• Convergence Security Journal
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• v.24 no.2
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• pp.55-61
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• 2024

With the development of IoT technology, wearable services have also developed rapidly. Wearable devices required for this service are used as sensors and controllers in the form of smart bands. Wearable devices implement very concise SWlogic for possible long-term use and use wireless communication protocols to improve convenience. However, because this wearable device aims to be lightweight, it is more vulnerable to security than terminals used for other information services. Many smart healthcare or smart medical services are passive or do not apply security technology. By exploiting this security environment, attackers can obtain or modify important information through access to wearable devices. In this study, we analyzed the technical operating environment of wearable services and identified authentication information reuse attacks, BIAS attacks, battery drain attacks and firmware attacks on wearable devices. And we analyzed the mechanism of each security threat and confirmed the attack effect. In this study, we presented a response plan to respond to the identified security threats. When developing wearable services, it is expected that safer services can be built if the response plan proposed in this study is considered.

• Journal of Engineering Education Research
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• v.19 no.1
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• pp.31-36
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• 2016

These days, ICT-related products are pouring out due to development of mobile technology and increase of smart phones. Among the ICT-related products, wearable devices are being spotlighted with the advent of hyper-connected society. In this paper, a body-attached type wearable device using EMG(electromyography) sensors is studied. The research field of EMG sensors is divided into two parts. One is medical area and another is control device area. This study corresponds to the latter that is a method of transmitting user's manipulation intention to robots, games or computers through the measurement of EMG. We used commercial device MYO developed by Thalmic Labs in Canada and matched up EMG of arm muscles with gesture controller. In the experiment part, first of all, various arm motions for controlling devices are defined. Finally, we drew several distinguishing kinds of motions through analysis of the EMG signals and substituted a joystick with the motions.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.214-220
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• 2019

Hearing impaired people are apt to be in danger because they can't detect danger with sound. Hearing impaired people have less risk-detection ability than non-disabled people because of lack of hearing. There are many devices to help the hearing impaired, such as hearing aids. A hearing aids can be helpful, but it may not be available depending on the degree or type of hearing loss for example, to the hearing-impaired people with little remaining hearing of high frequencies, ordinary hearing aids are not very useful for understanding the high frequency consonants and it requires a high cost, from thousands to tens of thousands of dollars. Also, it is difficult for the underprivileged, such as the low-income bracket and the elderly, to use them because they are difficult to manage. Therefore, this paper describes the development of low-cost wearable device to assistant a hearing-impaired people using Arduino. Also, it accepts values from switches or sensors and can control external electronic devices such as LEDs and motors to create objects that can interact with the environment. In this is paper, through sound sensors, the ambient sound was taken as an analogue value and transmitted to the aduino board, and the vibration motor was operated when the noise was generated, so that the user could be aware of the occurrence of danger.

• The Journal of Korea Robotics Society
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• v.9 no.3
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• pp.133-139
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• 2014

This paper proposes a wearable and motorized crutch control system for the patients using the conventional crutches. The conventional crutches have a few disadvantages such as the inconvenience caused by the direct contact between the ground and the armpit of the patients, and unstable gait patterns. In order to resolve these problems, the motorized crutch is designed as a wearable type on an injured lower limb. In other words, the crutch makes the lower limb to be moved forward while supporting the body weight, protecting the lower limb with frames, and rotating a roller equipped on the bottom of the frames. Also the crutch is controlled using the electromyography and two force sensing resistor (FSR) sensors. The electromyography is used to extract the walking intention from the patient and the FSR sensors to classify the stance and swing phases while walking. As a result, the developed crutch makes the patients walk enabling both hands to be free, as if normal people do.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2138-2142
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• 2005

A wearable sensor system is proposed as a man-machine interface to control a device for walking assistance. The sensor system is composed of small sensors to detect the information about the user's body motion such as the activity level of skeletal muscles and the acceleration of each body parts. Each sensor includes a microcomputer and all the sensors are connected into a network by using the serial communication function of the microcomputer. The whole network is integrated into a belt made of soft fabric, thus, users can put on/off very easily. The sensor system is very reliable because of its decentralized network configuration. The body information obtained from the sensor system is used for controlling the assisting device to achieve a comfortable and an effective walking training.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.318-330
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• 2015

