• Journal of IKEEE
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• v.25 no.4
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• pp.679-688
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• 2021

Gait is an evaluation index used in various clinical area including brain nervous system diseases. Signal source localizing and time-frequency analysis are mainly used after extracting independent components for Electroencephalogram data as a method of measuring and analyzing brain activation related to gait. Existing treadmill-based walking EEG analysis performs signal preprocessing, independent component analysis(ICA), and source localizing by merging data after the multiple EEG measurements, and extracts representative component clusters through inter-subject clustering. In this study we propose an analysis method, without merging to single dataset, that performs signal preprocessing, ICA, and source localization on each measurements, and inter-subject clustering is conducted for ICs extracted from all subjects. The effect of data merging on the IC clustering and time-frequency analysis was investigated for the proposed method and two conventional methods. As a result, it was confirmed that a more subdivided gait-related brain signal component was derived from the proposed "non-merging" method (4 clusters) despite the small number of subjects, than conventional method (2 clusters).

• Journal of Biomedical Engineering Research
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• v.35 no.6
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• pp.227-233
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• 2014

In this paper, to quantitatively evaluate the degree of rehabilitation for the disabled of unilateral lower extremity, we compared the EMG pattern of normal and simulated abnormal gait. The EMG signal was measured at a rate of 1 kHz on the quadriceps and biceps femoris, the pressure sensor was attached to the sole in order to distinguish the gait cycle. Integrated EMG (IEMG) was obtained by the gait cycle, and classified four patterns that were the normal gait pattern, amplitude decrease pattern, reversed pattern, and irregular pattern. For comparison of the patterns, a curve fitting was performed using the trigonometric functions. The result of curve fitting, the method using a variable A that corresponds to the amplitude of the regression curve was able to distinguish the reverse pattern and remaining pattern. The coefficient of determination ($R^2$) representing coincidence of the pattern of the regression curve and EMG was confirmed the biggest value at the normal gait. Therefore, the degree of normal gait can be confirmed using the coefficient of determination. This results show that it is possible to quantitatively confirm the degree of unilateral lower extremity disabled rehabilitation, and it will be contributed to the study of efficient rehabilitation methods by objective analysis.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.10 no.2
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• pp.155-162
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• 2016

The purpose of this study was to determine the walking imbalance using the EMG(electromyogram). To confirm the effectiveness of the proposed encoder and acceleration, EMG sensor based gait imbalance determination system. This experiment was carried out to evaluation with a healthy adult male to 10 people. The Encoder device is attached to the hip and knee joint in order to measure the gait signal. The Accelerometer sensors are attached on the ankle. The EMG sensors are attached on the vastus lateralis and anterior tibialis. SI(Symmetry Index) was used as an index for determining the gait imbalance. To confirm if the judgment has been made correctly, the heel, regarded as the cause of unbalanced ambulation, was adjusted from 0 cm to 6 cm with intervals of 1.5 cm. In the cases of the encoder and the EMG, the difference of 0 cm and 1.5 cm is determined into normal walk but the other difference is distinguished into gait imbalance. In the case of the accelerometer, the difference of 0 cm, 1.5 cm and 3 cm is determined into normal walk but the other difference is distinguished into gait imbalance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2627-2642
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• 2023

Parkinson's disease (PD) is a typical, chronic neurodegenerative disease involving the concentration of dopamine, which can disrupt motor activity and cause different degrees of gait disturbance relevant to PD severity in patients. As current clinical PD diagnosis is a complex, time-consuming, and challenging task that relays on physicians' subjective evaluation of visual observations, gait disturbance has been extensively explored to make automatic detection of PD diagnosis and severity rating and provides auxiliary information for physicians' decisions using gait data from various acquisition devices. Among them, wearable sensors have the advantage of flexibility since they do not limit the wearers' activity sphere in this application scenario. In this paper, an attention-based temporal network (ATN) is designed for the time series structure of gait data (vertical ground reaction force signals) from foot sensor systems, to learn the discriminative differences related to PD severity levels hidden in sequential data. The structure of the proposed method is illuminated by Transformer Network for its success in excavating temporal information, containing three modules: a preprocessing module to map intra-moment features, a feature extractor computing complicated gait characteristic of the whole signal sequence in the temporal dimension, and a classifier for the final decision-making about PD severity assessment. The experiment is conducted on the public dataset PDgait of VGRF signals to verify the proposed model's validity and show promising classification performance compared with several existing methods.

