• Proceedings of the Korean Information Science Society Conference
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• 2012.06d
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• pp.124-125
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• 2012

본 논문에서는 GPS 음영 지역에서 사용자의 위치 인식을 위해 추측 항법 기법을 이용하여 사용자의 이동 경로를 추적하는 중간 단계로써 스마트 폰의 내장된 가속도 센서와 나침반 센서를 이용하여 실험자의 걸음걸이 검출과 주머니 속의 스마트 폰의 상대 위치를 파악 방법을 제시한다. 실험 결과 가속도 센서를 이용한 걸음걸이 검출 율은 5%의 오차를 갖고 있으며, 지자기 센서를 이용한 스마트 폰의 자세는 검출 율은 100% 검출 하였으며, 향후 다양한 위치에 존재하는 스마트 폰을 스스로 인식하여 이동 방향을 찾는 연구를 제시하고자 한다.

• Journal of the Korea Society of Computer and Information
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• v.17 no.8
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• pp.107-114
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• 2012

We have developed a textile capacitive pressure sensor for smart gait analysis. The proposed system can convert sensor signal into step counts and pressure levels by different posture. To evaluate the performance of insole type textile capacitive sensor, we measured capacitance change by increment of weights from 10 kg to 100 kg with 10 kg increment using M1 class rectangular weights (four 20 kg weights and two 10 kg weights) which have ${\pm}10%$ tolerance. The result showed non-linearity characteristic of a general capacitive pressure sensor. The test was performed according to a test protocol for four different postures (sitting, standing, standing on a left leg and standing on a right leg) and different walking speeds (1 km/h and 4 km/h). Five healthy male subjects were participated in each test. As we expected, the pressure level was changed by pressure distribution according to posture. Also, developed textile pressure sensor showed higher recognition rate (average 98.06 %) than commercial pedometer at all walking speed. Therefore, the proposed step counts and posture monitoring system using conductive textile capacitive pressure sensor proved to be a reliable and useful tool for monitoring gait parameters.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.1
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• pp.162-172
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• 2015

In this paper, we propose an adaptive detection algorithm of footstep and a classification algorithm for activities of the detected footstep. The proposed algorithm can detect and classify whole movement as well as individual and irregular activities, since it does not use continuous footstep signals which are used by most previous research. For classifying movement, we use feature vectors obtained from frequency spectrum from FFT, CWT, AR model and image of AR spectrogram. With SVM classifier, we obtain classification accuracy of single footstep activities over 90% when feature vectors using AR spectrogram image are used.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.476-477
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• 2008

본 연구에서는 가속도 센서를 이용하여 보행패턴을 검출하고 가속도 센서의 출력 값을 무선으로 PC에 실시간으로 전달할 수 있는 휴대용 모듈을 개발하였다. PC에서는 휴대장치로부터 전송되는 데이터를 수집하여 운동패턴을 화면에 실시간으로 출력할 수 있게 하였다. 휴대 장치의 전력 소모를 최대한 줄이기 위해 무선 전송 부분은 zigbee 통신을 사용하였다. 착용자의 걸음걸이 패턴을 분석하기 위해 2축 가속도 센서를 사용하였으며 기본적인 보행수는 임계치를 사용하는 moving average 알고리즘을 이용하여 마이크로 콘트롤러에서 처리하였다.

• 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%.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.603-609
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• 2010

This paper introduces an objects recognition algorithm based on SURF(Speeded Up Robust Features) and GP(Genetic Programming) based gaits generation. Combining both methods, a recognition based intelligent walking for quadruped robots is proposed. The gait of quadruped robots is generated by means of symbolic regression for each joint trajectories using GP. A position and size of target object are recognized by SURF which enables high speed feature extraction, and then the distance to the object is calculated. Experiments for objects recognition and autonomous walking for quadruped robots are executed for ODE based Webots simulation and real robot.

• The Journal of the Korea Contents Association
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• v.8 no.8
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• pp.253-260
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• 2008

The research for a 3-axial accelerometer sensor has increased dramatically in the fields of cellular phone, PDA, etc. In this paper, we develop a human walking detection algorithm using 3-axial accelerometer sensor and a user interface system to show the activity expenditure in real-time. To measure a walking number more correctly in a variety of walking activities including walking, walking in place, running, slow walking, we propose a new walking number detection algorithm using adaptive threshold value. In addition, we calculate the activity expenditure base on counted walking number and display calculated activity expenditure on UI in real-time. From the experimental results, we could obtain that the detection rate of proposal algorithm is higher than that of existing algorithm using a fixed threshold value about $5{\sim}10%$. Especially, it could be found out high detection rate in walking in place.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.5
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• pp.39-46
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• 2007

Research on human identification in ubiquitous environment has recently attracted a lot of attention. As one of those research, gait recognition is an efficient method of human identification using physical features of a walking person at a distance. In this paper, we present a human shape extraction and tracking for gait recognition using geodesic active contour models(GACMs) combined with mean shift algorithm The active contour models (ACMs) are very effective to deal with the non-rigid object because of its elastic property. However, they have the limitation that their performance is mainly dependent on the initial curve. To overcome this problem, we combine the mean shift algorithm with the traditional GACMs. The main idea is very simple. Before evolving using level set method, the initial curve in each frame is re-localized near the human region and is resized enough to include the targe region. This mechanism allows for reducing the number of iterations and for handling the large object motion. The proposed system is composed of human region detection and human shape tracking modules. In the human region detection module, the silhouette of a walking person is extracted by background subtraction and morphologic operation. Then human shape are correctly obtained by the GACMs with mean shift algorithm. In experimental results, the proposed method show that it is extracted and tracked efficiently accurate shape for gait recognition.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.41-51
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• 2023

In this paper, we propose a technique of multi-time window feature extraction for anger detection in gait data. In the previous gait-based emotion recognition methods, the pedestrian's stride, time taken for one stride, walking speed, and forward tilt angles of the neck and thorax are calculated. Then, minimum, mean, and maximum values are calculated for the entire interval to use them as features. However, each feature does not always change uniformly over the entire interval but sometimes changes locally. Therefore, we propose a multi-time window feature extraction technique that can extract both global and local features, from long-term to short-term. In addition, we also propose an ensemble model that consists of multiple classifiers. Each classifier is trained with features extracted from different multi-time windows. To verify the effectiveness of the proposed feature extraction technique and ensemble model, a public three-dimensional gait dataset was used. The simulation results demonstrate that the proposed ensemble model achieves the best performance compared to machine learning models trained with existing feature extraction techniques for four performance evaluation metrics.

• 핵융합뉴스레터
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• s.46
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• pp.6-9
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• 2010

2008년 7월 최초 발생 성공 이후 지난 해 본격 가동에 들어서며 기대 이상의 운영성과를 냈던 한국의 태양 KSTAR가 3번째 실험에 들어간다. 금년 실험은 지난해보다 가열장치, 진공용기 내부 장치 등 향상된 장치 성능을 바탕으로 1천만도 이상의 초고온 플라즈마 발생 및 핵융합 반응을 통한 중성자 검출 등을 목표로 한다. 특히 오는 10월 대전에서 열리는 IAEA 국제핵융합에너지 컨퍼런스(FEC)에서 이번 KSTAR의 실험 성과를 발표할 예정으로 어느 때보다 세계 핵융합연구자들의 시선이 KSTAR로 몰리게 될 것으로 기대하고 있다.