• Journal of Mechanical Science and Technology
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• v.17 no.6
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• pp.825-835
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• 2003

Previous studies have demonstrated the importance of joint angle errors mainly due to skin artifact and measurement errors during gait analysis. Joint angle errors lead to unreliable kinematics and kinetic analyses in the investigation of human motion. The purpose of this paper is to present the Joint Averaging Coordinate System (JACS) method for human gait analysis. The JACS method is based on the concept of statistical data reduction of anatomically referenced marker data. Since markers are not attached to rigid bodies, different marker combinations lead to slightly different predictions of joint angles. These different combinations can be averaged in order to provide a "best" estimate of joint angle. Results of a gait analysis are presented using clinically meaningful terminology to provide better communication with clinical personal. In order to verify the developed JACS method, a simple three-dimensional knee joint contact model was developed, employing an absolute coordinate system without using any kinematics constraint in which thigh and shank segments can be derived independently. In the experimental data recovery, the separation and penetration distance of the knee joint is supposed to be zero during one gait cycle if there are no errors in the experimental data. Using the JACS method, the separation and penetration error was reduced compared to well-developed existing methods such as ACRS and Spoor & Veldpaus method. The separation and penetration distance ranged up to 15 mm and 12 mm using the Spoor & Veldpaus and ACRS method, respectively, compared to 9 mm using JACS method. Statistical methods like the JACS can be applied in conjunction with existing techniques that reduce systematic errors in marker location, leading to an improved assessment of human gait.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.1
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• pp.20-28
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• 2012

Gait analysis is an examination which extracts objective information from observing human gait and assesses the function. The equipments used recently for gait analysis are expensive due to multiple cameras and force plates, and require the large space to set up the system. In this paper, we proposed a method to measure human gait motions in 3D from a monocular video. Our approach was based on particle filtering to track human motion without training data and previous information about a gait. We used dynamics to make physics-based motions with the consideration of contacts between feet and base. In a walking sequence, our approach showed the mean angular error of $12.4^{\circ}$ over all joints, which was much smaller than the error of $34.6^{\circ}$ with the conventional particle filter. These results showed that a monocular camera is able to replace the existing complicated system for measuring human gait quantitatively.

• Journal of KIISE:Software and Applications
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• v.33 no.5
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• pp.499-507
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• 2006

Recently human gait has been considered as a useful biometric supporting high performance human identification systems. This paper proposes a view-based pedestrian identification method using the dynamic silhouettes of a human body modeled with the Hidden Markov Model(HMM). Two types of gait models have been developed both with an endless cycle architecture: one is a discrete HMM method using a self-organizing map-based VQ codebook and the other is a continuous HMM method using feature vectors transformed into a PCA space. Experimental results showed a consistent performance trend over a range of model parameters and the recognition rate up to 88.1%. Compared with other methods, the proposed models and techniques are believed to have a sufficient potential for a successful application to gait recognition.

• Journal of information and communication convergence engineering
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• v.15 no.4
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• pp.244-249
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• 2017

Wearable sensor-based gait analysis has been widely conducted to analyze various aspects of human ambulation abilities under the free-living condition. However, there have been few research efforts on using wearable sensors to analyze human walking on an unstable surface such as on a ship during a sea voyage. Since the motion of a ship on the unstable sea surface imposes significant differences in walking strategies, investigation is suggested to find better performing wearable sensor-based gait analysis algorithms on this unstable environment. This study aimed to compare two representative gait event algorithms including time domain and frequency domain analyses for detecting heel strike on an unstable platform. As results, although two methods did not miss any heel strike, the frequency domain analysis method perform better when comparing heel strike timing. The finding suggests that the frequency analysis is recommended to efficiently detect gait event in the unstable walking environment.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.6
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• pp.687-693
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• 2011

