• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.266-270
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• 2006

In this paper, we propose the human identification system based on Hidden Markov model using gait. Since each gait cycle consists of a set of continuous motion states and transition across states has probabilistic dependences, individual gait can be modeled using Hidden Markov model. We assume that individual gait consists of N discrete transitions and we propose gait feature representation, Modified Motion Silhouette Image (MMSI) to represent and recognize individual gait. MMSI is defined as a gray-level image and it provides not only spatial information but also temporal information. The experimental results show gait recognition performance of proposed system.

• Korean Journal of Applied Biomechanics
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• v.26 no.3
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• pp.309-314
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• 2016

Objective: There are no guidelines for choosing appropriate gait assistive devices. The aim of this study was to evaluate gait assistive devices in patients with Parkinson's disease. Method: We evaluated 15 individuals with Parkinson's disease who did or did not use one of two different devices including canes and two-wheeled walkers. Data were collected using the GAITRite system. Results: Participants in the group using canes and two-wheeled walkers had significantly increased double support time and decreased gait velocity, normalized gait velocity, and stride length compared with those who did not. Participants who used a two-wheeled walker had significantly decreased gait velocity, normalized gait velocity, and stride length compared with those who used a cane. Furthermore, participants who used a two-wheeled walker had significantly decreased coefficients of variation for step time, stride length, and swing time compared with those who used a cane. Conclusion: Our results indicated that the two-wheeled walker offered the most consistent advantages for decreasing the risk of falling.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.551-554
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• 2001

A necessity of remote control robots or various searching robots etc. that accomplish works given instead of human under long distance and extreme environment such as volcano, universe, deep-sea exploration and nuclear power plant etc. is increasing, and so the development and the research regarding these mobile robots are actively progressing. The wheel mobile robot or the track mobile robot have a sufficient energy efficiency under this en, but also have a lot of limits to accomplish works given which are caused from the restriction of mobile ability. Therefore, recently many researches for the walking robot with superior mobility and energy efficiency on the terrain, which is uneven or where obstacles, inclination and stairways exist, have been doing. The research for these walking robots is separated into fields of mechanism and control system, gait research, circumference environment and system condition recognition etc. greatly. It is a research field that the gait research among these is the centralist in actual implementation of walking robot unlike different mobile robots. A research field for gait of walking robot is classified into two parts according to the nature of the stability and the walking speed, static gait or dynamic gait. While the speed of a static gait is lower than that of a dynamic gait, a static gait which moves the robot to maintain a static stability guarantees a superior stability relatively. A dynamic gait, which make the robot walk controlling the instability caused by the gravity during the two leg supporting period and so maintaining the stability of the robot body spontaneously, is suitable for high speed walking but has a relatively low stability and a difficulty in implementation compared with a static gait. The quadruped walking robot has a strong point that can embody these gaits together. In this research, we will develope an autonomous quadruped robot with an asaptibility to the environment by selectry appropriate gait, element such as duty factor, stride, trajectory, etc.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.7
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• pp.479-490
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• 2014

A lower extremity exoskeleton is a robot device that attaches to the lower limbs of the human body to augment or assist with the walking ability of the wearer. In order to improve the wearer's walking ability, the robot senses the wearer's walking locomotion and classifies it into a gait-phase state, after which it drives the appropriate robot motions for each state using its actuators. This paper presents a method by which the robot senses the wearer's locomotion along with a novel classification algorithm which classifies the sensed data as a gait-phase state. The robot determines its control mode using this gait-phase information. If erroneous information is delivered, the robot will fail to improve the walking ability or will bring some discomfort to the wearer. Therefore, it is necessary for the algorithm constantly to classify the correct gait-phase information. However, our device for sensing a human's locomotion has very sensitive characteristics sufficient for it to detect small movements. With only simple logic like a threshold-based classification, it is difficult to deliver the correct information continually. In order to overcome this and provide correct information in a timely manner, a probabilistic gait-phase classification algorithm is proposed. Experimental results demonstrate that the proposed algorithm offers excellent accuracy.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.152-158
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• 2005

Commercial lower limb prostheses or orthotics help patients achieve a normal life. However, patients who use such aids need prolonged training to achieve a normal gait, and their fatigability increases. To improve patient comfort, this study proposed a method of predicting gait angle using neural networks and EMG signals. Experimental results using our method show that the absolute average error of the estimated gait angles is $0.25^{\circ}$. This performance data used reference input from a controller for the lower limb orthotic or prosthesis controllers while the patients were walking.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.2
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• pp.130-135
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• 2016

Gait recognition has advantage of non-contact type recognition. But It has disadvantage of low recognition rate when the pedestrian silhouette is changed due to bag or coat. In this paper, we proposed new method using combination of gait energy image feature and thermal face image feature. First, we extracted a face image which has optimal focusing value using human body rate and Tenengrad algorithm. Second step, we extracted features from gait energy image and thermal face image using linear discriminant analysis. Third, calculate euclidean distance between train data and test data, and optimize weights using genetic algorithm. Finally, we compute classification using nearest neighbor classification algorithm. So the proposed method shows a better result than the conventional method.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.549-551
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• 1998

This paper deals with the new gait implementation of biped walking robot(IWR-III). In the case of using old gait. The trunk should be stopped during the phase changing time. But using new gait, the trunk moves continuously for all walking time. As a result, IWR-III has a walking gait similar to human being, and the motion of balancing joints can be reduced by the trunk ahead effect in the double support phase, moreover, ZMP tracking is improved, therefore the stability of IWR-III is improved. The trajectory is planned with a 5th order spline interpolation and stability of IWR-III is certified with a biped simulator.

• Structural Engineering and Mechanics
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• v.54 no.5
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• pp.937-953
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• 2015

This paper investigates the load model for single footfall trace of human walking. A large amount of single person walking load tests were conducted using the three-dimensional gait analysis system. Based on the experimental data, Fourier series functions were adopted to model single footfall trace in three directions, i.e. along walking direction, direction perpendicular to the walking path and vertical direction. Function parameters such as trace duration time, number of Fourier series orders, dynamic load factors (DLFs) and phase angles were determined from the experimental records. Stochastic models were then suggested by treating walking rates, duration time and DLFs as independent random variables, whose probability density functions were obtained from experimental data. Simulation procedures using the stochastic models are presented with examples. The simulated single footfall traces are similar to the experimental records.

• Proceedings of the ESK Conference
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• 1994.04a
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• pp.136-144
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• 1994

In this study, the human gait trajectories during normal walking were synthesized using the inverse kinematics and optimization techniques. The synthesis based on a lower extremity model consisting of a torso and two legs. Each leg has three segments: thigh, shank, foot, and is assumed to move with six degrees-of-freedom. In order to synthesize trajectiories of this redundant system, the sum of angular displacements of articulating joints was selected as an objective function to be minimized. The proposed algorithm in this study is very useful for the analysis of human gait. For the gait analysis, the trajectories of four points in each leg should be measured. However, by using the algorithm, measuring the trajectories of two points is sufficient, and thus the experimental set-up can be simplified. The predicted joint trajectories showed a good agreement with those obtained from the experiment. The statistical analysis and graphic simula- tions are also presented.

• 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.