• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.6
• /
• pp.547-552
• /
• 2015

In this paper, an ARS-EKF based motion counting algorithm for repetitive exercises such as calisthenics is proposed using a smartwatch. Raw sensor signals from accelerometers and gyroscopes are widely used for conventional smartwatch counting algorithms based on pattern recognition. However, generated features from raw data are not intuitive to reflect the movement of motions. The proposed motion counter algorithm is composed of navigation based feature generation and counting with error correction. The candidate features for each activity are velocity and attitude calculated through an ARS-EKF algorithm. In order to select those features which reveal the characteristics of each motion, an exercise frame from the initial sensor frame is introduced. Counting processes are basically based on the zero crossing method, and misdetected counts are eliminated via simple classification algorithms considering the frequency of the counted motions. Experimental results show that the proposed algorithm efficiently and accurately counts the number of exercises.

• Journal of Korea Multimedia Society
• /
• v.21 no.8
• /
• pp.982-990
• /
• 2018

In recent years, many wearable devices and mobile apps related to life care have been developed, and a service for measuring the movement during walking and showing the amount of exercise has been provided. However, they do not measure walking in detail, so there may be errors in the total calorie consumption. If the user's behavior is measured by a multi-axis sensor and learned by a machine learning algorithm to recognize the kind of behavior, the detailed operation of walking can be autonomously distinguished and the total calorie consumption can be calculated more than the conventional method. In order to verify this, we measured activities and created a model using a machine learning algorithm. As a result of the comparison experiment, it was confirmed that the average accuracy was 12.5% or more higher than that of the conventional method. Also, in the measurement of the momentum, the calorie consumption accuracy is more than 49.53% than that of the conventional method. If the activity recognition is performed using the wearable device and the machine learning algorithm, the accuracy can be improved and the energy consumption calculation accuracy can be improved.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.10
• /
• pp.1-9
• /
• 2022

In this paper, we propose the Deep Learning-Based Companion Animal Abnormal Behavior Detection Service, which using video and sensor data. Due to the recent increase in households with companion animals, the pet tech industry with artificial intelligence is growing in the existing food and medical-oriented companion animal market. In this study, companion animal behavior was classified and abnormal behavior was detected based on a deep learning model using various data for health management of companion animals through artificial intelligence. Video data and sensor data of companion animals are collected using CCTV and the manufactured pet wearable device, and used as input data for the model. Image data was processed by combining the YOLO(You Only Look Once) model and DeepLabCut for extracting joint coordinates to detect companion animal objects for behavior classification. Also, in order to process sensor data, GAT(Graph Attention Network), which can identify the correlation and characteristics of each sensor, was used.

• Journal of Fashion Business
• /
• v.22 no.6
• /
• pp.94-104
• /
• 2018

The objective of this study was to develop a wearable systems that protect users, based on sensors that are easy to use, from accidents caused by harmful gases in the operator's poor working environment or the risk of ultraviolet rays during outdoor activities. By developing smart wappen with Light Emitting Diode (LED) light alarm function including UV sensor and gas sensor and central processing unit, systems that are applied to daily wear and work clothes to explore the possibility of user-centered, harmful environment monitoring products in real time were proposed. Each sensor was applied to sportswear and work clothes and the wappen system consisted of lightweight and thin form as a whole. Wappen to cover the device had one sheet cover on the front and another cover from the inside to form a sandwich like formation. Wappen was made in the same form as regular clothes that doesn't damage the exterior then a removable wappen system was developed using Velcro and snap methods to enable the separation of device or the exchange of batteries. De-adhesion method can occur in two ways, from the outside and from the inside, so the design is selected depending on the application. This study shows the significance of the development of sensor-based smart clothing, in that it presented a universal model for users.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.12 no.4
• /
• pp.715-724
• /
• 2017

Today all kinds of smart devices are being developed with various researches on wearable devices that support smart computing on the human body. Skin diseases continue to rise including freckles, pimples, atopy, and scalp trouble due to the environmental and genetic factors, and people pay bigger medical bills to treat their skin diseases. There is thus a need to develop a smart-phone or table-based smart healthcare imaging system of high portability and diagnostic accuracy capable of analyzing and managing various skin problems related to skin care. This study proposed an integrated system combining the Smart Mi Band, a wearable device using moisture and UV sensors based on IoT, on the hardware part with the sensor information monitoring software.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.13 no.1
• /
• pp.221-228
• /
• 2018

