• 대한산업공학회지
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• 제33권4호
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• pp.480-486
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• 2007

A gait diagnosis supporting system is necessary to evaluate the characteristics of abnormal gait of a patient in a systematic and efficient manner. The present study developed a gait diagnosis supporting system which compares abnormal gait of a patient with a reference gait data and presents abnormal gait characteristics in an organized form. Three types of diagnosis modules were developed for the spatio-temporal, kinematic and kinetic gait parameters, and a gait data for Korean normal adults was used for the reference data of the system. The system was applied to evaluate the gait pattern of three arthritis patients and the abnormal gait characteristics of them could be easily identified with a systematic and graphical presentation.

• 로봇학회논문지
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• 제18권2호
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• pp.182-188
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• 2023

Due to the acceleration of an aging society, the need for lower limb exoskeletons to assist gait is increasing. And for use in daily life, it is essential to have technology that can accurately estimate gait phase even in the walking environment and walking speed of the wearer that changes frequently. In this paper, we implement an LSTM-based gait phase estimation learning model by collecting gait data according to changes in gait speed in outdoor level ground and stair environments. In addition, the results of the gait phase estimation error for each walking environment were compared after learning for both max hip extension (MHE) and max hip flexion (MHF), which are ground truth criteria in gait phase divided in previous studies. As a result, the average error rate of all walking environments using MHF reference data and MHE reference data was 2.97% and 4.36%, respectively, and the result of using MHF reference data was 1.39% lower than the result of using MHE reference data.

• The Journal of Korean Physical Therapy
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• 제35권3호
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• pp.64-70
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• 2023

Purpose: This study investigated the influence of attention-demanding tasks on gait and measured differences in the temporal, spatial and kinematic characteristics between young healthy adults and elderly healthy adults. Methods: We recruited 16 healthy young adults and 15 healthy elderly adults in this study. All participants performed two cognitive tasks: a subtraction dual-task (SDT) and working memory dual-task (WMDT) during gait plus one normal gait. Using the LEGSys+ system, knee and hip-joint kinematic data during stance and swing phase and spatiotemporal parameter data were assessed in this study. Results: In the elderly adult group, attention-demanding tasks with gait showed a significant decrease in hip-joint motion during the stance phase, compared to the normal gait. Step length, stride length and stride velocity of the elderly adult group were significantly decreased in WMDT gait compared to normal gait (p<0.05). In the young adult group, kinematic data did not show any significant difference. However, stride velocity and cadence during SDT and WMDT gaits were significantly decreased compared to those of normal gait (p<0.05). Conclusion: We determined that attention-demanding tasks during gait in elderly adults can induce decreased hip-joint motion during stance phase and decreased gait speed and stride length to maintain balance and prevent risk of falling. We believe that understanding the changes during gait in older ages, particularly during attention-demanding tasks, would be helpful for intervention strategies and improved risk assessment.

• 전기학회논문지
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• 제62권10호
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• pp.1436-1443
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• 2013

A variety of studies on imitating the skeletal structure and the gait of legged animals have been done in order to develop walking robots which have an ability to adapt to atypical environments. In this paper, we analyzed the gait of a Bearded dragon lizard using the motion capture system, proposed a calibration scheme of the motion data and generated the trotting gait of a lizard based on the calibrated data. Also, we constructed the dynamic model based on the biometric data of a Bearded dragon lizard and applied the trotting gait of the lizard to the dynamic model. We verified the validity of the gait with the commercial dynamic simulation software.

• 대한물리치료과학회지
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• 제27권3호
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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.

• PNF and Movement
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• 제18권2호
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• pp.287-296
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• 2020

Purpose: The study aims were to develop a wearable inertial sensor-based gait analysis device that uses machine learning algorithms, and to validate this novel device using temporal gait parameters. Methods: Thirty-four healthy young participants (22 male, 12 female, aged 25.76 years) with no musculoskeletal disorders were asked to walk at three different speeds. As they walked, data were simultaneously collected by a motion capture system and inertial measurement units (Reseed®). The data were sent to a machine learning algorithm adapted to the wearable inertial sensor-based gait analysis device. The validity of the newly developed instrument was assessed by comparing it to data from the motion capture system. Results: At normal speeds, intra-class correlation coefficients (ICC) for the temporal gait parameters were excellent (ICC [2, 1], 0.99~0.99), and coefficient of variation (CV) error values were insignificant for all gait parameters (0.31~1.08%). At slow speeds, ICCs for the temporal gait parameters were excellent (ICC [2, 1], 0.98~0.99), and CV error values were very small for all gait parameters (0.33~1.24%). At the fastest speeds, ICCs for temporal gait parameters were excellent (ICC [2, 1], 0.86~0.99) but less impressive than for the other speeds. CV error values were small for all gait parameters (0.17~5.58%). Conclusion: These results confirm that both the wearable inertial sensor-based gait analysis device and the machine learning algorithms have strong concurrent validity for temporal variables. On that basis, this novel wearable device is likely to prove useful for establishing temporal gait parameters while assessing gait.

