• Annals of Rehabilitation Medicine
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• v.39 no.6
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• pp.897-904
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• 2015

Objective To investigate differences in plantar pressure distribution between a normal gait and unpredictable slip events to predict the initiation of the slipping process. Methods Eleven male participants were enrolled. Subjects walked onto a wooden tile, and two layers of oily vinyl sheet were placed on the expected spot of the 4th step to induce a slip. An insole pressure-measuring system was used to monitor plantar pressure distribution. This system measured plantar pressure in four regions (the toes, metatarsal head, arch, and heel) for three events: the step during normal gait; the recovered step, when the subject recovered from a slip; and the uncorrected, harmful slipped step. Four variables were analyzed: peak pressure (PP), contact time (CT), the pressure-time integral (PTI), and the instant of peak pressure (IPP). Results The plantar pressure pattern in the heel was unique, as compared with other parts of the sole. In the heel, PP, CT, and PTI values were high in slipped and recovered steps compared with normal steps. The IPP differed markedly among the three steps. The IPPs in the heel for the three events were, in descending order (from latest to earliest), slipped, recovered, and normal steps, whereas in the other regions the order was normal, recovered, and slipped steps. Finally, the metatarsal head-to-heel IPP ratios for the normal, recovered, and slipped steps were $6.1{\pm}2.9$, $3.1{\pm}3.0$, and $2.2{\pm}2.5$, respectively. Conclusion A distinctive plantar pressure pattern in the heel might be useful for early detection of a slip event to prevent slip-related injuries.

• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.496-504
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• 2023

This paper recognizes the motion intention of the wearer using a muscle stiffness sensor and proposes a control system for a wearable robot based on this. The proposed system recognizes the onset time of the motion using sensor data, determines the assistance mode, and provides assistive torque to the hip flexion/extension motion of the wearer through the generated reference trajectory according to the determined mode. The onset time of motion was detected using the CUSUM algorithm from the muscle stiffness sensor, and by comparing the detection results of the onset time with the EMG sensor and IMU, it verified its applicability as an input device for recognizing the intention of the wearer before motion. In addition, the stability of the proposed method was confirmed by comparing the results detected according to the walking speed of two subjects (1 male and 1 female). Based on these results, the assistance mode (gait assistance mode and muscle strengthening mode) was determined based on the detection results of onset time, and a reference trajectory was generated through cubic spline interpolation according to the determined assistance mode. And, the practicality of the proposed system was also confirmed by applying it to an actual wearable robot.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.5
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• pp.39-46
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• 2007

Research on human identification in ubiquitous environment has recently attracted a lot of attention. As one of those research, gait recognition is an efficient method of human identification using physical features of a walking person at a distance. In this paper, we present a human shape extraction and tracking for gait recognition using geodesic active contour models(GACMs) combined with mean shift algorithm The active contour models (ACMs) are very effective to deal with the non-rigid object because of its elastic property. However, they have the limitation that their performance is mainly dependent on the initial curve. To overcome this problem, we combine the mean shift algorithm with the traditional GACMs. The main idea is very simple. Before evolving using level set method, the initial curve in each frame is re-localized near the human region and is resized enough to include the targe region. This mechanism allows for reducing the number of iterations and for handling the large object motion. The proposed system is composed of human region detection and human shape tracking modules. In the human region detection module, the silhouette of a walking person is extracted by background subtraction and morphologic operation. Then human shape are correctly obtained by the GACMs with mean shift algorithm. In experimental results, the proposed method show that it is extracted and tracked efficiently accurate shape for gait recognition.

• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.207-214
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• 2010

The aim of this study was to investigate upper and lower limb muscle activity using EMG(electromyogram) sensors while walking and identify normal gait pattern using FSR(force sensing resistor) sensor. Fifteen college students participated in this study and their EMG and FSR signal were measured during stopping and walking trials. EMG signals from upper(pectoralis major and trapezius) and lower limbs(rectus femoris, biceps femoris, vastus medialis, vastus lateralis, semimembranosus, semitendinosus, soleus, peroneus longus, gastrocnemius medialis, and gastrocnemius lateralis) were obtained using the surface electrodes. FSR measured pressures on 8 areas of the sole of the foot during walking. EMG results showed that all muscle activities except for vastus lateralis and semimembranosus during walking had higher amplitudes than stopping. Additionally, muscle activities associated with stance and swing phase during walking were identified. Results on FSR showed that stance and swing phases were detected by FSR signals during a gait cycle. Eight gait phases－initial contact, loading response, mid stance, terminal stance, pre swing, initial swing, mid swing, and terminal swing- were classified.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.8 no.4
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• pp.259-265
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• 2014

The aims of this study are to investigate the walking intention detection of a rollator based on Infraed (IR) sensor measuring knee joint anterior displacement and leg muscle activities. We used Active Walker attached IR sensor to measure the knee joint anterior displacement and EMG signal of leg muscles(rectus femoris, biceps femoris, tibialis anterior, gastrocnemius) were taken by Delsys bagnli-8ch. Subjects were eight healthy males(age $23.7{\pm}0.5years$, height $175.4{\pm}2.3cm$, weight $70.6{\pm}5.6kg$) and they were involved in experiments which had been proceeded 30 minutes a week, during 3 weeks. This system indicates that the knee joint anterior displacement had the distinction increases according to the gait slope and velocity. We showed the increase of the femoral muscle activities along the anterior tilt and the increase of the crural muscle activities along the posterior tilt.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.1-13
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• 2024

