• Smart Media Journal
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• v.9 no.4
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• pp.97-101
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• 2020

Using passive electric field sensor which operates in non-contact mode, we can measure the electric potential induced from the change of electric charges on a sensor caused by the movement of human body or hands. In this study, we propose a new method, which utilizes PLN induced to the sensor around the moving object, to detect one's hand movement and extract gesture frames from the detected signals. Signals from the EPS sensors include a large amount of power line noise usually existing in the places such as rooms or buildings. Using the fact that the PLN is shielded in part by human access to the sensor, signals caused by motion or hand movement are detected. PLN consists mainly of signals with frequency of 60 Hz and its harmonics. In our proposed method, signals only 120 Hz component in frequency domain are chosen selectively and exclusively utilized for detection of hand movement. We use FFT to measure a spectral-separated frequency signal. The signals obtained from sensors in this way are continued to be compared with the threshold preset in advance. Once motion signals are detected passing throng the threshold, we determine the motion frame based on period between the first threshold passing time and the last one. The motion detection rate of our proposed method was about 90% while the correct frame extraction rate was about 85%. The method like our method, which use PLN signal in order to extract useful data about motion movement from non-contact mode EPS sensors, has been rarely reported or published in recent. This research results can be expected to be useful especially in circumstance of having surrounding PLN.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.19-23
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• 2024

Patients with amputations of both hands need prosthetic hands that serve both cosmetic and functional purposes, and research on prosthetic hands using electromyography of remaining muscles is active, but there is still the problem of high cost. In this study, an artificial prosthetic hand was manufactured and its performance was evaluated using low-cost parts and software such as a surface electromyography sensor, machine learning software Edge Impulse, Arduino Nano 33 BLE, and 3D printing. Using signals acquired with surface electromyography sensors and subjected to digital signal processing through Edge Impulse, the flexing movement signals of each finger were transmitted to the fingers of the prosthetic hand model through training to determine the type of finger movement using machine learning. When the digital signal processing conditions were set to a notch filter of 60 Hz, a bandpass filter of 10-300 Hz, and a sampling frequency of 1,000 Hz, the accuracy of machine learning was the highest at 82.1%. The possibility of being confused between each finger flexion movement was highest for the ring finger, with a 44.7% chance of being confused with the movement of the index finger. More research is needed to successfully develop a low-cost prosthetic hand.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.1
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• pp.26-33
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• 2009

The directions of the road are divided into two, the right-hand side and left-hand side of the road, by the convention and specific native method in the world. This paper deals with the characteristics and behaviors of drivers who are accustomed to driving on right-hand side of the road, drive with a handle on the left-hand side, and comparing with left-hand side drivers. The driver's eye movements were measured by eye camera and questionnaires were used for measuring subjective evaluation such as driving mental workload. The experimental results indicated even if the experts who had much experience on right-hand side driving, they had lower driving skill than novice driver, accustomed to driving on left-hand side. In terms of mental workload, MCH rating scale and MNASA-TLX, the right-hand side drivers were in lower stress condition than the left-hand side drivers because of having much driving experience. However, they conducted a few mistakes by confusing the position of turn signal and windshield wiper because of their driving habit or traits and it lead to operation mistakes. These results can be applied effectively to develop the driving support information with changed environments.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.4
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• pp.303-309
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• 2010

We proposes a new approach for testing the self visual-acuity by using the KS standard optotype. The proposed system provides their hand-gesture recognition method for the convenient response of subjects in the visual acuity measurement. Also, this system can measure a visual-acuity that excludes the examiner's subjective judgement or the subject's memorized guess, because of presenting a random optotype automatically by computer without a examiner. Especially, Our system guarantees the reliability by using the KS standard optotype and its presentation(KS P ISO 8596), which is defined by the Korea Standards Association in 2006. And the database management function of our system can provide the visual-acuity data to the EMR client easily. As a result, Our system shows the 98% consistency in the limit of the ${\pm}1$ visual-acuity level error by comparing the visual-acuity chart test.

• The Journal of the Acoustical Society of Korea
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• v.25 no.8
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• pp.362-369
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• 2006

In this paper, we improve a Physical modeling of sanio gayageum and synthesize sounds by right-hand playing styles. Parameters of loop filter are estimated from decay region of recorded sound. That results in improved accuracy and reduction of computational costs. Body is implemented to a resonator which has characteristics of main resonances extracted from impulse response. Residual signal with main resonances removed is used as excitation signal of proposed model. Amok alter is approximated to frequency response of amok and is implemented to the 15th order all-role digital filter. Beating (by middle and index finger) among the right-hand playing styles is represented by feedforward comb filter. Parameters of this filter are extracted from recorded sound. The synthesized sounds using improved physical model of sanjo gayageum. plucking and beating, are pretty similar to original sounds.

