• Journal of Korea Multimedia Society
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• v.11 no.6
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• pp.787-795
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• 2008

Human recognition on camera is an interesting topic in computer vision. While fingerprint and face recognition have been become common, gait is considered as a new biometric feature for distance recognition. In this paper, we propose a gait recognition algorithm based on the knee angle, 2 feet distance, walking velocity and head direction of a person who appear in camera view on one gait cycle. The background subtraction method firstly use for binary moving object extraction and then base on it we continue detect the leg region, head region and get gait features (leg angle, leg swing amplitude). Another feature, walking speed, also can be detected after a gait cycle finished. And then, we compute the errors between calculated features and stored features for recognition. This method gives good results when we performed testing using indoor and outdoor landscape in both lateral, oblique view.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.84-92
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• 2007

The gait recognition is presented for human identification from a sequence of noisy silhouettes segmented from video by capturing at a distance. The proposed gait recognition algorithm gives better performance than the baseline algorithm because of segmentation of the object by using multiple modules; i) motion detection, ii) object region detection, iii) head detection, and iv) active shape models, which solve the baseline algorithm#s problems to make background, to remove shadow, and to be better recognition rates. For the experiment, we used the HumanID Gait Challenge data set, which is the largest gait benchmarking data set with 122 objects, For realistic simulation we use various values for the following parameters; i) viewpoint, ii) shoe, iii) surface, iv) carrying condition, and v) time.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2118-2125
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• 2014

Gait Recognition is a new technique to identify the people by the way they walk. Human gait is a spatio-temporal phenomenon that typifies the motion characteristics of an individual. The proposed method makes a simple but efficient attempt to gait recognition. For each video file, spatial silhouettes of a walker are extracted by an improved background subtraction procedure using Gaussian Mixture Model (GMM). Here GMM is used as a parametric probability density function represented as a weighted sum of Gaussian component densities. Then, the relevant features are extracted from the silhouette tracked from the given video file using the Principal Component Analysis (PCA) method. The Fisher Linear Discriminant Analysis (FLDA) classifier is used in the classification of dimensional reduced image derived by the PCA method for gait recognition. Although gait images can be easily acquired, the gait recognition is affected by clothes, shoes, carrying status and specific physical condition of an individual. To overcome this problem, it is combined with footprint as a multimodal biometric system. The minutiae is extracted from the footprint and then fused with silhouette image using the Discrete Stationary Wavelet Transform (DSWT). The experimental result shows that the efficiency of proposed fusion algorithm works well and attains better result while comparing with other fusion schemes.

• The Journal of Korean Physical Therapy
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• v.8 no.1
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• pp.49-64
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• 1996

The technology of gait analysis is moving rapidly. Human gait is very complex, and a through understanding of it demands with the basic principles of biomechanics and the technology used to measure gait. Some professionals reluctance to use gait analysis may be due to the amount of time and effort necessary to accomplish this and the necessity for teamwork among the disciplines involved. Any form of observational gait analysis has limited precision and is more descriptive than quantative. The techniques of 3-D kinetic and kinematic analysis can provide a detailed biomechanical description of normal and pathological gait. This article review gait characteristics and procedures that are available for gait analysis. We are certain that, given the steady advance of technology and our continued efforts to document the benefits of that technology. gait analysis will soon be a routine part of the evaluation of both the elite athlete and the physically impaired adult or child.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.11
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• pp.1115-1123
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• 2007

The foot plays an important role in supporting the body and keeping body balance. An abnormal walking habit breaks the balance of the human body as well as the function of the foot. The foot orthotics which is designed to consider biomechanics effectively distributes the load of the human body on the sole of the foot. In this paper, gait analysis was performed for three male subjects wearing the orthotics. In this study, three male subjects were selected. The experimental apparatus consists of a plantar pressure analysis system and digital EMG system. The gait characteristics are simulated by ADAMS/LifeMOD. The COP (Center of Pressure), EMG and ground reaction force were investigated. As a result of gait analysis, the path of COP was improved and muscle activities were decreased with orthotics on the abnormal walking subjects.

• Journal of the 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.

• International Journal of Precision Engineering and Manufacturing
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• v.6 no.3
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• pp.37-44
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• 2005

Gait control capacity for most trans-femoral prostheses is significantly different from that of a normal person, and training is required for a long period of time in order for a patient to walk properly. People become easily tired when wearing a prosthesis or orthosis for a long period typically because the gait angle cannot be smoothly adjusted during wearing. Therefore, to improve the gait control problems of a trans-femoral prosthesis, the proper gait angle is estimated through surface EMG(electromyogram) signals on a normal leg, then the gait posture which the trans-femoral prosthesis should take is calculated in the neural network, which learns the gait kinetics on the basis of the normal leg's gait angle. Based on this predicted angle, a postural control method is proposed and tested adaptively following the patient's gait habit based on the predicted angle. In this study, the gait angle prediction showed accuracy of over $97\%$, and the posture control capacity of over $90\%$.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.7
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• pp.723-729
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• 2012

Spring-like leg models have been employed to explain various dynamic characteristics in human walking. However, this leg stiffness model has limitations to represent complex motion of actual human gait, especially the behaviors of each lower limb joint. The purpose of this research was to determine changes of total leg stiffness and lower limb joint stiffness with gait speed in knee osteoarthritis. Joint stiffness defined as the ratio of the joint torque change to the angular displacement change. Eight subjects with knee osteoarthritis participated to this study. The subject walked on a 12 m long and 1 m wide walkway with three sets of four different randomly ordered gait speeds, ranging from their self-selected speed to maximum speed. Kinetic and kinematic data were measured using three force plates and an optical marker system, respectively. Joint torques of lower limb joints calculated by a multi-segment inverse dynamics model. Total leg and each lower limb joint had constant stiffness during single support phase. The leg and hip joint stiffness increased with gait speed. The correlation between knee joint angles and torques had significant changed by the degree of severity of knee osteoarthritis.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.11a
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• pp.507-508
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• 2007

• Proceedings of the KOSOMBE Conference
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• v.1990 no.11
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• pp.68-70
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• 1990