• Journal of Food Hygiene and Safety
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• v.34 no.6
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• pp.542-550
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• 2019

To detect Escherichia coli from agri-food and production environments, a device based on IoT (internet of things) technology that can check test results in real time on a mobile phone has been developed. The efficiency of the developed device, which combines an incubator equipped with a UV lamp, a high-resolution camera and software to detect E. coli in the field, was evaluated by measuring the device's temperature, detection limit, and detection time. The device showed a difference between its programmed temperature setting and actual temperature of about 1.0℃. In a detection limit test performed with a single-colony inoculation, a color change to yellow and a florescent signal were detected after 12 and 15 h incubations, respectively. The incubation time also decreased along with increasing bacteria levels. When applying the developed method and device to various samples, including utensils, gloves, irrigation water, seeds, and vegetables, detection rates of E. coli using the device were higher than those of the Korean Food Code method. These results show that the developed protocol and device can efficiently detect E. coli from agri-food production environments and vegetables.

• Journal of Internet Computing and Services
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• v.17 no.2
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• pp.59-65
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• 2016

The result of analyzing the cognitive reaction due to the color environment has been applied to various filed especially in medical field. Moreover, the study about the identification of patient's condition and examination the brain activity by collecting the bio-signal based on the color environment is being actively conducted. Even though, there were a variety of experiments by convention the color environment using a light or LED color, it still has a problem that affects the psychological information. Therefore, our proposed system using a HMD (Head Mounting display) to provide a completed color environment condition. This system uses the BMS(Biomedical System) to collect the biometric information which responds to the specific color condition and the human body response information can be measured by the development the Memory and Attention test on Mobile phone. The collection of Biometric information includes electro cardiogram(ECG), respiration, oxygen saturation (Sp02), Bio-impedance, blood pressure will store in the database. In addition, we can verify the result of the human body reaction in the color environment by Memory and Attention application. By utilizing the reaction of the human body information that is collected thought the proposed system, we can analyze the correlation between the physiological information and the color environment. And we also expect that this system can apply to the medical diagnosis and treatment. For future work, we will expand the system for prediction and treatment of Alzheimer disease by analyzing the visualization data through the proposed system. We will also do evaluation on the effectiveness of the system for using in the rehabilitation program.

• The Korean Journal of Nuclear Medicine Technology
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• v.26 no.1
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• pp.27-32
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• 2022

Purpose When performing a whole-body bone scan, many patients are experiencing psychological difficulties due to the close distance to the detector. Recently, in the medical field, there is a report that using virtual reality (VR) equipment can give pain relief to pediatric patients with weak concentration or patients receiving severe treatment through a distraction method. Therefore, in this paper, VR equipment was used to provide psychological stability to patients during nuclear medicine tests, and it is intended to evaluate whether it can be used in clinical practice. Materials and Methods As VR equipment, ALLIP Z6 VR (ALLIP, Korea) was used and the experiment was conducted after connecting to a mobile phone. The subjects were 30 patients who underwent whole-body bone examination from September 1, 2021 to September 30, 2021. After intravenous injection of 99mTc-HDP, 3 to 6 hours later, VR equipment was put on and whole body images were obtained. After the test, a survey was conducted, and a Likert scale of 5 points was used for psychological anxiety and satisfaction with VR equipment. Hypothesis verification and reliability of the survey were analyzed using SPSS Statistics 25 (IBM, Corp., Armonk, NY, USA). Results Anxiety about the existing whole-body bone test was 3.03±1.53, whereas that of anxiety after wearing VR equipment was 2.0±1.21, indicating that anxiety decreased to 34%. When regression analysis of the effect of the patient's concentration on VR equipment on anxiety about the test, the B value was 0.750 (P<0.01) and the t value was 6.181 (P<0.01). decreased and showed an influence of 75%. In addition, overall satisfaction with VR equipment was 3.76±1.28, and the intention to reuse was 66%. The Cronbach α value of the reliability coefficient of the questionnaire was 0.901. Conclusion When using VR equipment, patients' attention was dispersed, anxiety was reduced, and psychological stability was found. In the future, as VR equipment technology develops, it is thought that if the equipment can be miniaturized and the resolution of VR content images is increased, it can be used in various clinical settings if it provides more realistic stability to the patient.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2004.11a
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• pp.53-55
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• 2004

In emergency cases, such as the severe trauma involving the fracture of skull, spine, or cervical bone, from auto accident or a fall, and/or pneumothorax which can not be diagnosed exactly by the eye examination, it is necessary the radiological examination during transferring to the hospital for emergency care. The aim of this study was to design and evaluate the prototype of mobile medical image communication system based on CDMA 1X EVDO. The system consists of a laptop computer used as a transmit DICOM client, linked with cellular phone which support to the CDMA 1X EVDO communication service, and a receiving DICOM server installed in the hospital. The DR images were stored with DICOM format in the storage of transmit client. Those images were compressed into JPEG2000 format and transmitted from transmit client to the receiving server. All of those images were progressively transmitted to the receiving server and displayed on the server monitor. To evaluate the image quality, PSNR of compressed image was measured. Also, several field tests had been performed using commercial CDMA2000 1X-EVDO reverse link with the TCP/IP data segments. The test had been taken under several velocity of vehicle in seoul areas.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.53-69
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• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.