• Journal of Korean Physical Therapy Science
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• 제29권1호
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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.

• ETRI Journal
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• 제42권1호
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• pp.46-53
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• 2020

Gait analysis is an effective clinical tool across a wide range of applications. Recently, inertial measurement units have been extensively utilized for gait analysis. Effective gait analyses require good estimates of heel-strike and toe-off events. Previous studies have focused on the effective device position and type of triaxis direction to detect gait events. This study proposes an effective heel-strike and toe-off detection algorithm using a smart insole with inertial measurement units. This method detects heel-strike and toe-off events through a time-frequency analysis by limiting the range. To assess its performance, gait data for seven healthy male subjects during walking and running were acquired. The proposed heel-strike and toe-off detection algorithm yielded the largest error of 0.03 seconds for running toe-off events, and an average of 0-0.01 seconds for other gait tests. Novel gait analyses could be conducted without suffering from space limitations because gait parameters such as the cadence, stance phase time, swing phase time, single-support time, and double-support time can all be estimated using the proposed heel-strike and toe-off detection algorithm.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• 제13권1호
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• pp.199-206
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• 2018

This study was to investigate the Influence of smart phone use on gait in healthy adults. Twenty healthy adults were recruited in this study. All subjects performed twice for each normal gait and smart phone gait. The normal gait walked at their chosen speed, and the smart phone gait walked while watching the video. GAITRite system was used to identify the temporal and spatial variables related to the gait pattern during walking. Statistical analysis was analyzed by paired t-test. In comparison of temporal variables, smart phone gait was significantly lower in gait speed and cadence than in normal gait(p<.05), and was significantly longer in single support time and double support time(p<.05). In comparison of spatial variables, smart phone gait was significantly shorter in step length and stride length than in normal gait(p<.05) and significantly longer in step width(p<.05). The results of this study demonstrated that smartphone use can negatively affect the correct gait patterns during walking.

• Physical Therapy Rehabilitation Science
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• 제11권2호
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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.

• Journal of The Korean Society of Integrative Medicine
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• 제9권4호
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• pp.291-298
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• 2021

Purpose: The Pedisole is a newly developed shoe-mounted wearable assessment system for analyzing balance and gait. This study aimed to determine the reliability and validity of the parameters provided by the system for static balance and gait analysis of healthy adults. Methods: This study included 38 healthy adults (22.4±1.9 years) with no history of injury in the lower limbs. All participants were asked to perform balance and gait tasks for undertaking measurements. For analysis of balance, both the smart Pedisole and Pedoscan systems were concurrently used to analyze the path length of the center of pressure (COP) and the weight ratio of the left and right for 10 s. Gait was measured using the smart Pedisole and GaitRite walkway systems simultaneously. The participants walked at a self-selected preferred gait speed. The cadence, stance time, swing time, and step time were used to analyze gait characteristics. Using the paired t-test, the intra-class coefficient correlation (ICC) was calculated for reliability. The Spearman correlation was used to assess the validity of the measurements. In total, data for balance from 36 participants and the gait profiles of 37 participants were evaluated. Results: There were significant differences between the COP path lengths (p<.050) derived from the two systems, and a significant correlation was found for COP path length (r=.382~.523) for static balance. The ICC for COP path length and weight ratio was found to be greater than .687, indicating moderate agreement in balance parameters. The ICC of gait parameters was found to be greater than .697 except for stance time, and there was significant correlation (r=.678~.922) with the GaitRite system. Conclusion: The newly developed smart insole-type Pedisole system and the related application are useful, reliable, and valid tools for balance and gait analysis compared to the gold standard Pedoscan and the GaitRite systems in healthy individuals.

• Journal of the Korea Society of Computer and Information
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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.

• Journal of rehabilitation welfare engineering & assistive technology
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• 제7권2호
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• pp.139-144
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• 2013

Various types of disease in the nervous and musculoskeletal system can change gait, and the gait analysis is very important in determining the progression of the disease. Most methods of analyzing gait are subject to high-priced equipment and spatial restrictions. This study used smart phone images and the walking track analysis program to make a comparative analysis with the existing gait analysis on the basis of the stride length measurements and the changes in the knee joint angle for walking. The test necessary to analyze gait was conducted in seven healthy men, and data about the angle of right and left knee joints and stride length were used to analyze gait. The gait analysis in this study obtained the similar results to the existing ones. The use of the methods suggested in this study will enable gait analysis to be made without high-priced equipment and spatial restrictions.

• Korean Journal of Applied Biomechanics
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• 제29권2호
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• pp.71-77
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• 2019

Objective: The aim of this study was to investigate postural control strategies on smart phone use during gait in over 50-year-old adults. Method: 8 elderly subjects (age: $55.5{\pm}3.29yrs$, height: $159.75{\pm}4.20cm$, weight: $62.87{\pm}8.44kg$) and 10 young subjects (age: $23.8{\pm}3.19yrs$, height: $158.8{\pm}5.97cm$, weight: $53.6{\pm}5.6kg$) participated in the study. They walked at a comfortable pace in a gaitway of ~8 m while: 1) reading text on a smart phone, 2) typing text on a smart phone, or 3) walking without the use of a phone. Gait parameters and kinematic data were evaluated using a three-dimensional movement analysis system. Results: The participants read or wrote text messages they walked with: slower speed; lesser stride length and step width; greater flexion range of motion of the head; more flexion of the thorax in comparison with normal walking. Conclusion: Texting or reading message on a smart phone while walking may pose an additional risk to pedestrians' safety.

• International journal of advanced smart convergence
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• 제9권4호
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• pp.139-148
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• 2020

Walking is one of the most natural and repetitive actions we do in our daily lives. However, many modern people have problems with shoulders, back and spine due to incorrect walking habits. Therefore, it is becoming important to diagnose and correct wrong walking habits, for example, in-toeing, out-toeing, etc. early, which can be a precursor to various diseases. In this study, we developed the system to diagnose and prevent incorrect gait by grasping and analyzing the angle and muscle activity of the foot according to the typical wrong gait type through MPU 6050 acceleration sensor and the surface EMG sensor. Through a smartphone, numerical and visualization screens based on walking can be used to represent the angle of the feet, real-time EMG values, and even the number of steps. The correction effect was enhanced by improving the cognitive ability through a system that allows individuals to easily diagnose gait through smart devices and improve them according to their own problems.