• The Journal of Korean Physical Therapy
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• 제21권2호
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• pp.87-95
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• 2009

Purpose: This study reports the basic reference data of the specific gait parameters for Korean normal adults. Methods: The basic gait parameters were extracted from 73 Adults (35 men and 38 women), 18 to 33 years of age, using a Vicon MX motion analysis system. The segment kinetics, such as joint moment and power, was analyzed at the hip, knee and ankle. Results: The motion patterns are typically associated with a specific phase of the gait cycle. The temporal-spatial gait parameters of Korean normal adults, such as cadence, walking speed, stride length, single support and double support, were similar to the other western reference data. The kinetic parameters of Korean normal adults, such as joint moments of force, joint mechanical power generation or absorption and ground reaction forces, were also similar to other western reference datasets. Conclusion: This study demonstrates that objective gait analysis can be used to document the gait patterns of normal healthy adults. The techniques of 3-dimensional temporal-spatial gait parameters and kinematic parameters analysis can provide a detailed biomechanical description of a normal and pathological gait.

• 한국정밀공학회지
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• 제29권11호
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• pp.1199-1206
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• 2012

Recently, technologies to help the elderly or disabled people who have difficulty in walking are being developed. In order to develop these technologies, it is necessary to construct a system that gathers the gait data of people and analysis of these data is also important. In this research, we constructed the development of sensor system which consists of pressure sensor, three-axis accelerometer and two-axis gyro sensor. We used k-means clustering algorithm to classify the data for characterization, and then calculated the symmetry index with histogram which was produced from each cluster. We collected gait data from sensors attached on two subjects. The experiment was conducted for two kinds of gait status. One is walking with normal gait; the other is walking with abnormal gait (abnormal gait means that the subject walks by dragging the right leg intentionally). With the result from the analysis of acceleration component, we were able to confirm that the analysis technique of this data could be used to determine gait symmetry. In addition, by adding gyro components in the analysis, we could find that the symmetry index was appropriate to express symmetry better.

• 한국콘텐츠학회논문지
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• 제9권12호
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• pp.674-682
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• 2009

본 연구는 편마비 환자에게 수중 걷기 훈련이 미치는 영향에 대해 알아보고자 10주간 수중 걷기 훈련과 지상 걷기 훈련 후 족저압, 거골하관절의 움직임, 보향각, 보행 속도를 측정하였다. 대상자는 20명으로 수중걷기 훈련 그룹(n=10)이 엄지발가락 영역, 뒤꿈치영역, 발허리부분의 족저압이 유의하게 증가하였고, 거골하관절의 움직임과 보향각이 안정화되었으며, 보행 속도 또한 증가함을 보였다. 보행 속도의 증가와 거골하 관절의 움직임 안정화와 보향각의 감소는 수중 걷기가 편마비 환자의 보행 속도 뿐만아니라 보행의 안정화에도 영향을 미친다고 생각되어진다. 또한 엄지발가락 영역과 뒤꿈치 영역의 족저압 증가는 보행시 뒤꿈치 닿기와 발가락 밀기 동작의 회복으로 해석되어진다. 이와 같은 결과로 볼때, 현재 사용되고 있는 치료사에 의한 전문적인 물리치료를 받지 못하는 환자들의 경우 스스로 수중 걷기 훈련만으로도 지상 걷기에 비하여 많은 효과를 볼 수 있을 것으로 기대된다.

• 한국운동역학회지
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• 제25권1호
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• pp.123-130
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• 2015

Purpose : The purpose of this study was to investigate the reliability of 3D-inertia measurement unit (IMU) based shoes in gait analysis. This was done with respect to the results of the optical motion capturing system and to collect reference gait data of healthy subjects with this device. Methods : The Smart Balance$^{(R)}$ system of 3D-IMU based shoes and Osprey$^{(R)}$ motion capturing cameras were used to collect motion data simultaneously. Forty four healthy subjects consisting of individuals in 20s (N=20), 40s (N=13), and 60s (N=11) participated in this study voluntarily. They performed natural walking on a treadmill for one minute at 4 different target speeds (3, 4, 5, 6 km/h), respectively. Results : Cadence (ICC=.998), step length (ICC=.970), stance phase (ICC=.845), and double-support phase (ICC=.684) from 3D-IMU based shoes were in agreement with results of optical motion system. Gait data of healthy subjects according to different treadmill speeds and ages were matched to previous literature showing increased cadence and reduced step length for elderly subjects. Conclusion : Conclusively, 3D-IMU based shoes in gait analysis were a satisfactory alternative option in measuring linear gait parameters.

