• Journal of Internet of Things and Convergence
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• v.10 no.4
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• pp.101-108
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• 2024

Indoor localization is a critical component for numerous applications, ranging from navigation in large buildings to emergency response. This paper presents an enhanced Pedestrian Dead Reckoning (PDR) scheme using smartphone sensors, integrating neural network-aided motion recognition, Kalman filter-based error correction, and multi-sensor data fusion. The proposed system leverages data from the accelerometer, magnetometer, gyroscope, and barometer to accurately estimate a user's position and orientation. A neural network processes sensor data to classify motion modes and provide real-time adjustments to stride length and heading calculations. The Kalman filter further refines these estimates, reducing cumulative errors and drift. Experimental results, collected using a smartphone across various floors of University, demonstrate the scheme's ability to accurately track vertical movements and changes in heading direction. Comparative analyses show that the proposed CNN-LSTM model outperforms conventional CNN and Deep CNN models in angle prediction. Additionally, the integration of barometric pressure data enables precise floor level detection, enhancing the system's robustness in multi-story environments. Proposed comprehensive approach significantly improves the accuracy and reliability of indoor localization, making it viable for real-world applications.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.1
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• pp.57-64
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• 2015

Activity recognition technology is gaining attention because it can provide useful information follow user's situation. In research of activity recognition before smartphone's dissemination, we had to infer user's activity by using independent sensor. But now, with development of IT industry, we can infer user's activity by using inner sensor of smartphone. So, more animated research of activity recognition is being implemented now. By applying activity recognition system, we can develop service like recommending application according to user's preference or providing information of route. Some previous activity recognition systems have a defect using up too much energy, because they use GPS sensor. On the other hand, activity recognition system which Google released recently (Google Activity Recognition) needs only a few power because it use 'Network Provider' instead of GPS. Thus it is suitable to smartphone application system. But through a result from testing performance of Google Activity Recognition, we found that is difficult to getting user's exact activity because of unnecessary activity element and some wrong recognition. So, in this paper, we describe problems of Google Activity Recognition and propose AGAR(Advanced Google Activity Recognition) applied method to improve accuracy level because we need more exact activity recognition for new service based on activity recognition. Also to appraise value of AGAR, we compare performance of other activity recognition systems and ours and explain an applied possibility of AGAR by developing exemplary program.

• Journal of the Korea Society of Computer and Information
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• v.25 no.5
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• pp.73-79
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• 2020

As the number of earthquakes gradually increases on the Korean Peninsula, much research has been actively conducted to detect earthquakes quickly and accurately. Because traditional seismic stations are expensive to install and operate, recent research is currently being conducted to detect earthquakes using low-cost MEMS sensors. In this article, we evaluate how a low-cost MEMS acceleration sensor installed in a smartphone can be used to detect earthquakes. To this end, we installed about 280 smartphones at various locations in Korea to collect acceleration data and then assessed the installed sensors' noise floor through PSD calculation. The noise floor computed from PSD determines the magnitude of the earthquake that the installed MEMS acceleration sensors can detect. For the last few months of real operation, we collected acceleration data from 200 smartphones among 280 installed smartphones and then computed their PSDs. Based on our experiments, the MEMS acceleration sensor installed in the smartphone is capable of observing and detecting earthquakes with a magnitude 3.5 or more occurring within 10km from an epic center. During the last several months of operation, the smartphone acceleration sensor recorded an earthquake of magnitude 3.5 in Miryang on December 30, 2019, and it was confirmed as an earthquake using STA/LTA which is a simple earthquake detection algorithm. The earthquake detection system using MEMS acceleration sensors is expected to be able to detect increasing earthquakes more quickly and accurately.

• Physical Therapy Rehabilitation Science
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• v.11 no.3
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• pp.311-320
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• 2022

Objective: To investigate the reliability and validity of static balance measurements using an acceleration sensor and a gyroscope sensor in smart phone inertial sensors. Design: Equivalent control group pretest-posttest. Methods: Subjects were forty five healthy adults aged twenty to fifty-years-old who had no disease that could affect the experiment. After pre-test, all participants wore a waist band with smart phone, and conducted six static balance measurements on the force plate twice for 35 seconds each. To investigate the test-retest reliability of both smart phone inertial sensors, we compared the intra-correlation coefficient (ICC 3, 1) between primary and secondary measurements with the calculated root mean scale-total data. To determine the validity of the two sensors, it was measured simultaneously with force plate, and the comparision was done by Pearson's correlation. Results: The test-retest reliability showed excellent correlation for acceleration sensor, and it also showed excellent to good correlation for gyroscope sensor(p<0.05). The concurrent validity of smartphone inertial sensors showed a mostly poor to fair correlation for tandem-stance and one-leg-stance (p<0.05) and unacceptable correlation for the other postures (p>0.05). The gyroscope sensor showed a fair correlation for most of the RMS-Total data, and the other data also showed poor to fair correlation (p<0.05). Conclusions: The result indicates that both acceleration sensor and gyroscope sensor has good reliability, and that compared to force plate, acceleration sensor has unacceptable or poor correlation, and gyroscope sensor has mostly fair correlation.

