• Journal of Korean Institute of Industrial Engineers
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• v.17 no.2
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• pp.39-50
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• 1991

The movement of human beings - walking, running, jumping and climbing, etc. - have long been of scientific interest. In particular, the science of human walking is called gait analysis. Various instruments have been developed to assist in the study of human gait. Recently gait analysis techniques are used in medical research to investigate the abnormalities of pathological gait. In this study, we constructed a comprehensive gait analysis system consisting of a walkway, a force platform, foot-switches and an ExpertVision motion analysis system. Time-distance gait parameters and vector diagrams can be analyzed by a special application program called Force Analysis System(FOANAS). Using quantitative discrimination of this system, the gait characteristic parameters of normal and pathological gait is facilitated.

• Journal of Korean Physical Therapy Science
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• v.27 no.3
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• pp.25-34
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• 2020

Background: Gait analysis is an important measurement for health professionals to assess gait patterns related to functional limitations due to neurological or orthopedic conditions. The purpose of this study was to investigate the reliability of the newly developed portable gait analysis system (PGAS). Design: Cross-sectional design. Test-retest study. Methods: The PGAS study was based on a wearable sensor, and measurement of gait kinematic parameters, such as gait velocity, cadence, step length and stride length, and joint angle (hip, knee, and ankle) in stance and swing phases. The results were compared with a motion capture system (MCS). Twenty healthy individuals were applied to the MCS and PGAS simultaneously during gait performance. Results: The test-retest reliability of the PGAS showed good repeatability in gait parameters with mean intra-class correlation coefficients (ICCs) ranging from 0.840 to 0.992, and joint angles in stance and swing phase from 0.907 to 0.988. The acceptable test-retest ICC was observed for the gait parameters (0.809 to 0.961), and joint angles (0.800 to 0.977). Conclusion: The results of this study indicated that the developed PGAS showed good grades of repeatability for gait kinematic data along with acceptable ICCs compared with the results from the MCS. The gait kinematic parameters in healthy subjects can be used as standard values for adopting this PGAS.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.9
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• pp.866-871
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• 2010

Gait analysis is essential for leg diagnosis and rehabilitation for the patients, the handicapped and the elderly. The use of 3D motion capture device for gait analysis is very common for gait analysis. However, this device has several shortcomings including limited workspace, visibility and high price. Instead, we developed gait estimation system using gyroscopes. This system provides gait information including the number of gaits, stride and walking distance. With four gyroscope (one for each leg's thigh and calf) outputs, the proposed gait modeling estimates the movements of the hip, the knees and the feet. Complete pedestrian localization is implemented with gait information and the heading angle estimated from the rate gyro and the magnetic compass measurements. The developed system is very useful for diagnosis and the rehabilitation of the pedestrian at the hospital. It is also useful for indoor localization of the pedestrians.

• Journal of Biomedical Engineering Research
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• v.41 no.3
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• pp.121-127
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• 2020

The purpose of this paper is to review the validation on the application of low frequency IMU(Inertial Measurement Unit) sensors by replacing high frequency motion analysis systems. Using an infrared-based 3D motion analysis system and IMU sensors (22 Hz) simultaneously, the gait cycle and knee flexion angle were measured. And the accuracy of each gait parameter was compared according to the statistical analysis method. The Bland-Altman plot analysis method was used to verify whether proper accuracy can be obtained when extracting gait parameters with low frequency sensors. As a result of the study, the use of the new gait assessment system was able to identify adequate accuracy in the measurement of cadence and stance phase. In addition, if the number of gait cycles is increased and the results of body anthropometric measurements are reflected in the gait analysis algorithm, is expected to improve accuracy in step length, walking speed, and range of motion measurements. The suggested gait assessment system is expected to make gait analysis more convenient. Furthermore, it will provide patients more accurate assessment and customized rehabilitation program through the quantitative data driven results.

• ETRI Journal
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• v.33 no.2
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• pp.259-266
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• 2011

We present a new method for an automated markerless system to describe, analyze, and classify human gait motion. The automated system consists of three stages: I) detection and extraction of the moving human body and its contour from image sequences, ii) extraction of gait figures by the joint angles and body points, and iii) analysis of motion parameters and feature extraction for classifying human gait. A sequential set of 2D stick figures is used to represent the human gait motion, and the features based on motion parameters are determined from the sequence of extracted gait figures. Then, a k-nearest neighbor classifier is used to classify the gait patterns. In experiments, this provides an alternative estimate of biomechanical parameters on a large population of subjects, suggesting that the estimate of variance by marker-based techniques appeared generous. This is a very effective and well-defined representation method for analyzing the gait motion. As such, the markerless approach confirms uniqueness of the gait as earlier studies and encourages further development along these lines.

