• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.4
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• pp.892-898
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• 2017

This paper describes the development of AVN(Audio Video Navigation) software for vehicle information analysis and gesture recognition. The module that examine the CAN(Controller Area Network) data of vehicle in the designed software analyzes the driving state. Using classified information, the AVN software converge vehicle information and hand gesture information. As the result, the derived data is used to match the service step and to perform the service. The designed AVN software was implemented in HW platform that common used in vehicles. And we confirmed the operation of vehicle analysing module and gesture recognition in a simulated environment that is similar with real world.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.32 no.2
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• pp.157-160
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• 2019

P(VDF-TrFE-CFE) (Poly (vinylidene fluoride-trifluoroethylene-chlorofluoroethylene)), which exhibits a high electrostriction of about 7%, can transmit tactile output as vibration or displacement. In this study, we investigated the applicability of P(VDF-TrFE-CFE) to wearable piezoelectric actuators. The P(VDF-TrFE-CFE) layers were deposited through spin-coating, and interspaced with patterned Ag electrodes to fabricate a two-layer $3.5mm{\times}3.5mm$ device. This layered structure was designed and fabricated to increase the output and displacement of the actuator at low driving voltages. In addition, a laser vibrometer and piezoelectric force microscope were used to analyze the device's vibration characteristics over the range of ~200~4,200 Hz. The on-off characteristics were confirmed at a frequency of 40 Hz.

• Journal of Sensor Science and Technology
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• v.24 no.1
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• pp.69-77
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• 2015

The Microsoft Kinect is a motion sensing input device which is widely used for many motion recognition applications such as fitness, sports, and rehabilitation. Until now, most of remote rehabilitation systems with the Microsoft Kinect have allowed the user or patient to do rehabilitation or fitness by following the motion of a video screen. However in this paper we propose a smart remote rehabilitation system with the Microsoft Kinect motion sensor and a wearable ECG sensor which can allow patients to offer monitoring of the individual's performance and personalized feedback on rehabilitation exercises. The proposed noble smart remote rehabilitation is able to monitor and measure the state of the patient's condition during rehabilitation exercise, and transmits it to the prescriber. This system can give feedback to a prescriber, a doctor and a patient for improving and recovering motor performance. Thus, the efficient rehabilitation training service can be provided to patient in response to changes of patient's condition during exercise.

• Journal of the Korean Physical Society
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• v.73 no.11
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• pp.1760-1763
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• 2018

To attain power generation with body heat, the thermal resistance matched design of the thermoelectric generator was the principal factor which was not critical in the case of thermoelectric generator for the waste heat generation. The dimension of thermoelectric legs and the number of thermoelectric leg-pairs dependent output power performances of the thermoelectric generator on the human wrist condition was simulated using 1-dimensional approximated heat flow equations with the temperature dependent material coefficients of the constituent materials and the dimension of the substrate. With the optimum thermoelectric generator design, thermoelectric generator modules were fabricated by using newly developed fabrication processes, which is mass production possible. The electrical properties and the output power characteristics of the fabricated thermoelectric modules were characterized by using a home-made test set-up. The output voltage of the designed thermoelectric generator were a few tens of millivolts and its output power was several hundreds of microwatts under the conditions at the human wrist. The measured output voltage and power of the fabricated thermoelectric generator were slightly lower than those of the designed thermoelectric generator due to several reasons.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.11a
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• pp.261-261
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• 2007

