• Journal of the Korean Society for Precision Engineering
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• v.22 no.3 s.168
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• pp.170-178
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• 2005

The purpose of this study is to develop a gait-event detection system, which is necessary for the cycle-to-cycle FES control of locomotion. Proposed gait event detection system consists of a signal measurement part and gait event detection part. The signal measurement was composed of the sensors and the LabVIEW program for the data acquisition and synchronization of the sensor signals. We also used a video camera and a motion capture system to get the reference gait events. Machine learning technique with ANN (artificial neural network) was adopted for automatic detection of gait events. 2 cycles of reference gait events were used as the teacher signals for ANN training and the remnants ($2\sim5$ cycles) were used fur the evaluation of the performance in gait-event detection. 14 combinations of sensor signals were used in the training and evaluation of ANN to examine the relationship between the number of sensors and the gait-event detection performance. The best combinations with minimum errors of event-detection time were 1) goniometer, foot-switch and 2) goniometer, foot-switch, accelerometer x(anterior-posterior) component. It is expected that the result of this study will be useful in the design of cycle-to-cycle FES controller.

• Journal of Korea Multimedia Society
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• v.21 no.3
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• pp.333-341
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• 2018

In this paper, we propose an algorithm to automatically detect gait's joints. The proposed method classifies gait's types into front gait and flank gait so as to automatically detect gait's joints. And then according to classified types, the proposed applies joint extracting algorithm to input images. Firstly, we split input images into foreground image using difference images of Hue and gray-scale image of input and background one and extract gait's object. The proposed method classifies gaits into front gait and flank gait using ratio of Face's width to torso's width. Then classified gait's type, joints are detected 10 at front gait and detected 7~8 at flank gait. The proposed method is applied to the camera's input and the result shows that the proposed method automatically extracts joints.

• Journal of Biomedical Engineering Research
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• v.26 no.3
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• pp.145-150
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• 2005

A new gait detection system using both FSR (force sensing resistor) sensors and a gyrosensor was developed to detect various gait patterns. FSR sensors were put in self-designed shoe insoles and a gyrosensor was attached to the heel of a shoe. An algorithm was also developed to determine eight different gait transitions during four gait phases: heel-strike, foot-flat, heel-off and swing. The developed system was evaluated from nine heathy mans and twelve hemiplegic patients. Healthy volunteers were asked to walk in various gait patterns: level walking, fore-foot walking and stair walking. Only the level walking was performed in hemiplegic patients. The gait detection system was compared with a optical motion analysis system and the outputs of the FSR sensors. In healthy subjects, the developed system detected successfully more than $99\%$ for both level walking and fore-foot walking. For stair walking, the successful detection rate of the system was above$97\%$. In hemiplegic patients, the developed system detected approximately 98% of gait transitions. The developed gait phase detection system will be helpful not only to determine pathological gait phases but also to apply prosthetics, orthotics and functional electrical stimulation for patients with various gait disorders.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.10
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• pp.196-203
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• 2004

In this study, a new gait phase detection system using both FSR(Force Sensing Resister) sensors and a gyrosensor was developed to detect various gait patterns. FSR sensors were put in self-designed shoe insoles and a gyrosensor was attached to the posterior aspect of a shoe. An algorithm was also developed to determine eight different gait transitions among four gait phases: heel-strike, foot-flat, heel-off and swing. The developed system was compared with the conventional gait phase detection system using only FSR sensors in various gait experiments such as level walking, fore-foot walking and stair walking. In fore-foot walking and stair walking, the developed system showed much better accuracy and reliability to detect gait phases. The developed gait phase detection system using both FSR sensors and a gyrosensor will be helpful not only to determine pathological gait phases but to apply prosthetics, orthotics and functional electrical stimulation to patients with gait disorders.

• ETRI Journal
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• v.42 no.1
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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.

