• Journal of KIISE
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• v.42 no.3
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• pp.295-306
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• 2015

MOnCa2 is a framework for building intelligent smartphone applications based on smartphone sensors and ontology reasoning. In previous studies, MOnCa determined and inferred user situations based on sensor values represented by ontology instances. When this approach is applied, recognizing user space information or objects in user surroundings is possible, whereas determining the user's physical context (travel behavior, travel destination) is impossible. In this paper, MOnCa2 is used to build recognition models for travel behavior and routes using smartphone sensors to analyze the user's physical context, infer basic context regarding the user's travel behavior and routes by adapting these models, and generate high-level context by applying ontology reasoning to the basic context for creating intelligent applications. This paper is focused on approaches that are able to recognize the user's travel behavior using smartphone accelerometers, predict personal routes and destinations using GPS signals, and infer high-level context by applying realization.

• Journal of Satellite, Information and Communications
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• v.11 no.3
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• pp.18-23
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• 2016

Internet of things (IoT) is revolutionizing in the patient-Centered medical monitoring and management by authorizing the Smartphone application and data analysis with medical centers. The network connectivity is basic requirement to collect the observed human beings' health information from Smartphone to monitor the health from IoT medical devices in personal healthcare. The IoT environment built in Smartphone is very effective and does not demand infrastructure. This paper presents the smart phone deployed personal IoT architecture for Non-Invasive ECG Capturing. The adaptable IoT medical device cum Gateway is used for personal healthcare with big data storage on cloud configuration. In this approach, the Smartphone camera based imaging technique used to extract the personal ECG waveform and forward it to the cloud based big data storage connectivity using IoT architecture. Elaborated algorithm allows for efficient ECG registration directly from face image captured from Smartphone or Tablet camera. The profound technique may have an exceptional value in monitoring personal healthcare after adequate enhancements are introduced.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.3
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• pp.489-498
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• 2012

Smartphone schedule management applications are widely used in mobile environment with the help of their effective user interface and high user accessibility. Unfortunately, since the applications do not consider frequent context changes like user status or location, they can not support schedule management which reflects the changes. In this paper, we propose an intelligent notification service which gathers context information of users such as location and status, and actively provides appropriate services according to the context information. Based on the notification service, we also present a smartphone application for smart schedule management. Using the GPS sensor of a smartphone, the service can be aware of the status of the user through location and schedule information of the user. Also, according to the changed contexts, it provides proactive notifications to user.

• Journal of Korean Society of Forest Science
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• v.110 no.1
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• pp.72-81
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• 2021

We developed smartphone application and web application server to acquire and effectively manage tree measurement information. Smartphone applications can measure tree height, diameter at breast height (DBH), azimuth, altitude, slope, and positional coordinates using augmented reality (Google AR core) and motion sensors. The web application server effectively manages and stores measurement information. To evaluate the accuracy of information acquired using a smartphone, 90 Korean pine trees (Pinus koraiensis) were randomly selected from a natural mixed forest, with a total of 90 representative trees randomly collected from a natural mixed forest. Then, height and DBH were measured using a Haglof Vertex Laser Hypsometer and caliper. Comparisons of the results indicated significant results at the 95% level and a very high average correlation of 0.972 for both tree height and DBH. In terms of DBH, the average errors were 0.6745 cm and 1.0139 cm for artificial coniferous and natural mixed forests, respectively.

• Tribology and Lubricants
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• v.30 no.3
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• pp.168-176
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• 2014

This paper presents an engine oil change warning algorithm based on the test results of a small dip-stick-gage-type engine-oil-deterioration-detection sensor, software to realize the algorithm and a display device to apply the software. The algorithm determines the engine oil deterioration condition from the rate of change in the dielectric constant based on the average measured capacitance at $80^{\circ}C$ after the engine stops. The rate of change in the dielectric constant at the time for oil change correlates with the time that one of recommended warning limits for engine oil physical properties such as TAN (Total Acid Number), TBN (Total Base Number) and viscosity is first reached. At this point, a warning signal for oil change appears on the display device like a smart-phone or individual display device. The frames of smartphone app have three stages. The user can directly input all of the thresholds into the frame of the smartphone app. The screen of the display device comprises one frame for each warning signature with the related message. The user can input the thresholds to the device through a USB cable connected to a personal computer.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.3
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• pp.273-284
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• 2020

