• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.6
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• pp.1405-1417
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• 2017

Researches that apply the acceleration signal due to user's gait measured at the wearable device to the authentication technology are being introduced recently. The gait acceleration signal based authentication technologies introduced so far have assumed that an attacker can obtain a user's gait acceleration signal only by attaching accelerometer directly to user's body. And the practical attack method for gait acceleration signal based authentication technology is mimic attack and it uses a person whose physical condition is similar to the victim or identifies the gait characteristics through the video of the gait of the victim. However, mimic attack is not effective and attack success rate is also very low, so it is not considered a serious threat. In this paper, we propose Video Gait attack as a new attack method for gait acceleration signal based authentication technology. It is possible to know the position of the wearable device from the user's gait video signal and generate a signal that is very similar to the accelerometer's signal using dynamic equation. We compare the user's gait acceleration signal and the signal that is calculated from video of user's gait and dynamic equation with experiment data collected from eight subjects.

• PNF and Movement
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• v.20 no.1
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• pp.135-145
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• 2022

Purpose: The purpose of this study was to investigate the association between cognitive and motor inhibition by comparing muscle activity and ground reaction force during unplanned gait termination according to reaction time measured through the stop-signal task. Methods: Sixteen young adults performed a stop-signal task and an unplanned gait termination separately. The subjects were divided into fast and slow groups based on their stop-signal reaction time (SSRT), as measured by the stop-signal task. Electromyography (EMG) and ground reaction force (GRF) were compared between the groups during unplanned gait termination. The data for gait termination were divided into three phases (Phase 1 to 3). The Mann-Whitney U test was used to compare spatiotemporal gait parameters and EMG and GRF data between groups. Results: The slow group had significantly higher activity of the tibialis anterior in Phase 2 and Phase 3 than the fast group (p <0.05). In Phase 1, the fast group had significantly shorter time to peak amplitude (TPA) of the soleus than the slow group (p <0.05). In Phase 2, the TPA of the tibialis anterior was significantly lower in the fast group than the slow group (p <0.05). In Phase 3, there was no significant difference in the GRF between the two groups (p >0.05). There were no significant difference between the two groups in the spatiotemporal gait parameters (p >0.05). Conclusion: Compared to the slow group, the fast group with cognitive inhibition suppressed muscle activity for unplanned gait termination. The association between SSRT and unplanned gait termination shows that a participant's ability to suppress an incipient finger response is relevant to their ability to construct a corrective gait pattern in a choice-demanding environment.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.115-115
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• 2000

A gait pattern classification method using electromyography(EMG) signal is presented. The gait pattern with four stages such as stance, heel-off, swing and heel-strike is analyzed and classified using feature parameters such as zero-crossing, integral absolute value and variance of the EMG signal. The EMG signal from Tibialis Anterior and Gastrocnemius muscles was obtained using the surface electrodes, and low-pass filtered at 10kHz. The filtered analog signal was sampled at every 0.5msec and converted to digital signal with 12-bit resolution. The obtained data is analyzed and classified in terms of feature parameters. Analysis results are given to show that the gait patterns classified by the proposed method are feasible.

• 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 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.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.2
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• pp.163-168
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• 2009

In this study, we would be developed the fuzzy controlled PGO that controlled the flexion and the extension of each PGO's hip joint using the bio-signal and FSR sensor. The PGO driving system is to couple the right and left sides of the orthosis by specially designed hip joints and pelvic section. This driving system consists of the orthosis, sensor, control system. An air supply system of muscle is composed of an air compressor, 2-way solenoid valve (MAC, USA), accumulator, pressure sensor. Role of this system provide air muscle with the compressed air at hip joint constantly. According to output signal of EMG sensor and foot sensor, air muscles and assists the flexion of hip joint during PGO gait. As a results, the maximum hip flexion angles of RGO's gait and PGO's gait were about $16^{\circ}\;and\;57^{\circ}$ respectively. The maximum angle of flexion/extention in hip joint of the patients during RGO's gait are smaller than normal gait, because of the step length of them shoes a little bit. But maximum angle of flexion/extention in hip joint of the patients during PGO's gait are larger than normal gait.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.7
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• pp.462-467
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• 2005

In this paper, we analyzed normal gait acceleration signal by time series analysis methods. Accelerations were measured during walking using a biaxial accelerometer. Acceleration data were acquired from normal subjects(23 men and one woman) walking on a level corridor of 20m in length with three different walking speeds. Acceleration signals were measured at a sampling frequency of 60Hz from a biaxial accelerometer mounted between L3 and L4 intervertebral area. Each step signal was analyzed using Box-Jenkins method. Most of the differenced normal step signals were modeled to AR(3) and the model didn't show difference for model's orders and coefficients with walking speed. But, tile model showed difference with acceleration signal direction - vertical and lateral. The above results suggested the proposed model could be applied to unit analysis.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.11
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• pp.1634-1640
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• 2017

The study aims to distinguish hemiplegic gait and normal gait using simple wearable device and classification algorithm. Thus, we developed a wearable system equipped three axis accelerometer and three axis gyroscope. The developed wearable system was verified by clinical experiment. In experiment, twenty one normal subjects and twenty one patients undergoing stroke treatment were participated. Based on the measured inertial signal, a random forest algorithm was used to classify hemiplegic gait. Four-fold cross validation was applied to ensure the reliability of the results. To select optimal attributes, we applied the forward search algorithm with 10 times of repetition, then selected five most frequently attributes were chosen as a final attribute. The results of this study showed that 95.2% of accuracy in hemiplegic gait and normal gait classification and 77.4% of accuracy in hemiplegic-side and normal gait classification.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1272-1277
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• 2005

In this study, we wolud be developed the fuzzy controlled PGO that controlled the flexion and the extension of each PGO's joint using the bio-signal and FSR sensor. The PGO driving system is to couple the right and left sides of the orthosis by specially designed hip joints and pelvic section. This driving system consists of the orthosis, sensor, control system. An air supply system of muscle is composed of an air compressor, 2-way solenoid valve(MAC, USA), accumulator, pressure sensor. Role of this system provide air muscle with the compressed air at hip joint constantly. According to output signal of EMG sensor and foot sensor, air muscles and assists the flexion of hip joint during PGO gait.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.7 no.2
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• pp.19-26
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• 2013

This paper propose a surface EMG signal based gait phase recognition method that selects features and channels adaptively. The proposed method can be used to control powered artificial prosthetic for lower limb amputees and can reduce overhead in real-time pattern recognition by selecting adaptive channels and features in an embedded device. The method can enhance the classification accuracy by adaptively selecting channels and features based on sensitivity and specificity of each subject because EMG signal patterns may vary according to subject's locomotion convention. In the experiments, we found that the muscles with highest recognition rate are different between human subjects. The results also show that the average accuracy of the proposed method is about 91% whereas those of existing methods using all channels and/or features is about 50%. Therefore we assure that sEMG signal based gait phase recognition using small number of adaptive muscles and corresponding features can be applied to control powered artificial prosthetic for lower limb amputees.