This study aims to provide a customized dynamic kiosk screen that considers user characteristics to cope with changes caused by increased use of kiosks. In order to optimize the screen composition according to the characteristics of the digital vulnerable group such as the visually impaired, the elderly, children, and wheelchair users, etc., users are classified into nine categories based on real-time analysis of user characteristics (wheelchair use, visual impairment, age, etc.). The kiosk screen is dynamically adjusted according to the characteristics of the user to provide efficient services. This study shows that the system communication and operation were performed in the embedded environment, and the used object detection, gait recognition, and speech recognition technologies showed accuracy of 74%, 98.9%, and 96%, respectively. The proposed technology was verified for its effectiveness by implementing a prototype, and through this, this study showed the possibility of reducing the digital gap and providing user-friendly "barrier-free kiosk" services.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.10
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• pp.944-950
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• 2015

Recent studies on ankle-foot prostheses used for transtibial amputees have focused on the adaptation of the ankle angle of the prosthesis according to ground conditions. For adaptation to various ground conditions (e.g., incline, decline, and step conditions), ankle-foot prostheses should first recognize the ground conditions as well as the current human motion pattern. For this purpose, the ground reaction forces and orientation angle of the tibia provide fundamental information. The measurement of the orientation angle, however, creates a challenge in practice. Although various sensors, such as accelerometers and gyroscopes, can be utilized to measure the orientation angles of the prosthesis, none of these sensors can be solely used due to their intrinsic drawbacks. In this paper, a time-varying complementary filtering (TVCF) method is proposed to incorporate the measurements from an accelerometer and a gyroscope to obtain a precise orientation angle. The cut-off frequency of TVCF is adaptively determined according to the human gait phase detected by a fuzzy logic algorithm. The performance of the proposed method is verified through experiments.

• Journal of Biomedical Engineering Research
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• v.34 no.4
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• pp.182-188
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• 2013

This study aims to develop a practical algorithm which can detect freezing of gait(FOG) in patients with Parkinson's disease(PD). Eighteen PD patients($68.8{\pm}11.1yrs.$) participated in this study, and three($68.7{\pm}4.0yrs.$) of them showed FOG. We suggested two time-domain algorithms(with 1-axis or 3-axes acceleration signals) and compared them with the frequency-domain algorithm in the literature. We measured the acceleration of left foot with a 3-axis accelerometer inserted at the insole of a shoe. In the time-domain method, the root-mean-square(RMS) acceleration was calculated in a moving window of 4s and FOG was defined as the periods during which RMS accelerations located within FOG range. The parameters in each algorithm were optimized for each subject using the simulated annealing method. The sensitivity and specificity were same, i.e., $89{\pm}8%$ for the time-domain method with 1-axis acceleration and were $91{\pm}7%$ and $90{\pm}8%$ for the time-domain method with 3-axes acceleration, respectively. Both performances were better in the time-domain methods than in the frequency-domain method although the results were statistically insignificant. The amount of calculation in the time-domain method was much smaller than in the frequency-domain method. Therefore it is expected that the suggested time domain algorithm would be advantageous in the systematic implementation of FOG detection.

• KIISE Transactions on Computing Practices
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• v.22 no.3
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• pp.133-138
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• 2016

Motion detection algorithms based on difference image are classified into background subtraction and previous frame subtraction. 1) Background subtraction is a convenient and effective method for detecting foreground objects in a stationary background. However in real world scenarios, especially outdoors, this restriction, (i.e., stationary background) often turns out to be impractical since the background may not be stable. 2) Previous frame subtraction is a simple technique for detecting motion in an image. The difference between two frames depends upon the amount of motion that occurs from one frame to the next. Both these straightforward methods fail when the object moves very "slightly and slowly". In order to efficiently deal with the problem, in this paper we present an algorithm for motion detection that incorporates "reflected light area" and "difference image". This reflected light area is generated during the frame production process. It processes multiplex difference image and AND-arithmetic of bitwise. This process incorporates the accuracy of background subtraction and environmental adaptability of previous frame subtraction and reduces noise generation. Also, the performance of the proposed method is demonstrated by the performance assessment of each method using Gait database sample of CASIA.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.476-477
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• 2008

본 연구에서는 가속도 센서를 이용하여 보행패턴을 검출하고 가속도 센서의 출력 값을 무선으로 PC에 실시간으로 전달할 수 있는 휴대용 모듈을 개발하였다. PC에서는 휴대장치로부터 전송되는 데이터를 수집하여 운동패턴을 화면에 실시간으로 출력할 수 있게 하였다. 휴대 장치의 전력 소모를 최대한 줄이기 위해 무선 전송 부분은 zigbee 통신을 사용하였다. 착용자의 걸음걸이 패턴을 분석하기 위해 2축 가속도 센서를 사용하였으며 기본적인 보행수는 임계치를 사용하는 moving average 알고리즘을 이용하여 마이크로 콘트롤러에서 처리하였다.