Spring-mass models have been widely accepted to explain the basic dynamics of human gait. Researchers found that the leg stiffness increased with gait speed to increase energy efficiency. However, the difference of leg stiffness change with gait speed between the young and the elderly has not been verified yet. In this study, we calculated the lower limb stiffness of the elderly using walking model with an axial spring. Vertical stiffness was defined as the ratio of the vertical force change to the vertical displacement change. Seven young and eight elderly subjects participated to the test. The subjects walked on a 12 meter long, 1 meter wide walkway at four different gait speeds, ranging from their self-selected speed to maximum speed randomly. Kinetic and kinematic data were collected using three force plates and motion capture cameras, respectively. The vertical stiffness of the two groups increased as a function of walking speed. Maximum walking speed of the elderly was slower than that of the young, yet the walking speed correlated well with the optimal stiffness that maximizes propulsion energy in both groups. The results may imply that human may use apparent limb stiffness to optimize energy based on spring-like leg mechanics.

• Physical Therapy Rehabilitation Science
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• v.11 no.2
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• pp.215-223
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• 2022

Objective: This study was to resolve the limitations of the experimental environment and to solve the shortcomings of the method of measuring human gait characteristics using optical measuring instruments. Design: A cross-sectional study. Methods: Fifteen healthy adults without a history of orthopedic surgery on the lower extremities for the past 6 months were participated. They were analyzed gait variables using the smart guide and the 3D image analysis at the same time, and their results were compared. Visual-3D was used to calculate the analysis variables. Results: The reliability and validity of the data according to the two measuring instruments were found to be very high; gait speed(0.85), cycle time(0.99), stride time of both feet(0.98, 0.97) stride legnth of both feet(0.86, 0.88) stride per minute of both feet(0.99, 0.96), foot speed of both feet(0.90, 0.91), step time of both feet(0.77, 0.71), step per minute(0.72, 0.74), stance time of both feet(0.96, 0.97), swing time of both feet(0.93, 0.79), double step time(0.81), initial double step time(0.84) and terminal step time(0.76). Conclusions: In the case of the smart insole, which measures human gait variables using the pressure sensor and inertial sensor inserted in the insole, the reliability and validity of the measured data were found to be very high. It can be used as a device to replace 3D image analysis when measuring pathological gait.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.261-267
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• 2000

A mathematical model was developed to calculate the muscle force of lower extremity during the gait. We constructed a model of human locomotion, which includes a muscle-skeletal system with 7 segments and 16 lower limb muscles. Using a optimization technique, muscle forces variation of the lower extremity during the gait were generated and its result was verified by comparing a experimental results of EMG analysis. Moreover. the walking movement of the model could be compared quantitatively with those of experimental studies in human by inverse dynamics.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.1163-1164
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• 2012

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.87-94
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• 2016

In this paper, we discuss a gait representation based on the width of silhouette in terms of discriminative power and robustness against the noise in silhouette image for gait recognition. Its sensitivity to the noise in silhouette image are rigorously analyzed using probabilistic noisy silhouette model. In addition, we develop a gait recognition system using width representation and identify subjects using the decision level fusion based on majority voting. Experiments on CASIA gait dataset A and the SOTON gait database demonstrate the recognition performance with respect to the noise level added to the silhouette image.

• Journal of Korean Physical Therapy Science
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• v.10 no.2
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• pp.53-61
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• 2003

The purposes of this study were to describe and compare pint moments according to 6 types of gait methods during free speed. 15 volunteers(7 male, 8 female: mean age = 23.33 yrs.) participated and performed 6 types of gait methods. From the 3 types of pint moments of lower extremities(hip, knee, ankle and foot), the following results were made: 1. In left hip pint, the flexion-extension moment was not significantly different, but the adduction-abduction moment and rotation moment were showed different curves during stance phase. 2. In left knee pint, the flexion-extension moment was not significantly different, but the varus-valgus moment and rotation moment were showed different curves during stance phase. 3. In left ankle and foot the dorsiflexion-plantarflexion moment was not significantly different but the varus-valgus moment and rotation moment were showed different curves during stance phase. In conclusion, because weight loading gait with 10-20% of body weight were normal gait patterns, It was inferred that all weight loading gaits did not indicate noxious reactions of human body.