Wearable and ubiquitous micro systems will be greatly growing and their related devices should be self-powered in order to avoid the replacement of finite power sources, for example, by scavenging energy from the environment. With ever reducing power requirements of both analog and digital circuits, power scavenging approaches are becoming increasingly realistic. One approach is to drive an electromechanical converter from ambient motion or vibration. Vibration-driven generators based on electromagnetic, electrostatic and piezoelectric technologies have been demonstrated. Among various generator types proposed so far, piezoelectric generator possesses considerable potential in micro system. To overcome low mechanical-to- electric energy conversion, the piezoelectric device should activate in resonance mode in response to external vibration. Normally, the external vibration excretes at low frequency ranging 0.1 to 200 Hz, whereas the resonant frequencies of the devices are fixed as constant. Therefore, keeping their resonant mode in varying external vibration can be one of important points in enhancing the conversion efficiency. We investigated the possibility of use of multi-bender type piezoelectric devices. To match the external vibration frequency with the device resonant frequency, the various devices with different resonant frequency were chosen. Under an external vibration acceleration of 0.1G at 120 Hz, the device exhibited a peak-to-peak voltage of 2.8 V and a power of 0.5 mw in resonance mode.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.91-98
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• 2019

Accidents of worker that occur in isolated places are difficult to rescue, unlike general construction accidents. There is a problem of communication limitation when an accident occurs in an isolated place. Also, it is difficult to search the accident place due to the absence of CCTV. In order to solve these problems, this paper proposes a device that combines IoT technology with a safety helmet, which must be worn in the workplace. The proposed device additionally designs and implements a real-time PPG(Photoplethysmography) sensor, body temperature sensor, accelerometer sensor and a camera sensor on the helmet. The proposed helmet system allows the user and the control center to monitor the state of the worker. In addition, when an abnormal biological signal or fall occurs to the worker, the image is transmitted to the control center. By using the proposed system, it is possible to check the status of the worker in real time, so that it has an advantage that it can cope with the accident quickly.

• Journal of the Korea Society of Computer and Information
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• v.22 no.2
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• pp.9-19
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• 2017

Useful continuous sensing applications are increasingly emerging as a new class of mobile applications. Meanwhile, open, multi-use sensor devices are newly adopted beyond smartphones, and provide huge opportunities to expand potential application categories. In this upcoming environment, uncoordinated use of sensor devices would cause severe imbalance in power consumption of devices, and thus result in early shutdown of some sensing applications depending on power-hungry devices. In this paper, we propose EnergyCordy, a novel inter-device energy use coordination system; with a system-wide holistic view, it coordinates the energy use of concurrent sensing applications over multiple sensor devices. As its key approach, we propose a relaxed sensor association; it decouples the energy use of an application from specific sensor devices leveraging multiple context inference alternatives, allowing flexible energy coordination at runtime. We demonstrated the effectiveness of EnergyCordy by developing multiple example applications over custom-designed wearable senor devices. We show that EnergyCordy effectively coordinates the power usage of concurrent sensing applications over multiple devices and prevent undesired early shutdown of applications.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.2
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• pp.129-132
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• 2017

A conventional pulse oximeter has high power consumption; thus, its mobility is severely limited. In this paper, we discuss the drawbacks of the existing pulse oximeters and propose a new ultra-low-power pulse oximeter that supports wireless data transmission for remotely monitoring vital signs, such as peripheral capillary oxygen saturation (SpO2) and beats per minute (BPM). We could notably reduce power consumption by using a low-frequency single clock in all well-customized modules. Also, our device is publicly certified, and thus, possibly engaged in clinical trials for commercial use.

• 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.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.2
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• pp.127-137
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• 2011

We have developed a wearable device that can convert sensor data into real-time step counts and activity levels. Sensor data on gait were acquired using a triaxial accelerometer. A test was performed according to a test protocol for different walking speeds, e.g., slow walking, walking, fast walking, slow running, running, and fast running. Each test was carried out for 36 min on a treadmill with the participant wearing a portable gas analyzer (K4B2), an Actical device, and the device developed in this study. The signal vector magnitude (SVM) was used to process the X, Y, and Z values output by the triaxial accelerometer into one representative value. In addition, for accurate step-count detection, we used three algorithms: an heuristic algorithm (HA), the adaptive threshold algorithm (ATA), and the adaptive locking period algorithm (ALPA). A regression equation estimating the energy expenditure (EE) was derived by using data from the accelerometer and information on the participants. The recognition rate of our algorithm was 97.34%, and the performance of the activity conversion algorithm was better than that of the Actical device by 1.61%.