• Journal of the Korea Society of Computer and Information
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• v.27 no.6
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• pp.23-31
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• 2022

Due to the recent outbreak of COVID-19 and an aging population and an increase in single-person households, the amount of time that household members spend doing various activities at home has increased significantly. In this study, we propose an algorithm for detecting anomalies in members of single-person households, including the elderly, based on the results of human movement and fall detection using an image sensor algorithm through home CCTV, an activity sensor algorithm using an acceleration sensor built into a smartphone, and a 2D LiDAR sensor-based LiDAR sensor algorithm. However, each single sensor-based algorithm has a disadvantage in that it is difficult to detect anomalies in a specific situation due to the limitations of the sensor. Accordingly, rather than using only a single sensor-based algorithm, we developed a fusion method that combines each algorithm to detect anomalies in various situations. We evaluated the performance of algorithms through the data collected by each sensor, and show that even in situations where only one algorithm cannot be used to detect accurate anomaly event through certain scenarios we can complement each other to efficiently detect accurate anomaly event.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.791-793
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• 2016

In the development of various types of sensor technology, the general users smartphone, the environment is increased, which can be seen in contact with the movement recognition device, such as a console game machine (Nintendo Wii), an increase in the user needs of the action recognition-based input device there is a tendency to have. Mouse existing behavior recognition, attached to the outside, is mounted in the form of mouse button is deformed, the left mouse was the role of the right button and a wheel, an acceleration sensor (or a gyro sensor) inside to, plays the role of a mouse cursor, is to manufacture a compact, there is a difficulty in operating the button, to apply a motion recognition technology is used to operate recognition technology only pointing cursor is limited. Therefore, in this paper, using a MEMS-based motion-les Koguni tion sensor (Motion Recognition Sensor), to recognize the behavior of the two points of the human body (thumb and forefinger), to generate the motion data, and this to the foundation, compared to the pre-determined matching table (moving and mouse button events cursor), and generates a control signal by determining, were studied the generated control signal input device of the computer wirelessly transmitting.

• Journal of Korea Multimedia Society
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• v.18 no.4
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• pp.565-574
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• 2015

Falls are the main cause of serious injuries and accidental deaths in people over the age of 65. Due to widespread adoption of smartphones, there has been a growing interest in the use of smartphones for detecting human behavior and activities. Modern smartphones are equipped with a wide variety of sensors such as an accelerometer, a gyroscope, camera, GPS, digital compass and microphone. In this paper, we introduce a new method that determines the fall direction of human subjects by analyzing the three axis components of acceleration vector.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.01a
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• pp.327-328
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• 2013

선풍기를 켜둔 채 잠시 자리를 비우거나 선풍기의 속도를 조절하고 싶은 경우가 자주 있다. 이 경우 선풍기가 가까이에 위치하는 경우는 문제가 없지만 벽걸이 형 선풍기 또는 선풍기가 사람 가까이에 위치하지 않는 경우, 선풍기의 속도 조절 또는 ON/OFF 제어를 위해 사용자가 불편함을 겪는 경우가 많다. 원격 제어장치가 추가로 제공되는 선풍기도 있지만 이 경우 원격제어장치를 가까이에 휴대하고 있어야 하는 불편함이 따른다. 그러나 스마트폰은 일반적으로 늘 가까이에 두고 있으므로 스마트폰을 이용하여 원격 제어 한다면 이런 문제가 해결될 것이다. 따라서 우리는 최근 많이 보급되고 있는 스마트폰을 이용하여 원격으로 쉽게 속도를 제어함과 동시에 초음파 센서를 이용하여 사람의 유무를 판단하여 선풍기의 ON/OFF를 자동으로 제어하여 절전도 할 수 있도록 장치와 스마트폰용 응용 프로그램을 개발하였다. 개발된 장치는 사용자 편의성이 높아짐과 동시에 전기도 절약되는 장점을 가진다.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1387-1394
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• 2017

As rate in an elderly population and expanding their range of activity rapidly increase, the demographics of the elderly population on a use of transportation also rise. Elderly pedestrians often find it difficult to react promptly to the traffic accidents as they are less perceptive of the dangers present under the situation. More than half of the elderly traffic accidents are elderly pedestrian accidents in road. Therefore, we design and implement smart safety hat for safety of elderly pedestrian. The smart safety hat binds stripe-shaped LED around a hat in order for a driver to perceive pedestrian easy and quickly. Features of smart safety hat include controlling the number of LEDs by using a light sensor and warning through vibration using a sound sensor. Also, we used Bluetooth to communicate with the smartphone to enable user customization of the light and numbers of LEDs.