improve the medical environment of the people.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.6
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• pp.126-134
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• 2015

A sleep disorder is being recognized as one of the major health issues related to high levels of stress. At the same time, interests about quality of sleep are rapidly increasing. However, diagnosing sleep disorder is not a simple task because patients should undergo polysomnography test, which requires a long time and high cost. To solve this problem, an accelerometer embedded wrist-worn device is being considered as a simple and low cost solution. However, conventional methods determine a state of user to "sleep" or "wake" according to whether values of individual section's accelerometer data exceed a certain threshold or not. As a result, a high miss-classification rate is observed due to user's intermittent movements while sleeping and tiny movements while awake. In this paper, we propose a novel method that resolves the above problems by employing a dynamic classifier which evaluates a similarity between the neighboring data scores obtained from SVM classifier. A performance of the proposed method is evaluated using 50 data sets and its superiority is verified by achieving 88.9% accuracy, 88.9% sensitivity, and 88.5% specificity.

• Journal of the Microelectronics and Packaging Society
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• v.25 no.3
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• pp.43-47
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• 2018

In this study, we fabricated transparent resistive strain sensor by embedding silver nanowire in polydimethylsiloxane (PDMS) substrate to sense the finger bending motion electrically. Using bluetooth as wireless data transfer system, strain data was transferred to computer and smart phone application enabling near field communication. Additionally, we made a program translating resistance change by finger motion strain to save images and confirmed that it worked at application and computer.

• Journal of Sensor Science and Technology
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• v.20 no.2
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• pp.137-144
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• 2011

Wireless sensor network(WSN) has the potential to greatly effect many aspects of u-healthcare. By outfitting the potential with WSN, wearable sensor node can collects real-time data on physiological status and transmits through base station to server PC. However, there is a significant gap between WSN and healthcare. WSN has the limited resource about computing capability and data transmission according to bio-sensor sampling rates and channels to apply healthcare system. If a wearable node transmits ECG and accelerometer data of 4 channel sampled at 100 Hz, these data may occur high loss packets for transmitting human activity and ECG to server PC. Therefore current wearable sensor nodes have to solve above mentioned problems to be suited for u-healthcare system. Most WSN based activity and ECG monitoring system have been implemented some algorithms which are applied for signal vector magnitude(SVM) algorithm and ECG noise algorithm in server PC. In this paper, A wearable sensor node using integrated ECG and 3-axial accelerometer based on wireless sensor network is designed and developed. It can form multi-hop network with relay nodes to extend network range in WSN. Our wearable nodes can transmit 1-channel activity data processed activity classification data vector using SVM algorithm to 3-channel accelerometer data. ECG signals are contaminated with high frequency noise such as power line interference and muscle artifact. Our wearable sensor nodes can remove high frequency noise to clear original ECG signal for healthcare monitoring.

• Science of Emotion and Sensibility
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• v.22 no.4
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• pp.97-106
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• 2019

The purpose of this study was to investigate the effect of the type and measurement location of a fabric strain gauge sensor on the detection performance for respiratory signals. We implemented two types of sensors to measure the respiratory signal and attached them to a band to detect the respiratory signal. Eight healthy males in their 20s were the subject of this study. They were asked to wear two respiratory bands in turns. While the subjects were measured for 30 seconds standing comfortably, the respiratory was given at 15 breaths per minute were synchronized, and then a 10-second break; subsequently, the entire measurement was repeated. Measurement locations were at the chest and abdomen. In addition, to verify the performance of respiratory measurement in the movement state, the subjects were asked to walk in place at a speed of 80 strides per minute(SPM), and the respiratory was measured using the same method mentioned earlier. Meanwhile, to acquire a reference signal, the SS5LB of BIOPAC Systems, Inc., was worn by the subjects simultaneously with the experimental sensor. The Kruskal-Wallis test and Bonferroni post hoc tests were performed using SPSS 24.0 to verify the difference in measurement performances among the group of eight combinations of sensor types, measurement locations, and movement states. In addition, the Wilcoxon test was conducted to examine whether there are differences according to sensor type, measurement location, and movement state. The results showed that the respiratory signal detection performance was the best when the respiratory was measured in the chest using the CNT-coated fabric sensor regardless of the movement state. Based on the results of this study, we will develop a chest belt-type wearable platform that can monitor the various vital signal in real time without disturbing the movements in an outdoor environment or in daily activities.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.2
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• pp.397-407
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• 2015

In this paper, we designed 1 chip IC for 3-axis gyroscope and 3-axis accelerometer used for various IoT/M2M mobile devices such as smartphone, wearable device and etc. We especially focused on analysis of gyroscope noise and proposed new architecture for removing various noise generated by gyroscope MEMS and IC. Gyroscope, accelerometer and geo-magnetic sensors are usually used to detect user motion or to estimate moving distance, direction and relative position. It is very important element to designing a low noise IC because very small amount of noise may be accumulated and affect the estimated position or direction. We made a mathematical model of a gyroscope sensor, analyzed the frequency characteristics of MEMS and circuit, designed a low noise, compact and low power 1 chip 6-axis inertial sensor IC including 3-axis gyroscope and 3-axis accelerometer. As a result, designed IC has 0.01dps/${\sqrt{Hz}}$ of gyroscope sensor noise density.

• Journal of IKEEE
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• v.23 no.4
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• pp.1273-1279
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• 2019

Though sleep polysomnography (PSG) is considered as a golden rule for medical diagnosis of sleep disorder, it is essential to find alternative diagnosis methods due to its cost and time constraints. Recently, as the popularity of wearable health devices, there are many research trials to replace conventional actigraphy to consumer grade devices. However, these devices are very limited in their use due to the accessibility of the data and algorithms. In this paper, we showed the predictive model for sleep stages classified by American Academy of Sleep Medicine (AASM) standard and we proposed the estimation of sleep cycle by comparing sensor data and power spectrums of δ wave and θ wave. The sleep stage prediction for 31 subjects showed an accuracy of 85.26%. Also, we showed the possibility that proposed algorithm can find the sleep cycle of REM sleep and NREM sleep.

• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1779-1785
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• 2018

In this paper, we propose an error correction method to improve the accuracy of human activity recognition using sensor event data obtained by smart devices such as wearable and smartphone. In the context awareness through the smart device, errors inevitably occur in sensing the necessary context information due to the characteristics of the device, which degrades the prediction performance. In order to solve this problem, we apply Kalman filter's error correction algorithm to compensate the signal values obtained from 3-axis acceleration sensor of smart device. As a result, it was possible to effectively eliminate the error generated in the process of the data which is detected and reported by the 3-axis acceleration sensor constituting the time series data through the Kalman filter. It is expected that this research will improve the performance of the real-time context-aware system to be developed in the future.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.293-297
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• 2009

This paper describes a real-time reliable measurement and analysis system for ubiquitous healthcare based on IEEE802.15.4 standard. In order to obtain and monitor physiological body signals continuously, wearable pulse oximeter is designed in wrist that could used to measure oxygen saturation of a patient unobtrusively. The measured data was transferred to a central PC or server by using wireless sensor nodes via a wireless sensor network for storage and analysis purposes. LabVIEW server program was designed to monitor and process the measured photoplethysmogram(PPG) to accelerated plethysmogram(APG) by appling second order derivatives in server PC. These experimental results demonstrate that APG can precisely describe the features of an individual's PPG and be used as estimation of vascular elasticity for blood circulation.