• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.1070-1073
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• 2002

The biomechanical interaction between the stump and the prosthetic socket is critically important to achieve close-to-normal ambulation. Many investigators suggested that the pressure changes during gait of transfemoral amputees are closely related to the prosthetic alignment, the socket shape, the stump size, and the residual muscle activity. The effects of the prosthetic alignment, the socket shape, and the stump size on the interface pressure were investigated previously. However, there is no report how the residual muscle activities in the transfemoral stump affect the socket interface pressure characteristics during gait. Since designs of socket fur lower limb amputees need to consider the socket interface pressure characteristics, the interface pressure patterns by the residual muscle activities during gait should be investigated. In this study, myoelectric signals (MES) and socket interface pressure in residual limb of transfemoral amputees were measured during the stance and swing phases of gait. For the purpose, specially designed quadrilateral sockets that MES electrodes could be instrumented were fabricated. A total of two transfemoral amputees were participated in the experiments. The measured temporal MES amplitude and interface pressure in knee flexor (biceps femoris) and extensor (rectus femoris) had significant correlations (P ＜ 0.05). Based on the test results, It was suggested that the residual muscle activity of transfemoral amputees stump is an important factor affecting socket pressure changes during walk.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.1417-1418
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• 2013

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.152-158
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• 2005

Commercial lower limb prostheses or orthotics help patients achieve a normal life. However, patients who use such aids need prolonged training to achieve a normal gait, and their fatigability increases. To improve patient comfort, this study proposed a method of predicting gait angle using neural networks and EMG signals. Experimental results using our method show that the absolute average error of the estimated gait angles is $0.25^{\circ}$. This performance data used reference input from a controller for the lower limb orthotic or prosthesis controllers while the patients were walking.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.6 no.2
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• pp.43-48
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• 2012

In this paper, we present the method of gait phases detection using multi biomedical signals during normal gait. Electromyogram(EMG) signals, muscle of thigh angle measurement device and resistive sensors are used for experiments. We implemented a test targeting five adult male and identified the pattern of EMG signal of normal gait. For acquiring the EMG signal, subjects attached surface Ag/AgCl electrodes to quadriceps femoris, biceps femoris, tibialis anterior and gastrocnemius medialis. Resistance sensors are attached to the heel toe and soles of the each feet for measuring attachment state of between feet and ground. Infrared sensors are attached on the thigh and thigh angle measurement device has the range from flection 25 degrees to extension 20 degrees. The results of this paper, The stance and swing phase could be confirmed during the normal gait and be classified in detail the eight steps.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.3
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• pp.181-189
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• 2015

Powered prosthesis is used to assist walking of people with an amputated lower limb and/or weak leg strength. The accurate gait phase classification is indispensable in smooth movement control of the powered prosthesis. In previous gait phase classification using physical sensors, there is limitation that powered prosthesis should be simulated as same as the speed of training process. Therefore, we propose EMG signal based gait phase recognition method to classify stairs ascending and stairs descending into four steps without using physical sensors, respectively. RMS, VAR, MAV, SSC, ZC, WAMP features are extracted from EMG signal data and LDA(Linear Discriminant Analysis) classifier is used. In the training process, the AHRS sensor produces various ranges of walking steps according to the change of knee angles. The experimental results show that the average accuracies of the proposed method are about 85.6% in stairs ascending and 69.5% in stairs descending whereas those of preliminary studies are about 58.5% in stairs ascending and 35.3% in stairs descending. In addition, we can analyze the average recognition ratio of each gait step with respect to the individual muscle.

• 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.

• Physical Therapy Rehabilitation Science
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• v.3 no.1
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• pp.43-48
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• 2014

Objective: The purpose of this study was to examine the influences on gait features during mobile phone use while ramp walking. Design: Cross-sectional study. Methods: Thirty-three healthy adult subjects performed four walking conditions on an outside ramp with a 5 m length, 1.5 m width, and a $5^{\circ}$ angle. All participants were touch screen mobile phone users. Four walking conditions were used: 1) ramp ascent, 2) ramp descent, 3) texting during ramp ascent, and 4) texting during ramp descent. In conditions 3) and 4), subjects texted the words of "Aegukga"-the song of patriotism-while walking. Upon the signal of start, the subjects walked the ramp during texting. Gait parameters were measured at the length of 3 m excluding 1 m of the start and end of the total length. Each situation was repeated three times for each subject, and mean values were calculated. For gait examination, a gait analyzer was used (OptoGait). Results: Subjects ranged in age from 23 to 38 years (mean age, 27.73). Eighty-three percent of subjects in our study had experienced an accident during mobile phone use. Texting on a mobile phone while walking significantly decreased ramp gait, speed, cadence, stride length, step length, and single support (p<0.05) and significantly increased stride time, step time, gait cycle, and double support (p<0.05). There was a significant difference in cadence, step length, stride time, step time, and single support during ramp ascent and descent (p<0.05). Conclusions: Texting on a mobile phone while walking significantly decreased gait quality.

• Journal of Information Processing Systems
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• v.9 no.2
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• pp.333-348
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• 2013

Mobile authentication/identification has grown into a priority issue nowadays because of its existing outdated mechanisms, such as PINs or passwords. In this paper, we introduce gait recognition by using a mobile accelerometer as not only effective but also as an implicit identification model. Unlike previous works, the gait recognition only performs well with a particular mobile specification (e.g., a fixed sampling rate). Our work focuses on constructing a unique adaptive mechanism that could be independently deployed with the specification of mobile devices. To do this, the impact of the sampling rate on the preprocessing steps, such as noise elimination, data segmentation, and feature extraction, is examined in depth. Moreover, the degrees of agreement between the gait features that were extracted from two different mobiles, including both the Average Error Rate (AER) and Intra-class Correlation Coefficients (ICC), are assessed to evaluate the possibility of constructing a device-independent mechanism. We achieved the classification accuracy approximately $91.33{\pm}0.67%$ for both devices, which showed that it is feasible and reliable to construct adaptive cross-device gait recognition on a mobile phone.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.7
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• pp.730-736
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• 2012

Latest, many researchers do research on wearable robot. The purpose of the researches is very diverse, it will improve efficiency in the industry, taken to replace the many workers in the military field and taken to assist bodily functions run out by aging. However, there is no clear Differentiated strategy depending on the purpose for design and control of the wearable robot. Although a common purpose is to drive the robot by the sensor signal (intent signals), the optimization about the mechanism and control studies must be done according to the user's physical ability and purpose. In this study, the study's first phase for the development of wearable robotic gait rehabilitation, gait characteristics were analyzed elders and hemiplegia patients using a 3D gait analysis system (VICON512). As a result, asymmetric gait characteristics of the hemiplegia patients were found compared with the normal elderly.