Big data is an important keyword in World's Fourth Industrial Revolution in public and private division including IoT(Internet of Things), AI(Artificial Intelligence) and Cloud system in the fields of science, technology, industry and society. Big data based on services are available in various fields such as transportation, weather, medical care, and marketing. In particular, in the field of sports, various types of bio-signals can be collected and managed by the appearance of a wearable device that can measure vital signs in training or rehabilitation for daily life rather than a hospital or a rehabilitation center. However, research on big data with vital signs from wearable devices for training and rehabilitation for baseball players have not yet been stimulated. Therefore, in this paper, we propose a system for baseball infield and outfield players, especially which can store and analyze the momentum measurement vital signals based on big data.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.10
• /
• pp.139-145
• /
• 2021

In this paper, by using the cattle feed intake, rumination, and in heat monitoring technology, RFI (Residual Feed Intake) monitoring and wearable devices and PCs for predicting abnormalities in budding target web and smart A monitoring system using a phone application was designed and implemented. With the development of this system, the farmer is expected to increase economic efficiency. By analyzing the feed intake, it is possible to identify the difference between the recommended feed amount based on the cow's weight and the feed amount consumed by the cow, and it is expected that early detection of metabolic disorders (abnormality of metabolism) is possible. Farmers using the results of this thesis can distinguish the cows with the most efficient performance, and the 6-axis motion sensor signals input from the wearable device attached to the cow's skin (neck) and the microphone attached to the wearable device. It is possible to measure the cow's rumination and feed intake through the sound of the cow's throat. In the future, improvements will be made to measure additional vital signs such as heart rate and respiration.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2016.02a
• /
• pp.389.1-389.1
• /
• 2016

The flexible solid state device has been widely studied as portable and wearable device applications such as display, sensor and curved circuits. A zero-bias operation without any external power consumption is a highly-demanding feature of semiconductor devices, including optical communication, environment monitoring and digital imaging applications. Moreover, the flexibility of device would give the degree of freedom of transparent electronics. Functional and transparent abrupt p/n junction device has been realized by combining of p-type NiO and n-type ZnO metal oxide semiconductors. The use of a plastic polyethylene terephthalate (PET) film substrate spontaneously allows the flexible feature of the devices. The functional design of p-NiO/n-ZnO metal oxide device provides a high rectifying ratio of 189 to ensure the quality junction quality. This all transparent metal oxide device can be operated without external power supply. The flexible p-NiO/n-ZnO device exhibit substantial photodetection performances of quick response time of $68{\mu}s$. We may suggest an efficient design scheme of flexible and functional metal oxide-based transparent electronics.

• Journal of the Korea Society of Computer and Information
• /
• v.11 no.6 s.44
• /
• pp.133-141
• /
• 2006

Recently many wearable computers have been developed. But they still have many user interface problems from both an input and output perspective. This paper presents a wearable user interface based on EOG(electrooculogram) sensing circuit and marker recognition. In the proposed user interface, the EOG sensor circuit which tracks the movement of eyes by sensing the potential difference across the eye is used as a pointing device. Objects to manipulate are represented human readable markers. And the marker recognition system detects and recognize markers from the camera input image. When a marker is recognized, the corresponding property window and method window are displayed to the head mounted display. Users manipulate the object by selecting a property or a method item from the window. By using the EOG sensor circuit and the marker recognition system, we can manipulate an object with only eye movement in the wearable computing environment.

• Journal of rehabilitation welfare engineering & assistive technology
• /
• v.11 no.3
• /
• pp.253-259
• /
• 2017

Conventional upper extremity rehabilitation paradigm after brain injury has several shortcomings that is monotonous, simple, and repetitive in exercises over a long period of time, thereby causing training efficiency to decline as a consequence of low interest and participation. To resolve this issue, this paper proposes a new rehabilitative program integrating a wearable device integrated with EMG and motion sensor and a mobile game for the upper limbs' rehabilitative training. The developed wearable device is manufactured in the form of band, making it easy to wear. The mobile game is designed to enable rehabilitative training through games reflective of flexion, extension, abduction, and adduction identified by motion sensors along with grasp motion recognized by EMG signals measured from the wearable device. It also provides a tailored rehabilitative environment suitable for individual patients based on difficulty adjustments. As a consequence of applying the developed program to 14 brain injury in need of the upper limb rehabilitation and taking surveys on the utility of the developed rehabilitative program, the responses indicated that the developed rehabilitative program is far much more interesting and fun than the conventional rehabilitative program, further to the desire of those surveyed to reuse the developed program in the future.