• Title, Summary, Keyword: Gait recognition

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A Study on Feature selection based the Fuzzy Min-Max Neural Network and Application on Gait Phase recognition using EMG (퍼지 최대-최소 신경망을 이용한 특징 집합 선택에 관한 연구 및 보행 단계인식에의 응용)

  • Lee, Tae-Yeop;Lee, Sang-Wan;Byeon, Jeung-Nam
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.167-171
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    • 2007
  • 본 논문은 패턴 분류 문제에 사용되는 퍼지 최대-최소 신경망 방법을 이용하여 특정 집합으로부터 새로운 특정 집합을 추출해내고 추출된 특정 집합으로부터 의미 있는 특정을 선택해 내는 새로운 방법을 제안한다. 퍼지 최대-최소 신경망은 패턴 분류를 위해 주로 사용이 되어 왔지만, 퍼지 최대-최소 신경망을 이용해 특정 집합의 값들을 패턴 공간내의 초상자의 집합으로 변환하고 변환된 초상자들끼리의 인접성을 척도로 단순한 연산을 통한 빠른 특정 집합을 선택하게 된다. 마지막으로 본 논문의 특정 집합 선택 방법을 하지 근전도 신호를 이용한 보행 패턴 분류에 적용해 보고, 그 결과를 기존 여러 특정 집합 선태 방법들과 비교해 봄으로써 제안한 방법의 타당성 및 적용 가능성을 알아본다.

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An Obstacle Avoidance Trajectory Planning for a Quadruped Walking Robot Using Vision and PSD sensor

  • Kong, Jung-Shik;Lee, Bo-Hee;Kim, Jin-Geol
    • 제어로봇시스템학회:학술대회논문집
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    • pp.105.1-105
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    • 2002
  • $\textbullet$ This paper deals with obstacle avoidance of a quadruped robot with a vision system and a PSD sensor. $\textbullet$ The vision system needs for obstacle recognition toward robot. $\textbullet$ Ths PSD sensor is also important element for obstacle recognition. $\textbullet$ We propose algorithm that recognizes obstacles with one vision and PSD sensor. $\textbullet$ We also propose obstacle avoidance algorithm with map from obstacle recognition algorithm. $\textbullet$ Using these algorithm, Quadruped robot can generate gait trajectory. $\textbullet$ Therefore, robot can avoid obstacls, and can move to target point.

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Gait-based Human Identification System using Eigenfeature Regularization and Extraction (고유특징 정규화 및 추출 기법을 이용한 걸음걸이 바이오 정보 기반 사용자 인식 시스템)

  • Lee, Byung-Yun;Hong, Sung-Jun;Lee, Hee-Sung;Kim, Eun-Tai
    • Journal of Korean Institute of Intelligent Systems
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    • v.21 no.1
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    • pp.6-11
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    • 2011
  • In this paper, we propose a gait-based human identification system using eigenfeature regularization and extraction (ERE). First, a gait feature for human identification which is called gait energy image (GEI) is generated from walking sequences acquired from a camera sensor. In training phase, regularized transformation matrix is obtained by applying ERE to the gallery GEI dataset, and the gallery GEI dataset is projected onto the eigenspace to obtain galley features. In testing phase, the probe GEI dataset is projected onto the eigenspace created in training phase and determine the identity by using a nearest neighbor classifier. Experiments are carried out on the CASIA gait dataset A to evaluate the performance of the proposed system. Experimental results show that the proposed system is better than previous works in terms of correct classification rate.

A Study on User Authentication with Smartphone Accelerometer Sensor (스마트폰 가속도 센서를 이용한 사용자 인증 방법 연구)

  • Seo, Jun-seok;Moon, Jong-sub
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.25 no.6
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    • pp.1477-1484
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    • 2015
  • With the growth of financial industry with smartphone, interest on user authentication using smartphone has been arisen in these days. There are various type of biometric user authentication techniques, but gait recognition using accelerometer sensor in smartphone does not seem to develop remarkably. This paper suggests the method of user authentication using accelerometer sensor embedded in smartphone. Specifically, calibrate the sensor data from smartphone with 3D-transformation, extract features from transformed data and do principle component analysis, and learn model with using gaussian mixture model. Next, authenticate user data with confidence interval of GMM model. As result, proposed method is capable of user authentication with accelerometer sensor on smartphone as a high degree of accuracy(about 96%) even in the situation that environment control and limitation are minimum on the research.