• Journal of the Korean Society of Physical Medicine
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• v.13 no.3
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• pp.19-25
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• 2018

PURPOSE: Most gait assessment tools are expensive and require controlled laboratory environments. Tri-axial accelerometers have been used in gait analysis as an alternative to laboratory assessments. Many smartphones have added an accelerometer, making it possible to assess spatio-temporal gait parameters. This study was conducted to confirm the reliability and validity of a smartphone-based accelerometer at quantifying spatio-temporal gait parameters of stroke patients when attached to the body. METHODS: We measured gait parameters using a smartphone accelerometer and gait parameters through the GAITRite analysis system and the reliability and validity of the smartphone-based accelerometer for quantifying spatio-temporal gait parameters for stroke patients were then evaluated. Thirty stroke patients were asked to walk at self-selected comfortable speeds over a 10 m walkway, during which time gait velocity, cadence and step length were computed from smartphone-based accelerometers and validated with a GAITRite analysis system. RESULTS: Smartphone data was found to have excellent reliability ($ICC2,1{\geq}.98$) for measuring the tested parameters, with a high correlation being observed between smartphone-based gait parameters and GAITRite analysis system-based gait parameters (r = .99, .97, .41 for gait velocity, cadence, step length, respectively). CONCLUSION: The results suggest that specific opportunities exist for smartphone-based gait assessment as an alternative to conventional gait assessment. Moreover, smartphone-based gait assessment can provide objective information about changes in the spatio-temporal gait parameters of stroke subjects.

• Journal of Biomedical Engineering Research
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• v.26 no.3
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• pp.145-150
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• 2005

A new gait detection system using both FSR (force sensing resistor) sensors and a gyrosensor was developed to detect various gait patterns. FSR sensors were put in self-designed shoe insoles and a gyrosensor was attached to the heel of a shoe. An algorithm was also developed to determine eight different gait transitions during four gait phases: heel-strike, foot-flat, heel-off and swing. The developed system was evaluated from nine heathy mans and twelve hemiplegic patients. Healthy volunteers were asked to walk in various gait patterns: level walking, fore-foot walking and stair walking. Only the level walking was performed in hemiplegic patients. The gait detection system was compared with a optical motion analysis system and the outputs of the FSR sensors. In healthy subjects, the developed system detected successfully more than $99\%$ for both level walking and fore-foot walking. For stair walking, the successful detection rate of the system was above$97\%$. In hemiplegic patients, the developed system detected approximately 98% of gait transitions. The developed gait phase detection system will be helpful not only to determine pathological gait phases but also to apply prosthetics, orthotics and functional electrical stimulation for patients with various gait disorders.

• The Journal of the Society of Stroke on Korean Medicine
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• v.14 no.1
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• pp.23-30
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• 2013

■ Objectives This study is to examine change of gait parameters on shoes sole height(high heels, MBT shoes, house shoes) through gait analyzer. ■ Methods The subjects of this study were 12 women in their twenties. Gait analysis system is 5m in total length and gait is led to be comfortable. They put three kinds of shoes each and were led to walk 5m on gait analysis system. ■ Results There were significant differences in step length, single support and load response of gait parameters and in stride length and total double support at double support phase. ■ Conclusion Muscle activity differs in that different that shoes sole height and form because tibialis anterior muscle has strengthen and gastrocnemius has stretched. Therefore we think that patients with knee joint problem consider gait parameters when shoes select.

• Journal of Korean Physical Therapy Science
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• v.29 no.1
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• pp.30-40
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• 2022

Background: This study analyzed the reliability of smart guides for gait analysis in patients with stroke. Design: Cross-sectional study. Methods: The participants of the study were 30 patients with stroke who could walk more than 10 m and had an MMSE-K test score of ≥24. Prior to the experiment, the subjects or their guardians entered their demographic characteristics including gender, age, height, weight into the prepared computer. The experiment was conducted in a quiet, comfortable, and independent location, and the patient was reminded of the equipment description, precautions, and safety rules for walking. A smart insole was inserted into the shoes of the patients and the shoes were put on before the patients walked three times on the 5-m gait analysis system mat installed in the laboratory. Results: The reliability of the equipment was compared with that of the gait analysis system, and the results of this study are as follows: among the gait analysis items, velocity had an ICC=0.982, the cadence had an ICC=0.905, the swing phase on the side of the gait cycle had an ICC=0.893, the swing phase on the side of the gait had an ICC=0.839, that on the non-affected side had an ICC=0.939, single support on the affected side had an ICC=0.812, and support on the non-affected side had an ICC=0.767. Conclusion: The results of this study indicate no statistical difference between the smart insole and the gait analysis system. Therefore, it is believed that real-time gait analysis through smart insole measurement could help patients in rehabilitation.

• Journal of Biomedical Engineering Research
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• v.28 no.2
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• pp.218-228
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• 2007

Modem concepts of gait rehabilitation after stroke favor a task-specific repetitive approach. In practice, the required physical effort of the therapists limits the realization of this approach. Therefore, a mechanized gait trainer enabling nonambulatory patients to have the repetitive practice of a gait-like movement without overstraining therapists was constructed. In this study, we developed an active gait training system for patients with gait disorder. This system provides joint movements to patients who cannot carry out an independent gait. It provides a normal stance-swing ratio of 60:40 using an eccentric configuration of two gears. Joint motions of the knee and the ankle were evaluated with using the 3D motion analysis system and compared with the results from the multi-body dynamics simulation. In addition, clinical investigations were also performed for low stroke patients during the 6-week gait training. Results from the dynamics simulation showed that joint movements of the knee and the ankle were affected by the gear size, the step length and the length of the foot plate, except the radius of curvature of the foot guide plate. Also, the 6-week gait training revealed relevant improvements of the gait ability in all low subjects. Functional ambulation category levels of subjects after training were 2 in three patients and 1 in a patient. The developed active gait trainer seems feasible as an adjunctive tool in gait rehabilitation after stroke.