• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.105.1-105
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• 2002

$\textbullet$ This paper deals with obstacle avoidance of a quadruped robot with a vision system and a PSD sensor. $\textbullet$ The vision system needs for obstacle recognition toward robot. $\textbullet$ Ths PSD sensor is also important element for obstacle recognition. $\textbullet$ We propose algorithm that recognizes obstacles with one vision and PSD sensor. $\textbullet$ We also propose obstacle avoidance algorithm with map from obstacle recognition algorithm. $\textbullet$ Using these algorithm, Quadruped robot can generate gait trajectory. $\textbullet$ Therefore, robot can avoid obstacls, and can move to target point.

• 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.

• The Korean Journal of Rehabilitation Nursing
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• v.5 no.2
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• pp.193-204
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• 2002

This study was a quasi-experimental study of nonequivalent control group pretest- posttest design to investigate the effect of aerobic walking exercise program on the physical & psychological functions of home stayed stroke patients. The data were collected during the period of May 20th to August 15th, 2001. The subjects for this study were 40 hemiplegic stroke patients with the experimental group consisting of 19 patients and the control group being composed of 21 patients. The patients selected for this study were: (a)living in J city who had been diagnosed with stroke and at home after being discharged from the hospital, (b)suffering from stroke for 6 months to 5 years, (c)without recognition disorder with the MMSE-K score above 25, (d)below 2 on the modified Ashworth scale, (e)free from heart and pulmonary disease (f)able to walk beyond 15 minutes for themselves. The aerobic walking exercise program for the experimental group was aerobic exercise and education and supportive care. The aerobic exercise was 8 weeks' period, three times a week, 35 to 50 minutes a day. And the education and supportive care was consisted of one home visiting and 2 times telephoning a week. The data were analysed by $X^2$-test, paired t-test and unpaired t-test and ANCOVA through SAS/PC program. The results of the study were as follows: 1. There was insignificant difference in the gait length experimental and control group. There was significant difference in the gait speed between the two groups. 2. There was significant difference in the dynamic valance between the two groups. 3. There was significant difference in ADL score between the two groups. 4. There was no significant difference in the depression between the two groups. As shown above, the results of 8 weeks' the aerobic walking exercise program for home stayed stroke patients produced positive effects on gait speed, dynamic valance, ADL score. And this program was expected that it was more effective in different intervention period, verified program. Also it was needed follow study.

• Journal of Advanced Navigation Technology
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• v.26 no.5
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• pp.365-372
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• 2022

Compared to sensors mainly used in human activity recognition (HAR) systems, inertial measurement unit (IMU) sensors are small and light, so can achieve lightweight system at low cost. Therefore, in this paper, we propose a binary neural network (BNN) based gait pattern analysis system using IMU sensor, and present the design and implementation results of an FPGA-based accelerator for computational acceleration. Six signals for gait are measured through IMU sensor, and a spectrogram is extracted using a short-time Fourier transform. In order to have a lightweight system with high accuracy, a BNN-based structure was used for gait pattern classification. It is designed as a hardware accelerator structure using FPGA for computation acceleration of binary neural network. The proposed gait pattern analysis system was implemented using 24,158 logics, 14,669 registers, and 13.687 KB of block memory, and it was confirmed that the operation was completed within 1.5 ms at the maximum operating frequency of 62.35 MHz and real-time operation was possible.

• Journal of Sensor Science and Technology
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• v.22 no.1
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• pp.54-64
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• 2013

In this study, we developed a falling recognition system to transmit SMS data through CDMA communication using a three axises acceleration sensor and a two axises gyro sensor. 5 healthy men were selected into a control group, and the fall recognition system using the three axises acceleration sensor and the two axises gyro sensor was devised to conduct an experiment. The system was attached to the upper of their sternum. According to the experiment protocol, the experiment was carried out 3 times repeatedly divided into 3 specific protocols: falling during gait, falling in stopped state, and falling in everyday life. Data obtained in the falling recognition system and LabVIEW 8.5 were used to decide if falling corresponds to that regulated in an analysis program applying an algorithm proposed in this study. In addition, results from falling recognition were transmitted to designated cellular phone in a SMS (Shot Message Service) form. These research results show that an erroneous detection rate of falling reached 19% in applying an acceleration signal only; 6% in applying an angular velocity; and 2% in applying a proposed algorithm. Such finding suggests that an erroneous detection rate of falling is improved when the proposed algorithm is applied incorporated with acceleration and angular velocity. In this study therefore, we proposed that a falling recognition system implemented in this study can make a contribution to the recognition of falling of the aged or the disabled.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.3
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• pp.223-228
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• 2011

