• Journal of the Korea Society of Computer and Information
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• v.23 no.2
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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 the Korea Society of Computer and Information
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• v.27 no.11
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• pp.29-38
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• 2022

This paper proposes a system for classifying gait types using an ensemble deep learning network for gait data measured by a smart insole equipped with multi-sensors. The gait type classification system consists of a part for normalizing the data measured by the insole, a part for extracting gait features using a deep learning network, and a part for classifying the gait type by inputting the extracted features. Two kinds of gait feature maps were extracted by independently learning networks based on CNNs and LSTMs with different characteristics. The final ensemble network classification results were obtained by combining the classification results. For the seven types of gait for adults in their 20s and 30s: walking, running, fast walking, going up and down stairs, and going up and down hills, multi-sensor data was classified into a proposed ensemble network. As a result, it was confirmed that the classification rate was higher than 90%.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.2
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• pp.130-135
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• 2016

Gait recognition has advantage of non-contact type recognition. But It has disadvantage of low recognition rate when the pedestrian silhouette is changed due to bag or coat. In this paper, we proposed new method using combination of gait energy image feature and thermal face image feature. First, we extracted a face image which has optimal focusing value using human body rate and Tenengrad algorithm. Second step, we extracted features from gait energy image and thermal face image using linear discriminant analysis. Third, calculate euclidean distance between train data and test data, and optimize weights using genetic algorithm. Finally, we compute classification using nearest neighbor classification algorithm. So the proposed method shows a better result than the conventional method.

• Journal of the Korean Society of Physical Medicine
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• v.16 no.3
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• pp.123-130
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• 2021

PURPOSE: Robotic gait training is being used increasingly to improve the gross motor performance and gait speed. The present study examined the effectiveness of a novel end-effector type of robotic gait training (RGT) system on standing, walking, running, and jumping functions, as well as the gait speed in children with spastic cerebral palsy. METHODS: Eleven children with spastic cerebral palsy Gross Motor Function Classification System (GMFCS) levels I-III (6 males; age range, 15.09 ± 1.44 years) were examined. They underwent 24 sessions (30 minutes/sessions, one time/day, three days/week for eight consecutive weeks) of RGT. The Gross Motor Function Measure-88 D domain (GMFM D), and GMFM E were assessed with a pretest and posttest of RGT. The setting was a one-group pretest-posttest design. RESULTS: A comparison of the pre-test and post-test show that the outcomes in post-test of GMFM D (p < .01), GMFM E (p < .05), and 10MWT were improved significantly after RGT intervention. CONCLUSION: The present study provided the first evidence on the effects of an eight-weeks RGT intervention in participants with spastic CP. The outcomes of this clinical study showed that standing performance, locomotion function, and gait speed increased in after 24 sessions of the end-effector RGT system in children with spastic cerebral palsy.

• Physical Therapy Korea
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• v.22 no.2
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• pp.30-40
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• 2015

Robot-assisted rehabilitation therapy has been used to increase physical function in post-stroke patients. The aim of this meta-analysis was to identify whether robot-assisted gait training can improve patients' functional abilities. A comprehensive search was performed of PubMed, Cochrane Central Register of Controlled Trials (CENTRAL), Physiotherapy Evidence Database (PEDro), Academic Search Premier (ASP), ScienceDirect, Korean Studies Information Service System (KISS), Research Information Sharing Service (RISS), Korea National Library, and the Korean Medical Database up to April, 2014. Fifteen eligible studies researched the effects of robot-assisted gait training to a control group. All outcome measures were classified by International Classification of Functioning, Disability, and Health (ICF) domains (body function and structures, activity, and participation) and were pooled for calculating the effect size. The overall effect size of the robot-assisted gait training was .356 [95% confidence interval (CI): .186~.526]. When the effect was compared by the type of electromechanical robot, Gait Trainer (GT) (.471, 95% CI: .320~.621) showed more effective than Lokomat (.169, 95% CI: .063~.275). In addition, acute stroke patients showed more improvement than others. Although robot-assisted gait training may improve function, but there is no scientific evidence about the appropriate treatment time for one session or the appropriate duration of treatment. Additional researchers are needed to include more well-designed trials in order to resolve these uncertainties.

• Journal of Biomedical Engineering Research
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• v.38 no.3
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• pp.129-136
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• 2017

The exoskeleton robot is a technology developed to be used in various fields such as military, industry and medical treatment. The exoskeleton robot works by sensing the movement of the wearer. By recognizing the wearer's daily activities, the exoskeleton robot can assist the wearer quickly and efficiently utilize the system. In this study, LDA, QDA, and kNN are used to classify gait types through kinetic data obtained from subjects. Walking was selected from general walking and stair walking which are mainly performed in daily life. Seven IMUs sensors were attached to the subject at the predetermined positions to measure kinematic factors. As a result, LDA was classified as 78.42%, QDA as 86.16%, and kNN as 87.10% ~ 94.49% according to the value of k.

• Archives of design research
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• v.14 no.2
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• pp.95-105
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• 2001

The gait characteristics and shapes of human foot are changed by their diseases and habits. Especially, it is known that the size and shape of foot of the elderly(aged) differ from these of youth foot, because of muscle degeneration and wearing footwear for a long time. Therefore, to design and make last for the elder footwear, anthropometric data measured elder feet and classified their foot type will be necessary. Nevertheless, elder footwear has been designed and made by using the same last of the youth. Therefore, to design elder footwear, we measured 49 anthropometric data on foot of the 252 elderly males and females, and classified their foot shape and type according to FI(foot index) and MPA(matarars-phalanx angle) in this study. The results showed that the elderly has a tendency of slenderizing on foot compare to the youth, and elderly females have more deformed foot type than elderly males. The results can be provided as basic information to the design of elder footwear.