• Journal of the Korea Safety Management & Science
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• v.11 no.3
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• pp.73-78
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• 2009

In this paper, we developed the care system for older people living alone using the RFID technologies and the living informations. The care system store living informations, extracted through a unconstrained detection method by the RFID tags and readers, into a monitering server. The unconstrained detection method improved a weakness of existing systems that detected a living informations through an infrared sensor, ultrasonic sensor, camera, consumed quantity of the tap water or gas. The result of this study will playa very important role, as a part of a composite older-welfare services. Also, in the future, accumulated living informations will be allowed for a health data of older peoples.

• The Journal of Korea Robotics Society
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• v.10 no.4
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• pp.223-229
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• 2015

This paper proposes a novel method for detection of hand raising poses from images acquired from a single camera attached to a mobile robot that navigates unknown dynamic environments. Due to unconstrained illumination, a high level of variance in human appearances and unpredictable backgrounds, detecting hand raising gestures from an image acquired from a camera attached to a mobile robot is very challenging. The proposed method first detects faces to determine the region of interest (ROI), and in this ROI, we detect hands by using a HOG-based hand detector. By using the color distribution of the face region, we evaluate each candidate in the detected hand region. To deal with cases of failure in face detection, we also use a HOG-based hand raising pose detector. Unlike other hand raising pose detector systems, we evaluate our algorithm with images acquired from the camera and images obtained from the Internet that contain unknown backgrounds and unconstrained illumination. The level of variance in hand raising poses in these images is very high. Our experiment results show that the proposed method robustly detects hand raising poses in complex backgrounds and unknown lighting conditions.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.141-156
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• 2013

In this paper we propose a method to detect human faces in color images. Many existing systems use a window-based classifier that scans the entire image for the presence of the human face and such systems suffers from scale variation, pose variation, illumination changes, etc. Here, we propose a lighting insensitive face detection method based upon the edge and skin tone information of the input color image. First, image enhancement is performed, especially if the image is acquired from an unconstrained illumination condition. Next, skin segmentation in YCbCr and RGB space is conducted. The result of skin segmentation is refined using the skin tone percentage index method. The edges of the input image are combined with the skin tone image to separate all non-face regions from candidate faces. Candidate verification using primitive shape features of the face is applied to decide which of the candidate regions corresponds to a face. The advantage of the proposed method is that it can detect faces that are of different sizes, in different poses, and that are making different expressions under unconstrained illumination conditions.

• The Journal of the Acoustical Society of Korea
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• v.26 no.1E
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• pp.21-26
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• 2007

In this paper, we propose a modified Viterbi algorithm to compensate for endpoint detection error during the decoding phase of an isolated word recognition task. Since the conventional Viterbi algorithm explores only the search space whose boundaries are fixed to the endpoints of the segmented utterance by the endpoint detector, the recognition performance is highly dependent on the accuracy level of endpoint detection. Inaccurately segmented word boundaries lead directly to recognition error. In order to relax the degradation of recognition accuracy due to endpoint detection error, we describe an unconstrained search of word boundaries and present an algorithm to explore the search space with efficiency. The proposed algorithm was evaluated by performing a variety of simulated endpoint detection error cases on an isolated word recognition task. The proposed algorithm reduced the Word Error Rate (WER) considerably, from 84.4% to 10.6%, while consuming only a little more computation power.

• Journal of Biomedical Engineering Research
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• v.35 no.3
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• pp.55-61
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• 2014

In sleep monitoring system, polysomnography (PSG) is the gold-standard but previous studies revealed that attaching numerous amount of sensors disturb sleep during the test which is the fundamental disadvantage of PSG. We suggest an unconstrained rapid-eye-movement (REM) sleep monitoring method measured with polyvinylidene (PVDF) film-based sensor for the normal and the obstructive sleep apnea (OSA) patients. Nine normal subjects and seventeen OSA patients have participated in the study. During REM sleep, rate and variability of respiration are known to be greater than in other sleep stages. Based on this phenomena, respiratory signals of participants were unconstrainedly measured using the PVDF-based sensor with the PSG and REM sleep were extracted from the average rate and variability of respiration. In epoch-by-epoch REM sleep detection, proposed method classified REM sleep with an average sensitivity of 72.3%, specificity of 92.5%, accuracy of 88.9%, and kappa statistic of 0.60 compared to the results of PSG. Student's t-test showed no significant difference between the results of normal and OSA group. This method is potentially applicable to REM sleep detection in homing environment or ambulatory monitoring.