• Korean Journal of Applied Biomechanics
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• v.31 no.1
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• pp.24-29
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• 2021

Objective: The goal of this study is to calculate and compare the Maximum Lyapunov Exponent (MLE) for the anteroposterior, mediolateral and vertical displacement of the markers attached to bony land marks of the trunk and foot. Method: Ten young and healthy male subjects (age: 26.5±3.27 years, height: 167.44±5.12 cm, and weight 69.5±7.36) participated in the study. Three-dimensional positional coordinate of eight different trunk and foot marker during walking on tread mill were analysed. Results: MLE values for anteroposterior displacement of the marker were found to be significantly different with MLE values for mediolateral and vertical displacement whereas MLE values for mediolateral displacement of the marker shows no significant difference with the MLE values for vertical displacement of the markers at significance level 0.05. Conclusion: Finding of this study suggest that it is essential to consider the displacement in all three direction to examine the real characteristic of a gait signal.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.7 no.1
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• pp.39-43
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• 2013

In this paper, we present a effective method of gait rehabilitation training using critical point of median frequency in muscle fatigue analysis using EMG. To target the five healthy volunteers, EMG signal were measured in the quadriceps femoris muscle and the tibialis anterior muscle in order to determine muscle fatigue. We performed a test targeting three adult male for 30 minutes on a treadmill at a speed of 6km/h same. EMG signal analysis in frequency and median frequency is calculated to quantification of muscle fatigue, and calculated the critical point which is saturated by muscle fatigue during 30 minutes. We set saturated point the threshold which muscle can withstand. The results of this paper, we are able to quantify the threshold of the muscle.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.10
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• pp.452-457
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• 2006

In this paper, an algorithm to detect steps in hemiplegic patients using a 3-axis accelerometer a紅ached on the trunk was proposed. The proposed algorithm consisted of the signal pre-processing, the step detector, the classification of steps and the calculation of stride time. Two FIR band-pass filters were designed and steps were measured by the combination of filtered signals in the vertical and the anteroposterior directions. In addition, the classification of steps and the calculation of stride time were computed by using the detected steps and lateral signals. For the experiment, fourteen hemiplegic patients were participated and the linear accelerations of the trunk and foot switch signals were measured synchronously. To evaluate the system performance, the detected steps and initial contacts by the foot switch were compared. The average error between the steps and initial contacts was 0.024ms and the difference of the average stride time was 0.01s. Finally, all gait events were detected exactly. Results showed that the accelerometry could use for the gait evaluation in clinical rehabilitation therapies.

• Journal of Korean Physical Therapy Science
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• v.26 no.2
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• pp.46-50
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• 2019

Background: The purpose of present study is to effect of arm swing on uphill road gait in healthy adults. Design: Cross-sectional study. Methods: This study was Participated in 15 healthy subjects. The subjects were allowed to walk uphill, about 10m from the line drawn on the floor. The subject stood at the starting line and started by pressing the start button with the signal "start", and I pressed Stop at the last incoming point. Walking with and without arm swing was measured twice in random draws. Results: Walking path with arm swing showed good results in walking path duration, cadence, speed, stride length, and Gait cycle duration rather than uphill walk without arm swing. Conclusion: As a results of this study, The arm swing is important in getting uphill.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.4
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• pp.142-148
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• 2016

Recently, human identification using various biological signals has been studied and human identification based on the gait has been actively studied. In this paper, we propose a human identification based on the EMG(Electromyography) signal of the thigh muscles that are used when walking. Various features such as RMS, MAV, VAR, WAMP, ZC, SSC, IEMG, MMAV1, MMAV2, MAVSLP, SSI, WL are extracted from EMG signal data and ANN(Artificial Neural Network) classifier is used for human identification. When we evaluated the recognition ratio per channel and features to select approptiate channels and features for human identification. The experimental results show that the rectus femoris, semitendinous, vastus lateralis are appropriate muscles for human identification and MAV, ZC, IEMG, MMAV1, MAVSLP are adaptable features for human identification. Experimental results also show that the average recognition ratio of method of using all channels and features is 99.7% and that of using selected 3 channels and 5 features is 96%. Therefore, we confirm that the EMG signal can be applied to gait based human identification and EMG signal based human identification using small number of adaptive muscles and features shows good performance.

• Journal of Electrical Engineering and Technology
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• v.10 no.5
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• pp.2155-2161
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• 2015

This study suggests an optical method to measure cardinal of gait (step width, step length, and stride length) with position sensitive detector (PSD). The effect of reflector’s shape (flat and cylinder) on the PSD output voltage was examined for the application of the suggested system to real situations with a curved shape reflector (e.g. shoes). Various mathematical models were evaluated to find the optimal equation for the distance measurement. Considering the effect of shape on detected signal, the inverse polynomial model was developed. The suggested method is simple to operate, low in cost, small in size, and can evaluate gait parameters in real time. This method is expected to be useful in the field of rehabilitation and sport science