The performance of Global Navigation Satellite System (GNSS) chipset and Inertial Measurement Unit (IMU) sensors embedded in smartphones for location-based services (LBS) is limited due to the economic reasons for their mass production. Therefore, it is necessary to efficiently process the output data of the smartphone's embedded sensors in order to derive the optimum navigation values and, as a previous step, output performance of smartphone embedded sensors needs to be verified. This paper analyzes the navigation performance of such devices by processing the raw measurements data output from smartphones. For this, up-to-dated versions of smartphones provided by Samsung (Galaxy s10e) and Xiaomi (Mi 8) are used in the test experiment to compare their performances and characteristics. The GNSS and IMU data are extracted and saved by using an open market application software (Geo++ RINEX Logger & Mobile MATLAB), and then analyzed in post-processing manner. For GNSS chipset, data is extracted from static environments and verified the position, Carrier-to-Noise (C/N₀), Radio Frequency Interference (RFI) performance. For IMU sensor, the validity of navigation and various location-based-services is predicted by extracting, storing and analyzing data in static and dynamic environments.

• Journal of Digital Contents Society
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• v.18 no.3
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• pp.559-565
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• 2017

A lot of studies have been conducted to recognize the motion state or behavior of the user using the acceleration sensor built in the smartphone. In this paper, we applied the neural networks to the 3-axis acceleration information of smartphone to study human behavior. There are performance issues in applying time series data to neural networks. We proposed a multi-temporal neural networks which have trained three neural networks with different time windows for feature extraction and uses the output of these neural networks as input to the new neural network. The proposed method showed better performance than other methods like SVM, AdaBoot and IBk classifier for real acceleration data.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.781-790
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• 2024

As modern people's interest in exercise and health increases, the demand for exercise-related information and devices is increasing, and exercising in the wrong posture can lead to body imbalance and injury. Therefore, in this study, the purpose of this study is to correct the posture for health promotion and injury prevention through the correct exercise posture of users. It was developed using Arduino Uno R3, a pressure sensor, and an acceleration sensor as the main memory device of the system. The pressure sensor was used to determine the squat posture, and the acceleration sensor was used to determine three types of gait: normal step, nasolabial step, and saddle step. Data is transmitted to a smartphone through a Bluetooth module and displayed on an app to guide the user in the correct exercise posture. The gait was determined based on the 20˚ angle at which the foot was opened, and the correct squat posture was compared with the ratio of the pressure sensor values of the forefoot and hindfoot based on the data of the skilled person. Therefore, based on an experiment with about 90% accuracy when determining gait and 95% accuracy based on a 7:3 ratio of pressure sensor values in squat posture, a system was established to guide users to exercise in the correct posture by checking in real time through a smartphone application and correcting exercise in the wrong posture.

• Journal of Sensor Science and Technology
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• v.31 no.2
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• pp.131-137
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• 2022

Crimes related to illegal cameras are steadily increasing and causing social problems. Owing to the development of camera technology, the miniaturization and high performance of illegal cameras have caused anxiety among many people. This study is for detecting hidden cameras effectively such that they could not be easily detected by human eyes. An image sensor-based module with 940 nm wavelength infrared detection technology was developed, and an image processing algorithm was developed to selectively detect illegal cameras. Based on the Android smartphone environment, image processing technology was applied to an image acquired from an infrared camera, and a detection sensor module that is less sensitive to ambient brightness noise was studied. Experiments and optimization studies were conducted according to the Gaussian blur size, adaptive threshold size, and detection distance. The performance of the infrared image-based illegal camera detection sensor module was excellent. This is expected to contribute to the prevention of crimes related to illegal cameras.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.11
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• pp.1011-1017
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• 2012

Augmented Reality is being widely used not only for Smartphone users but also in industries such as maintenance, construction area. With smartphone, due to the low localization accuracy and the requirement of special infrastructure, current LBS (Localization Based Service) is limited to show P.O.I. (Point of Interest) nearby. Improvement of IMU (Inertial Measurement Unit) based deadreckoning is presented in this paper. Additional sensors such as the magnetic compass and magnetic flux sensors are used as well as the accelerometer and the gyro for getting more movement information. Based on the pedestrian movement, appropriate sensor information is selected and the complementary filter is used in order to enhance the accuracy of the localization. Additional sensors are used to measure the movements of the upper body and the head and to provide the user's line of sight.