• ETRI Journal
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• v.39 no.5
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• pp.621-631
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• 2017

Abnormal samples are usually difficult to obtain in production systems, resulting in imbalanced training sample sets. Namely, the number of positive samples is far less than the number of negative samples. Traditional Support Vector Machine (SVM)-based anomaly detection algorithms perform poorly for highly imbalanced datasets: the learned classification hyperplane skews toward the positive samples, resulting in a high false-negative rate. This article proposes a new imbalanced SVM (termed ImSVM)-based anomaly detection algorithm, which assigns a different weight for each positive support vector in the decision function. ImSVM adjusts the learned classification hyperplane to make the decision function achieve a maximum GMean measure value on the dataset. The above problem is converted into an unconstrained optimization problem to search the optimal weight vector. Experiments are carried out on both Cloud datasets and Knowledge Discovery and Data Mining datasets to evaluate ImSVM. Highly imbalanced training sample sets are constructed. The experimental results show that ImSVM outperforms over-sampling techniques and several existing imbalanced SVM-based techniques.

• Journal of Korea Multimedia Society
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• v.15 no.5
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• pp.577-586
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• 2012

The smartphone emerged due to the rapid development of the Internet has brought greater convenience to life in a positive manner. Recently, however, because of unconstrained exposure to harmful video, reckless use of smart phones has become a domestic issue in our society. In this paper, a service which detects harmful videos by using the acoustic information is designed and implemented on the Android OS. In order to implement the service of Android OS-based detection of the harmful movie, the speed of existing sound-based detection method for harmful videos is improved. The GMM(Gaussian Mixture Model) was used for classifier and the number of Gaussian Mixture was 18. The implemented service shows a detection rate of 97.02% for a total of 1,210 data files (approximately 687 hours) which comprises 669 general videos files (about 424 hours) and 541 harmful video files (about 263 hours). It's speed is 5.6 times faster than the traditional methods whitout reducing the detection rate.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.2
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• pp.118-123
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• 2011

Video based object tracking normally deals with non-stationary image streams that change over time. Robust and real time moving object tracking is considered to be a problematic issue in computer vision. Multiple object tracking has many practical applications in scene analysis for automated surveillance. In this paper, we introduce a specified object tracking based particle filter used in an environment of multiple moving objects. A differential image region based tracking method for the detection of multiple moving objects is used. In order to ensure accurate object detection in an unconstrained environment, a background image update method is used. In addition, there exist problems in tracking a particular object through a video sequence, which cannot rely only on image processing techniques. For this, a probabilistic framework is used. Our proposed particle filter has been proved to be robust in dealing with nonlinear and non-Gaussian problems. The particle filter provides a robust object tracking framework under ambiguity conditions and greatly improves the estimation accuracy for complicated tracking problems.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.5
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• pp.33-42
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• 2010

In this study, we have proposed a simple cardiorespiratory monitoring method based on displacements of human body which occurs due to periodic heartbeat and breathing. The proposed system consists of an aircushion, pressure sensing hardware and heartbeat and respiration signals extraction algorithm. The aircushion was used for unconstrained measurement of the respiration and heartbeats without a sensor attached on the subject's skin surface. The displacements of subject sitting on the aircushion cause small pressure variations. These variations are amplified and filtered with the pressure sensing hardware. Finally, heart rate and respiration rate are extracted by signal processing algorithm based on frequency domain filter. To evaluate the performance, extracted respiration and heart rate from proposed system were compared with conventional methods. The average sensitivity of respiration and heart rate are 98.67% and 99.24%, respectively. These results show the proposed method has advantages of installing and processing simplicity so as to be used easily in unconstrained respiration and heart rate monitoring in daily life.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.482-483
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• 2008

본 연구는 수면 중 무구속적 방식으로 에어 베개에 부착한 PPG 센서에서 호흡 및 심박을 검출하는 방법을 제안하였다. 본 연구에서 사용된 반사형 PPG 센서는 광원이 피부로 투과되어 혈관의 이완 및 수축 정도를 측정할 수 있다. 반사형 PPG 센서로부터 하드웨어 모듈을 통과한 생체 신호는 AD 변환되어 PC로 전송된다 PPG에서 검출된 분당 심박 수는 전송된 신호의 밸리점간의 시간 간격을 이용하여 추출하며 호흡 신호는 밸리점의 크기를 연결하여 추출하였다. 검출된 호흡 신호와 기준 호흡 신호간의 상관성을 확인하기 위해 기준신호로 호흡과 심박을 동시에 측정하여 그 결과를 분석하였다. PPG 센서로부터 획득한 심박 및 호흡 신호는 기준신호들과 높은 상관성을 가지며 호흡시 발생하는 움직임과 호흡 속도에 영향을 받는다는 것을 알 수 있었다.