In wearable sensor systems (WSSs), sensor nodes are deployed around human body parts such as the arms, the legs, the stomach, and the back. These sensors have limited lifetimes because they are battery-operated. Thus, transmission power control (TPC) is needed to save the energy of sensor nodes. The TPC should control the transmission power level (TPL) of sensor nodes based on current channel conditions. However, previous TPC algorithms did not precisely estimate the channel conditions. Therefore, we propose a new TPC algorithm that uses an accelerometer to directly measure the current channel condition. Based on the directly measured channel condition, the proposed algorithm adaptively adjusts the transmission interval of control packets for updating TPL. The proposed algorithm is efficient because the power consumption of the accelerometer is much lower than that of control packet transmissions. To evaluate the effectiveness of our approach, we implemented the proposed algorithm in real sensor devices and compared its performance against diverse TPC algorithms. Through the experimental results, we proved that the proposed TPC algorithm outperformed other TPC algorithms in all channel environments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.141-143
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• 2021

The Activity patterns of animal species are difficult to access and the behavior of freely moving individuals can not be assessed by direct observation. As it has become large challenge to understand the activity pattern of animals such as dogs, and cats etc. One approach for monitoring these behaviors is the continuous collection of data by human observers. Therefore, in this study we assess the activity patterns of dog using the wearable sensors data such as accelerometer and gyroscope. A wearable, sensor -based system is suitable for such ends, and it will be able to monitor the dogs in real-time. The basic purpose of this study was to develop a system that can detect the activities based on the accelerometer and gyroscope signals. Therefore, we purpose a method which is based on the data collected from 10 dogs, including different nine breeds of different sizes and ages, and both genders. We applied six different state-of-the-art classifiers such as Random forests (RF), Support vector machine (SVM), Gradient boosting machine (GBM), XGBoost, k-nearest neighbors (KNN), and Decision tree classifier, respectively. The Random Forest showed a good classification result. We achieved an accuracy 86.73% while the detecting the activity.

• Journal of Korea Multimedia Society
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• v.23 no.9
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• pp.1147-1154
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• 2020

Nowadays, one of the most common diseases is chronic mental fatigue syndrome. This can be caused by many factors, such as busy life, heavy workload, high population density, and adverse technological impact. Most office workers and students who are sitting all day long while being exposed to this kind of environments are likely to be involved in the mental illness. Therefore, to prevent the illness, it has been highly required to design a device that enables mental fatigue to be monitored continuously without human intervention. This paper proposes a linear regression method to reliably estimating the level of human mental fatigue using wearable physiological sensors, with an estimation error of 0.852. Also, this paper presents an Android application that is able to check mental health conditions in daily life.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1046-1048
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• 2003

Recently, various researches in respect of the positioning technologies using satellites and the other sensors have made location-based services (LBS) more common and accurate. Consequently, concern about position information has been increasing. However, since these positioning systems only focus on user's position, it is difficult to know the user's attitude or detailed behaviors at the specific position. It is worthy to study on how to acquire such human attitude or behavior, because those information is useful to know the context of the user. In this paper, the sensor unit consisting of three dimensional accelerometer was attached to human body, and autonomously measured the perpendicular acceleration of ordinary human behaviors including activity modes such as walking, running, and transportation mode using transportation such as a train, a bus, and an elevator. Subsequently, using the classified measurement results, the method to identify the human activity modes was proposed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.281-282
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• 2019

In recent years welfare of animals is the biggest challenge because animals, especially dogs are widely recognized as pet as well as they are using as service animals. So, for the wellbeing of the dog it is necessary to perform objective assessment to track their behavior in everyday life. In this paper, we have proposed an automatic behavior assessment system for dogs based on a neck worn and tail worn accelerometer and gyroscope platform, and data analysis techniques that recognize typical dog activities. We evaluate the system based on the analysis of 8 behavior traits in 3 dogs, incorporating 2 breeds of various sizes. Our proposed framework able to reproduce the manual assessment that is based on the video recording which is treated as gold standard that exhibits the real-life use case of automated dog behavior analysis.