In this paper, we proposed a technique to recognize three states in stance phase of gait cycle. Walking assistive devices are used to help the elderly people walk or to monitor walking behavior of the disabled persons. For the effective assistance, they adopt an intelligent sensor system to understand user's current state in walking. There are three states in stance phase; Loading Response, Midstance, and Terminal Stance. We developed a foot pressure sensor using 24 FSRs (Force Sensing/Sensitive Resistors). The foot pressure patterns were integrated through the interpolation of FSR cell array. The pressure patterns were processed to get the trajectories of COM (Center of Mass). Using the trajectories of COM of foot pressure, we can recognize the three states of stance phase. The experimental results show the effective recognition of stance phase and the possibility of usage on the walking assistive device for better control and/or foot pressure monitoring.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.2
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• pp.215-222
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• 2016

Research on gait recognition and its use is actively underway. This study suggests a method to recognize abnormal gaits of pedestrians. The purposes of the existing research to recognize normal steps are to measure physical activities and to validate people by their walks, but the purpose to recognize abnormal steps in this study is to insure the safe life of pedestrians. There are situations in which pedestrians are unaware of themselves vulnerable and can not ask for help. The purpose of this research is that even if pedestrians are unaware of themselves and there are no spontaneous requests for helps, it is intended for them to escape from dangers and difficulties by adopting the recent IOT technology. Hence, this study analyzes normal pace of pedestrians using the triaxial acceleration sensors, and takes ranges of their normal walking. And then, the steps of pedestrians are measured using the triaxial acceleration sensors, contrasted with their normal walking ranges, and determine whether their steps are normal or not. When it is out of the state for normal paces, a method to determine as abnormal paces is suggested.

• Physical Therapy Rehabilitation Science
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• v.6 no.2
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• pp.90-95
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• 2017

Objective: The purpose of this study was to investigate the physical therapists' perception of the use of gait measures, the frequency of the gait measures used, and also to identify the barriers that limit the use of these assessment tools. Design: Cross-sectional study. Methods: Physical therapists from the Seoul, Gyeonggi area from March to July 2016 were included in the study. Over the course of 18 weeks, a cross-sectional study was conducted with a self-report questionnaire. A total of 700 questionnaires were distributed and 350 questionnaires (50%) were collected, however with the exclusion of 140 questionnaires due to non-consent, a total of 210 questionnaires (30%) were analysed. Results: Out of the 10 standardized assessment tools, the therapists showed the highest perception for the timed up and go test (TUG [n=153, 72.9%]) and they also had high perception for the 10 meters walk test (10MWT [n=149, 71.0%]), and 6-minute walk test (6MWT [n=123, 58.6%]). The respondents answered that the TUG (n=116, 55.2%), 10MWT (n=100, 47.6%), and 6MWT (n=51, 24.3%) was used the most often. On the contrary, only four (1.9%) therapists have used the Chedoke-McMaster stroke assessment and the Rivermead Mobility Index. The lack of time was considered as the most important barrier to the use of assessment tools in clinical practice. Conclusions: Through this study, it has been shown that the domestic physical therapists used the TUG and the 10MWT mainly due to high recognition and evaluation status; however, the lack of time was the greatest impediment to the clinical application of the gait assessment tools.

• 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 the Korea Society of Computer and Information
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• v.17 no.8
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• pp.107-114
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• 2012

We have developed a textile capacitive pressure sensor for smart gait analysis. The proposed system can convert sensor signal into step counts and pressure levels by different posture. To evaluate the performance of insole type textile capacitive sensor, we measured capacitance change by increment of weights from 10 kg to 100 kg with 10 kg increment using M1 class rectangular weights (four 20 kg weights and two 10 kg weights) which have ${\pm}10%$ tolerance. The result showed non-linearity characteristic of a general capacitive pressure sensor. The test was performed according to a test protocol for four different postures (sitting, standing, standing on a left leg and standing on a right leg) and different walking speeds (1 km/h and 4 km/h). Five healthy male subjects were participated in each test. As we expected, the pressure level was changed by pressure distribution according to posture. Also, developed textile pressure sensor showed higher recognition rate (average 98.06 %) than commercial pedometer at all walking speed. Therefore, the proposed step counts and posture monitoring system using conductive textile capacitive pressure sensor proved to be a reliable and useful tool for monitoring gait parameters.