• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.619-621
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• 2016

스마트폰에는 여러 센서들이 내장되어있다. 가속도 센서는 그 중 하나로 장치의 가속도를 측정한다. 세 가지의 축으로 측정되는 센서 값을 종합하고 이를 기반으로 독거노인들의 생활 패턴 분석을 통해 시간대에 따라 센서 값의 변화를 측정하여 장시간 센서 값이 변하지 않을 시 보호자에게 메시지가 자동으로 전송된다. 이를 통해 간병인은 보호자의 신변을 확인할 수 있다.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.139-148
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• 2020

Walking is one of the most natural and repetitive actions we do in our daily lives. However, many modern people have problems with shoulders, back and spine due to incorrect walking habits. Therefore, it is becoming important to diagnose and correct wrong walking habits, for example, in-toeing, out-toeing, etc. early, which can be a precursor to various diseases. In this study, we developed the system to diagnose and prevent incorrect gait by grasping and analyzing the angle and muscle activity of the foot according to the typical wrong gait type through MPU 6050 acceleration sensor and the surface EMG sensor. Through a smartphone, numerical and visualization screens based on walking can be used to represent the angle of the feet, real-time EMG values, and even the number of steps. The correction effect was enhanced by improving the cognitive ability through a system that allows individuals to easily diagnose gait through smart devices and improve them according to their own problems.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.3
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• pp.35-42
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• 2012

The database for driving patterns can be utilized in various system such as automatic driving system, driver safety system, and it can be helpful to monitor driving style. Therefore, we propose a driving pattern recognition system in which the sensor streams from a smartphone are recorded and used for recognizing driving events. In this paper we focus on the driving pattern recognition that is an essential and preliminary step of driving style recognition. We divide input sensor streams into 7 driving patterns such as, Left-turn(L), U-turn(U), Right-turn(R), Rapid-Braking(RB), Quick-Start(QS), Rapid-Acceleration (RA), Speed-Bump(SB). To classify driving patterns, first, a preprocessing step for data smoothing is followed by an event detection step. Last the detected events are classified by DTW(Dynamic Time Warping) algorithm. For assisting drivers we provide the classified pattern with the corresponding video stream which is recorded with its sensor stream. The proposed system will play an essential role in the safety driving system or driving monitoring system.

• Journal of Korea Game Society
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• v.14 no.2
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• pp.67-76
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• 2014

Recently there have been many interests on tangible sport games that can recognize the motions of players. In this paper, we propose essential technologies required for tangible games, which are methods for swing motion recognition and the calculation of shuttle cock's trajectory. When a user carries out a badminton swing while holding a smartphone with his hand, the motion signal generated by smartphone-embedded acceleration sensors is transformed into a feature vector through a Daubechies filter, and then its swing type is recognized using a k-NN based method. The method for swing motion presented herein provides an advantage in a way that a player can enjoy tangible games without purchasing a commercial motion controller. Since a badminton shuttle cock has a particular flight trajectory due to the nature of its shape, it is not easy to calculate the trajectory of the shuttle cock using simple physics rules about force and velocity. In this paper, we propose a method for calculating the flight trajectory of a badminton shuttle cock in which the wind effect is considered.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.127-135
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• 2014

Interactive-typed devices and contents that have been often applied in the field of entertainment and game are the technology that allows you to maximize the enjoyment and participation of users through the interaction of each. In this paper, we designed an interactive-typed smartphone app that is based on the Android platform, implemented the wearable Bluetooth device to control via a interactive interface with a vibration sensor and three-axis acceleration sensor. We tested the functionality and 3-axis motion's operability by using smartphone app, interface interactive-typed device that has been developed, prove useful as a wearable Bluetooth device that has the convenience of the user. Further, it is shown that by implementing the optimized protocol of the sensor data transfer over Bluetooth, it is possible to reduce the malfunction of the content of the smart phone.

• Journal of IKEEE
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• v.24 no.4
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• pp.1188-1191
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• 2020

In this paper, automotive safety and convenience service is proposed based on bluetooth and smart watch. The proposed service performs accident detection, kidnapping detection, kid-left-alone-in-car detection, parking location recording, and smart key function. Conventional smartphone services often fails to precisely recognize accident and kidnapping situations since smartphone is located on the dashboard or in the bag. On the contrary, smartwatch recognizes accident and kidnapping situations more precisely since it is always worn on the wrist with hearbeat monitoring. The proposed service recognise various situations around drives and passengers using acceleration sensor, GPS sensor, heartbeat sensor and bluetooth link status. It also performs accident notice, sound recording, and other necessary actions. It also performs door opening, door closing, hazard light flickering, and other necessary actions using OBD-II connection to the vehicle.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1224-1230
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• 2020

Since the spread of smart phones, interest in wearable devices has increased and diversified, and is closely related to the lives of users, and has been used as a method for providing personalized services. In this paper, we propose a method to detect the user's behavior by applying information from a 3-axis acceleration sensor and a 3-axis gyro sensor embedded in a smartphone to a convolutional neural network. Human behavior differs according to the size and range of motion, starting and ending time, including the duration of the signal data constituting the motion. Therefore, there is a performance problem for accuracy when applied to a convolutional neural network as it is. Therefore, we proposed a Time-Division Feature Fusion Convolutional Neural Network (TDFFCNN) that learns the characteristics of the sensor data segmented over time. The proposed method outperformed other classifiers such as SVM, IBk, convolutional neural network, and long-term memory circulatory neural network.

• 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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• 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 Multimedia Information System
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• v.2 no.2
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• pp.233-240
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• 2015

Mobile devices are becoming increasingly sophisticated and the latest generation of smartphones now incorporates many diverse and powerful sensors. These sensors include acceleration sensor, magnetic field sensor, light sensor, proximity sensor, gyroscope sensor, pressure sensor, rotation vector sensor, gravity sensor and orientation sensor. The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications. In this paper, we describe and evaluate a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity that a user is performing. To implement our system, we collected labeled accelerometer data from 10 users as they performed daily activities such as "phone detached", "idle", "walking", "running", and "jumping", and then aggregated this time series data into examples that summarize the user activity 5-minute intervals. We then used the resulting training data to induce a predictive model for activity recognition. This work is significant because the activity recognition model permits us to gain useful knowledge about the habits of millions of users-just by having them carry cell phones in their pockets.