• 한국정밀공학회지
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• 제22권3호
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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.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권5호
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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.

• 한국멀티미디어학회논문지
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• 제11권6호
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• pp.787-795
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• 2008

Human recognition on camera is an interesting topic in computer vision. While fingerprint and face recognition have been become common, gait is considered as a new biometric feature for distance recognition. In this paper, we propose a gait recognition algorithm based on the knee angle, 2 feet distance, walking velocity and head direction of a person who appear in camera view on one gait cycle. The background subtraction method firstly use for binary moving object extraction and then base on it we continue detect the leg region, head region and get gait features (leg angle, leg swing amplitude). Another feature, walking speed, also can be detected after a gait cycle finished. And then, we compute the errors between calculated features and stored features for recognition. This method gives good results when we performed testing using indoor and outdoor landscape in both lateral, oblique view.

• Journal of Information Processing Systems
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• 제15권4호
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• pp.957-966
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• 2019

This paper studies a novel approach to natural gait cycles based gait recognition via kernel Fisher discriminant analysis (KFDA), which can effectively calculate the features from gait sequences and accelerate the recognition process. The proposed approach firstly extracts the gait silhouettes through moving object detection and segmentation from each gait videos. Secondly, gait energy images (GEIs) are calculated for each gait videos, and used as gait features. Thirdly, KFDA method is used to refine the extracted gait features, and low-dimensional feature vectors for each gait videos can be got. The last is the nearest neighbor classifier is applied to classify. The proposed method is evaluated on the CASIA and USF gait databases, and the results show that our proposed algorithm can get better recognition effect than other existing algorithms.

• 대한의용생체공학회:의공학회지
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• 제30권6호
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• pp.516-520
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• 2009

The purpose of this study is to determine optimal filtering condition and threshold for the detection of gait-cycles for various walkway slopes as well as gait velocities. Ten young healthy subjects with accelerometer system on thigh and ankle walked on a treadmill at 9 conditions (three speeds and three slopes) for 5 minutes. Two direction signals, i.e. anterior-posterior (AP) and superior-inferior (SI) directions, of each sensor (four sensor orientations) were used to detect specific events of gait cycle. Variation of the threshold (from -1G to 1G) and lowpass cutoff frequency (fc) were applied to the event detection and their performance was evaluated according to the error index (EI), which was defined as the combination of the accuracy and false positive rate. Optimal fc and threshold were determined for each slope in terms of the EI. The optimal fc, threshold and their corresponding EI depended much on the walkway slope so that their coefficients of variation (CV) ranged 19~120%. When all data for 3 slopes were used in the identification of optimal conditions for each sensor, the best error indices for all sensor orientations were comparable ranging 1.43~1.76%, but the optimal fc and threshold depended much on the sensor position. The result indicates that the gait-cycle detection robust to walkway slope is possible by threshold method with well-defined filtering condition and threshold.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2006년도 추계학술대회 논문집
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• pp.547-548
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• 2006

• 대한인간공학회지
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• 제26권2호
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• pp.131-136
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• 2007

In this study, we developed an active ankle-foot orthosis(AAFO) which can control dorsi/ plantar flexion of the ankle joint to prevent foot drop and toe drag during walking. 3D gait analyses were performed on five healthy subjects under three different gait conditions: the normal gait without AFO, the SAFO gait with the conventional plastic AFO, and the AAFO gait with the developed AFO. As a result, the developed AAFO preeminently induced the normal gait compared to the SAFO. Additionally, AAFO prevented foot drop by proper plantarflexion during loading response and provided enough plantarflexion moment as a driving force to walk forward by sufficient push-off during pre-swing. AAFO also could prevent toe drag by proper dorsiflexion during swing phase. These results indicate that the developed AAFO may have more clinical benefits to treat foot drop and toe drag, compared to conventional AFOs, and also may be useful in patients with other orthotic devices.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 학술대회 논문집 정보 및 제어부문
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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.

• 감성과학
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• 제13권1호
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• pp.207-214
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• 2010

본 연구에서는 근전도 신호를 활용하여 정상인의 보행과 관련된 상지와 하지 근육의 신호를 확인하고 저항센서를 이용하여 정상적인 보행 패턴을 확인하였다. 대학생 15명을 대상으로 정지해 있을 때와 평지를 보행할 때, 상지의 4부위(대흉근과 승모근)와 하지의 10부위(대퇴직근, 대퇴이두근, 내측광근, 외측광근, 반막양근, 반건양근, 가자미근, 장비골근, 내비복근과 외비복근)에 전극을 부착하여 근전도를 측정하였다. 저항센서는 양측 발바닥의 8부위에 센서를 부착하여 보행시 발에 가해지는 압력을 측정하였다. 그 결과, 근전도 신호는 정지상태에 비하여 보행 시에 허벅지의 외측광근과 반건양근을 제외하고 모든 근육에서 유의하게 높은 진폭을 가졌다. 또한 보행주기의 두 단계인 입각기와 유각기와 관련된 근육을 확인하였다. 저항 센서의 신호 분석 결과, 평균 보폭 주기 동안 크게 입각기와 유각기의 두 주기와 세부적으로 여덟 단계 - 초기 접지기, 하중 반응기, 중간 입각기, 말기 입각기, 전 유각기, 초기 유각기, 중간 유각기, 말기 유각기 - 의 보행 주기를 확인할 수 있었다.