ACMs-based Human Shape Extraction and Tracking System for Human Identification (개인 인증을 위한 활성 윤곽선 모델 기반의 사람 외형 추출 및 추적 시스템)

  • Park, Se-Hyun;Kwon, Kyung-Su;Kim, Eun-Yi;Kim, Hang-Joon
    • Journal of the Korea Industrial Information Systems Research
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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.

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Perception and use of gait measures among physical therapists in South Korea

  • Jang, Ho Young;Kim, You Lim;Kim, Sung-jin;Yoon, Tak Yong;Kim, Kyung Hun;Ahn, Ick Keun;Lee, Suk Min
    • Physical Therapy Rehabilitation Science
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    • v.6 no.2
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    • pp.90-95
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    • 2017
  • Objective: The purpose of this study was to investigate the physical therapists' perception of the use of gait measures, the frequency of the gait measures used, and also to identify the barriers that limit the use of these assessment tools. Design: Cross-sectional study. Methods: Physical therapists from the Seoul, Gyeonggi area from March to July 2016 were included in the study. Over the course of 18 weeks, a cross-sectional study was conducted with a self-report questionnaire. A total of 700 questionnaires were distributed and 350 questionnaires (50%) were collected, however with the exclusion of 140 questionnaires due to non-consent, a total of 210 questionnaires (30%) were analysed. Results: Out of the 10 standardized assessment tools, the therapists showed the highest perception for the timed up and go test (TUG [n=153, 72.9%]) and they also had high perception for the 10 meters walk test (10MWT [n=149, 71.0%]), and 6-minute walk test (6MWT [n=123, 58.6%]). The respondents answered that the TUG (n=116, 55.2%), 10MWT (n=100, 47.6%), and 6MWT (n=51, 24.3%) was used the most often. On the contrary, only four (1.9%) therapists have used the Chedoke-McMaster stroke assessment and the Rivermead Mobility Index. The lack of time was considered as the most important barrier to the use of assessment tools in clinical practice. Conclusions: Through this study, it has been shown that the domestic physical therapists used the TUG and the 10MWT mainly due to high recognition and evaluation status; however, the lack of time was the greatest impediment to the clinical application of the gait assessment tools.

Using Keystroke Dynamics for Implicit Authentication on Smartphone

  • Do, Son;Hoang, Thang;Luong, Chuyen;Choi, Seungchan;Lee, Dokyeong;Bang, Kihyun;Choi, Deokjai
    • Journal of Korea Multimedia Society
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    • v.17 no.8
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    • pp.968-976
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    • 2014
  • Authentication methods on smartphone are demanded to be implicit to users with minimum users' interaction. Existing authentication methods (e.g. PINs, passwords, visual patterns, etc.) are not effectively considering remembrance and privacy issues. Behavioral biometrics such as keystroke dynamics and gait biometrics can be acquired easily and implicitly by using integrated sensors on smartphone. We propose a biometric model involving keystroke dynamics for implicit authentication on smartphone. We first design a feature extraction method for keystroke dynamics. And then, we build a fusion model of keystroke dynamics and gait to improve the authentication performance of single behavioral biometric on smartphone. We operate the fusion at both feature extraction level and matching score level. Experiment using linear Support Vector Machines (SVM) classifier reveals that the best results are achieved with score fusion: a recognition rate approximately 97.86% under identification mode and an error rate approximately 1.11% under authentication mode.