• 한국컴퓨터정보학회논문지
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• 제23권2호
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• pp.17-26
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• 2018

In this paper, we propose a gait type classification method based on pressure sensor which reflects various terrain and velocity variations. In order to obtain stable gait classification performance, we divide the whole gait data into several steps by detecting the swing phase, and normalize each step. Then, we extract robust features for both topographic variation and speed variation by using the Null-LDA(Null-Space Linear Discriminant Analysis) method. The experimental results show that the proposed method gives a good performance of gait type classification even though there is a change in the gait velocity and the terrain.

• International journal of advanced smart convergence
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• 제12권3호
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• pp.61-67
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• 2023

Gait analysis plays a key role in the research field of exploring and understanding human movement. By quantitatively analyzing the complexity of human movement and the various factors that influence it, it is possible to identify individual gait characteristics and abnormalities. This is especially true for people with walking difficulties or special circumstances, such as amputee, for example. This is because it can help us understand their gait characteristics and provide individualized rehabilitation plans. In this paper, we compare and analyze the differences in ankle joint motion and angles between normal and amputee. In particular, a filtering process was applied to the ankle joint angle data to obtain high accuracy results. The results of this study can contribute to a more accurate understanding and improvement of the gait patterns of normal and amputee.

• 대한물리의학회지
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• 제16권1호
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• pp.17-22
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• 2021

PURPOSE: This study examined the correlations between gait, static balance, and pelvic inclination in patients with chronic stroke. METHODS: Twenty-two chronic stroke patients were included in this study. The subjects participated in gait, static balance, and pelvic inclination tests. In the gait measurement, the cadence and gait velocity were measured, and the average of three trials was calculated and recorded. The static balance was measured using a force platform. The data was captured for ten seconds, and five successful trials were recorded. Pelvic inclination in the sagittal plane was measured using a palpation meter. For data processing, a KolmogorovSmirnov test was used to determine the type of distribution for all variables. Pearson's correlation coefficient was used for correlation analysis. The correlations among the gait, static balance, and pelvic inclination was calculated. The level of significance was .05. RESULTS: Significant negative correlations were observed between the gait variables (cadence, velocity) and static balance variables (COP path length, COP average velocity, and 95% confidence ellipse area) (p < .05). On the other hand, there was no significant correlation between pelvic inclination and gait or between the pelvic inclination and static balance variables. CONCLUSION: Significant correlations were observed between the gait function and static balance. On the other hand, there were no significant correlations between the pelvic inclination and gait and static balance. These results suggest that the pelvic inclination is not an important consideration for increasing the gait function and static balance.

• Annals of Clinical Neurophysiology
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• 제5권2호
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• pp.197-201
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• 2003

Background: The aim of this study is to present the basic reference data of kinematic gait analysis of normal Korean adults with 3 dimensional electrogoniometer, $Domotion^{(R)}$. Method: The basic kinematic gait parameters of hip, knee and ankle joints on the sagittal plane were obtained from 10 healthy adults with 5 repetition for each. Three-dimensional gait analysis was performed with $Domotion^{(R)}$ electrogoniometer in 10 meters long flat floor. Each data collected was processed with IBM PC equipped with gait analysis program. Results: Mean maximal hip flexion was $23.05^{\circ}{\pm}4.62^{\circ}$and mean maximal hip extension was $6.46^{\circ}{\pm}1.30^{\circ}$. Knee flexion was observed with two peak values. The first peak knee flexion was $6.50^{\circ}{\pm}2.07^{\circ}$ at 20.4% of gait cycle and the second peak flexion was $50.34^{\circ}{\pm}2.23^{\circ}$ at 75.8% of gait cycle. Mean maximum ankle dorsiflexion was $5.57^{\circ}{\pm}1.19^{\circ}$ at 44% of gait cycle and mean maximum ankle plantar flexion was $15.51^{\circ}{\pm}1.73^{\circ}$ at 68.5% of gait cycle. Conclusion: We concluded three dimensional gait analysis with electrogoniometer $Domotion^{(R)}$ offers a valid and reliable kinematic data and the application of this tools for clinical gait evaluation will be helpful in management of pathological gait.