• 한국운동역학회지
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• 제30권3호
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• pp.247-253
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• 2020

Objective: This study aimed to examine the effects of real-time visual feedback gait training on gait stability in older adults. Method: Twelve older adults participated in this study, being divided into 2 groups including a) visual feedback (VF) and b) non-visual feedback (NVF) groups. For 4 weeks, VF performed a treadmill walking training with real-time visual feedback about their postural information while NVF performed a normal treadmill walking training. For evaluations of gait stability, kinematic data of 15-minute treadmill walking were collected from depth-based motion capture system (30 Hz, exbody, Korea). Given that step lengths in both right and left sides were determined based on kinematic data, three variables including step difference, coefficient of variation, approximate entropy were calculated to evaluate gait symmetry, variability and complexity, respectively. Results: For research findings, VF exhibited significant improvements in gait stability after 4-week training in comparison to NVF, particularly in gait symmetry and complexity measures. However, greater improvement in gait variability was observed in NVF than VF. Conclusion: Given that visual feedback walking gives potential effectiveness on gait stability in older adults, gait training with visual feedback may be a robust therapeutic intervention in people with gait disturbances like instability or falls.

• Journal of information and communication convergence engineering
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• 제14권1호
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• pp.45-50
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• 2016

Gait-based classification has gained much interest as a possible authentication method because it incorporate an intrinsic personal signature that is difficult to mimic. The study investigates machine learning techniques to mitigate the natural variations in gait among different subjects. We incorporated several machine learning algorithms into this study using the data mining package called Waikato Environment for Knowledge Analysis (WEKA). WEKA's convenient interface enabled us to apply various sets of machine learning algorithms to understand whether each algorithm can capture certain distinctive gait features. First, we defined 24 gait features by analyzing three-axis acceleration data, and then selectively used them for distinguishing subjects 10 years of age or younger from those aged 20 to 40. We also applied a machine learning voting scheme to improve the accuracy of the classification. The classification accuracy of the proposed system was about 81% on average.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.542-544
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• 2005

This paper describes the detection of spatio-temporal parameter using an accelerometer and footswitches to evaluate a symmetry and balance of hemiplegic patients. We detected gait data using a 3-axis accelerometer that mounted between L3 and IA intervertebral area and footswitches made by FSR-Sensor attached insole. To minimize the error of the gait parameters to be detected incorrectly in case of using only accelerometer, we enhancement the performance of detection by measuring an accelerometer and foots witches data at the same time. So, it was possible to detect more accurate gait parameters. As a result, we can confirm the symmetry and balance of hemiplegic patients. In the future. these results could be used to evaluate the walking ability in hemiplegic patients in clinical pratice.

• 한국산업융합학회 논문집
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• 제22권4호
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• pp.459-465
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• 2019

There is an increasing interest in health and research on methods for measuring human body information. The importance of continuously observing information such as the step change and the walking speed is increasing. At a person's gait, information about the disease and the currently weakened area can be known. In this paper, gait is measured using wearable walking module built in shoes. We want to make continuous measurement possible by simplifying gait measurement method. This module is designed to receive information of gyro sensor and acceleration sensor. The designed module is capable of WiFi communication and the collected walking information is stored in the server. The information stored in the server is corrected by integrating the acceleration sensor and the gyro sensor value. A band-pass filter was used to reduce the error. This data is categorized by the Gait Finder into walking and waiting states. When walking, each step is divided and stored separately for analysis.

• 한국멀티미디어학회논문지
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• 제23권8호
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• pp.927-939
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• 2020

Gait recognition can identify people's identity from a long distance, which is very important for improving the intelligence of the monitoring system. Among many human features, gait features have the advantages of being remotely available, robust, and secure. Traditional gait feature extraction, affected by the development of behavior recognition, can only rely on manual feature extraction, which cannot meet the needs of fine gait recognition. The emergence of deep convolutional neural networks has made researchers get rid of complex feature design engineering, and can automatically learn available features through data, which has been widely used. In this paper,conduct feature metric learning in the three-dimensional space by combining the three-dimensional convolution features of the gait sequence and the Siamese structure. This method can capture the information of spatial dimension and time dimension from the continuous periodic gait sequence, and further improve the accuracy and practicability of gait recognition.

• 한국컴퓨터정보학회논문지
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• 제27권11호
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• pp.29-38
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• 2022

본 논문에서는 멀티 센서가 장착된 스마트 인솔로 측정한 보행 데이터에 대해 앙상블 딥러닝 네트워크를 이용하여 보행의 타입을 분류하는 시스템을 제안한다. 보행 타입 분류 시스템은 인솔에 의해 측정된 데이터를 정규화하는 부분과 딥러닝 네트워크를 이용하여 보행의 특징을 추출하는 부분, 그리고 추출된 특징을 입력으로 보행의 타입을 분류하는 부분으로 구성되어 있다. 서로 다른 특성을 가지는 CNN과 LSTM을 기반으로 하는 네트워크를 독립적으로 학습하여 두 종류의 보행 특징 맵을 추출하였으며, 각각의 분류 결과를 결합하여 최종적인 앙상블 네트워크의 분류 결과를 도출하였다. 20~30대 성인의 걷기, 뛰기, 빠르게 걷기, 계단 오르기와 내려가기, 언덕 오르기와 내려가기의 7종류의 보행에 대해, 스마트 인솔을 이용하여 실측한 멀티 센서 데이터를 제안한 앙상블 네트워크로 분류해 본 결과 90% 이상의 높은 분류율을 보이는 것을 확인하였다.