The Effect of Home stayed Stroke Patients' gait, Valance, Activities of Daily Living, Depression in the Aerobic Walking Exercise Program. (유산소 걷기운동 프로그램이 재가 뇌졸중 환자의 보행, 균형, 일상활동 수행능력, 우울에 미치는 효과)

  • Roh, Kook-Hee
    • The Korean Journal of Rehabilitation Nursing
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    • v.5 no.2
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    • pp.193-204
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    • 2002
  • This study was a quasi-experimental study of nonequivalent control group pretest- posttest design to investigate the effect of aerobic walking exercise program on the physical & psychological functions of home stayed stroke patients. The data were collected during the period of May 20th to August 15th, 2001. The subjects for this study were 40 hemiplegic stroke patients with the experimental group consisting of 19 patients and the control group being composed of 21 patients. The patients selected for this study were: (a)living in J city who had been diagnosed with stroke and at home after being discharged from the hospital, (b)suffering from stroke for 6 months to 5 years, (c)without recognition disorder with the MMSE-K score above 25, (d)below 2 on the modified Ashworth scale, (e)free from heart and pulmonary disease (f)able to walk beyond 15 minutes for themselves. The aerobic walking exercise program for the experimental group was aerobic exercise and education and supportive care. The aerobic exercise was 8 weeks' period, three times a week, 35 to 50 minutes a day. And the education and supportive care was consisted of one home visiting and 2 times telephoning a week. The data were analysed by $X^2$-test, paired t-test and unpaired t-test and ANCOVA through SAS/PC program. The results of the study were as follows: 1. There was insignificant difference in the gait length experimental and control group. There was significant difference in the gait speed between the two groups. 2. There was significant difference in the dynamic valance between the two groups. 3. There was significant difference in ADL score between the two groups. 4. There was no significant difference in the depression between the two groups. As shown above, the results of 8 weeks' the aerobic walking exercise program for home stayed stroke patients produced positive effects on gait speed, dynamic valance, ADL score. And this program was expected that it was more effective in different intervention period, verified program. Also it was needed follow study.

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Gait Type Classification Based on Kinematic Factors of Gait for Exoskeleton Robot Recognition (외골격 로봇의 동작인식을 위한 보행의 운동학적 요인을 이용한 보행유형 분류)

  • Cho, Jaehoon;Bong, wonwoo;Kim, donghun;Choi, Hyeonki
    • Journal of Biomedical Engineering Research
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    • v.38 no.3
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    • pp.129-136
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    • 2017
  • The exoskeleton robot is a technology developed to be used in various fields such as military, industry and medical treatment. The exoskeleton robot works by sensing the movement of the wearer. By recognizing the wearer's daily activities, the exoskeleton robot can assist the wearer quickly and efficiently utilize the system. In this study, LDA, QDA, and kNN are used to classify gait types through kinetic data obtained from subjects. Walking was selected from general walking and stair walking which are mainly performed in daily life. Seven IMUs sensors were attached to the subject at the predetermined positions to measure kinematic factors. As a result, LDA was classified as 78.42%, QDA as 86.16%, and kNN as 87.10% ~ 94.49% according to the value of k.

Implementation of a Falls Recognition System Using Acceleration and Angular Velocity Signals (가속도 및 각속도 신호를 이용한 낙상 인지 시스템 구현)

  • Park, Geun-Chul;Jeon, A-Young;Lee, Sang-Hoon;Son, Jung-Man;Kim, Myoung-Chul;Jeon, Gye-Rok
    • Journal of Sensor Science and Technology
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    • v.22 no.1
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    • pp.54-64
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    • 2013
  • In this study, we developed a falling recognition system to transmit SMS data through CDMA communication using a three axises acceleration sensor and a two axises gyro sensor. 5 healthy men were selected into a control group, and the fall recognition system using the three axises acceleration sensor and the two axises gyro sensor was devised to conduct an experiment. The system was attached to the upper of their sternum. According to the experiment protocol, the experiment was carried out 3 times repeatedly divided into 3 specific protocols: falling during gait, falling in stopped state, and falling in everyday life. Data obtained in the falling recognition system and LabVIEW 8.5 were used to decide if falling corresponds to that regulated in an analysis program applying an algorithm proposed in this study. In addition, results from falling recognition were transmitted to designated cellular phone in a SMS (Shot Message Service) form. These research results show that an erroneous detection rate of falling reached 19% in applying an acceleration signal only; 6% in applying an angular velocity; and 2% in applying a proposed algorithm. Such finding suggests that an erroneous detection rate of falling is improved when the proposed algorithm is applied incorporated with acceleration and angular velocity. In this study therefore, we proposed that a falling recognition system implemented in this study can make a contribution to the recognition of falling of the aged or the disabled.