• Journal of Sensor Science and Technology
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• v.18 no.6
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• pp.449-455
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• 2009

In this paper, we developed a digital gait analyzer using the triaxial accelerometer(TA). An approach for normal gait detection employing decay slope peak detection(DSPD) algorithm was presented. The TA was attached to the center of the waist of a subject. The subject walked a bare floor at 60, 92 and 120 steps/minute. We analyzed vertical axis acceleration signal for gait detection. At 60, 92, 120 steps/minute walking, detection accuracy of gait events were over 99 % accuracy.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.5
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• pp.248-253
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• 2006

The purpose of this study is to develop a portable gait-event detection system which is necessary for the cycle-to-cycle FES(functional electrical stimulation) control of locomotion. To make the system portable, we made following modifications in the gait signal measurement system. That is, 1) to make the system wireless using Bluetooth communication, 2) to make the system small-sized and battery-powered by using low power consumption ${\mu}$ P(ATmega8535L). The gait-events were analyzed in off-line at the main computer using ANN(Artificial Neural Network). The Proposed system showed no mis-detection of the gait-events of normal subject and hemiplegia subjects. The performance of the system was better than the previous wired-system.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.2
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• pp.145-152
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• 2010

Gait phase detection is important for evaluating the recovery of gait ability in patients with paralysis, and for determining the stimulation timing in FES walking. In this study, three different motion sensors(tilt sensor, gyrosensor and accelerometer) were used to detect gait events(heel strike, HS; toe off, TO) and they were compared one another to determine the most applicable sensor for gait phase detection. Motion sensors were attached on the shank and heel of subjects. Gait phases determined by the characteristics of each sensor's signal were compared with those from FVA. Gait phase detections using three different motion sensors were valid, since they all have reliabilities more than 95%, when compared with FVA. HS and TO were determined by both FVA and motion sensor signals, and the accuracy of detecting HS and TO with motion sensors were assessed by the time differences between FVA and motion sensors. Results show of that the tilt sensor and the gyrosensor could detect gait phase more accurately in normal subjects. Vertical acceleration from the accelerometer could detect HS most accurately in hemiplegic patient group A. The gyrosensor could detect HS and TO most accurately in hemiplegic patient group A and B. Valid error ranges of HS and TO were determined by 3.9 % and 13.6 % in normal subjects, respectively. The detection of TO from all sensor signals was valid in both patient group A and B. However, the vertical acceleration detected HS validly in patient group A and the gyrosensor detected HS validly in patient group B. We could determine the most applicable motion sensors to detect gait phases in hemiplegic patients. However, since hemiplegic patients have much different gait patterns one another, further experimental studies using various simple motion sensors would be required to determine gait events in pathologic gaits.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.84-92
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• 2007

The gait recognition is presented for human identification from a sequence of noisy silhouettes segmented from video by capturing at a distance. The proposed gait recognition algorithm gives better performance than the baseline algorithm because of segmentation of the object by using multiple modules; i) motion detection, ii) object region detection, iii) head detection, and iv) active shape models, which solve the baseline algorithm#s problems to make background, to remove shadow, and to be better recognition rates. For the experiment, we used the HumanID Gait Challenge data set, which is the largest gait benchmarking data set with 122 objects, For realistic simulation we use various values for the following parameters; i) viewpoint, ii) shoe, iii) surface, iv) carrying condition, and v) time.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.542-544
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• 2005

This paper describes the detection of spatio-temporal parameter using an accelerometer and footswitches to evaluate a symmetry and balance of hemiplegic patients. We detected gait data using a 3-axis accelerometer that mounted between L3 and IA intervertebral area and footswitches made by FSR-Sensor attached insole. To minimize the error of the gait parameters to be detected incorrectly in case of using only accelerometer, we enhancement the performance of detection by measuring an accelerometer and foots witches data at the same time. So, it was possible to detect more accurate gait parameters. As a result, we can confirm the symmetry and balance of hemiplegic patients. In the future. these results could be used to evaluate the walking ability in hemiplegic patients in clinical pratice.