• Structural Engineering and Mechanics
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• v.39 no.6
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• pp.813-831
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• 2011

This paper presents an efficient version of Hilbert-Huang transform for nonlinear non-stationary systems analyses. An ensemble empirical mode decomposition (EEMD) is introduced to alleviate the problem of mode mixing between intrinsic mode functions (IMFs) decomposed by EMD. Yet the problem has not been fully resolved when a signal of a similar scale resides in different IMF components. Instead of using a trial and error method to select the "best" outcome generated by EEMD, a hybrid algorithm based on EEMD and EMD is proposed for multi-mode signal processing. The developed approach comprises the steps from a bandpass filter design for regrouping modes of the IMFs obtained from EEMD, to the mode extraction using EMD, and to the assessment of each mode in the marginal spectrum. A simulated two-mode signal is tested to demonstrate the efficiency and robustness of the approach, showing average relative errors all equal to 1.46% for various noise levels added to the signal. The developed approach is also applied to a real bridge structure, showing more reliable results than the pure EMD. Discussions on the mode determination are offered to explain the connection between modegrouping form on the one hand, and mode-grouping performance on the other.

• Journal of Environmental Science International
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• v.24 no.3
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• pp.353-358
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• 2015

In this study, we analyzed two types of echolocation calls used by the parti-coloured bat, Vespertilio sinensis. Bats were captured in the Naejangsan National Park in October 2013. Call sounds of hand-released bats were recorded at the location of capture within the National Park. We analyzed pulse duration (PD), pulse interval (PI), peak frequency (PF), maximum frequency ($F_{MAX}$), minimum frequency ($F_{MIN}$), and bandwidth (BW). V. sinensis emitted the different types of the echolocation calls depending on the surrounding environment. Frequency modulated-constant frequency (FM-CF) signal of audible range was emitted when they flew in the uncluttered space over the canopy. However, when flying in the cluttered space below the canopy, they only emitted FM signal. FM-CF signal is in the audible range (e.g., low frequency), and FM signal has a harmonic broadband frequency range of two. There were significant differences in PD, PI, PF, FMAX, FMIN, and BW between the calls emitted over and below the canopy. Considering the functional characteristics of FM and CF signals, we conclude that the foraging activity of V. sinensis was observed below the canopy, and recommend the use of FM signal and broadband as echolocation signals.

• ETRI Journal
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• v.17 no.4
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• pp.25-35
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• 1996

This paper derives the symbol error probability for quadrature amplitude modulation(QAM) with L-fold space diversity in Rayleigh fading channels. Two combining techniques, maximal ratio combining(MRC) and selection combining(SC), are considered. The formula for MRC space diversity is obtained by averaging the symbol error probability of M-ary QAM in an additive white Gaussian noise(AWGN) channel over a chi-square distribution with 2L degrees of freedom. The obtained formula overcomes the limitations of the earlier work, which has been limited only to deriving the symbol error rate(SER) of QAM with two branch MRC space diversity. The formula for SC space diversity is obtained by averaging the symbol error probability of M-ary QAM in an AWGN channel over the distribution of the maximum signal-to noise ratio among all of the diversity channels for SC space diversity has been reported yet. Analytical results show that the probability of error decreases with the order of diversity gain per additional branch decreases as the number of branches becomes larger. On the other hand, the performance of 16 QAM with MRC becomes much better than that of SC as the number of branches becomes larger. By giving the order of diversity, L, and the number of signal points, M, we have been able to obtain the SER performance of QAM with general space diversity. These results can be used to determine the order of diversity to achieve the desired SER in land mobile communication system employing QAM modulation.

• Smart Media Journal
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• v.6 no.2
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• pp.26-32
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• 2017

In this paper, we implement a signal-detecting electric circuit based LED lighting control system which is essential in NUI technology using EPIC converting surrounding earth electric field disturbance signals to electric potential signals. We used signal-detecting electric circuits which was developed to extract individual signal for each EPIC sensor while conventional EPIC-based development equipments provide limited forms of signals. The signals extracted from our developed circuit contributed to better performance as well as flexiblity in processes of feature extracting stage and pattern recognition stage. We designed a system which can control the brightness and on/off of LED lights with four hand gestures in order to justify its applicability to real application systems. We obtained faster pattern classification speed not only by developing an instruction system, but also by using interface control signals.

• The Journal of Korea Robotics Society
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• v.3 no.2
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• pp.81-90
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• 2008

While increasing demand of the service for the disabled and the elderly people, assistive technologies have been developed rapidly. The natural signal of human such as voice or gesture has been applied to the system for assisting the disabled and the elderly people. As an example of such kind of human robot interface, the Soft Remote Control System has been developed by HWRS-ERC in $KAIST^[1]$. This system is a vision-based hand gesture recognition system for controlling home appliances such as television, lamp and curtain. One of the most important technologies of the system is the hand gesture recognition algorithm. The frequently occurred problems which lower the recognition rate of hand gesture are inter-person variation and intra-person variation. Intra-person variation can be handled by inducing fuzzy concept. In this paper, we propose multivariate fuzzy decision tree(MFDT) learning and classification algorithm for hand motion recognition. To recognize hand gesture of a new user, the most proper recognition model among several well trained models is selected using model selection algorithm and incrementally adapted to the user's hand gesture. For the general performance of MFDT as a classifier, we show classification rate using the benchmark data of the UCI repository. For the performance of hand gesture recognition, we tested using hand gesture data which is collected from 10 people for 15 days. The experimental results show that the classification and user adaptation performance of proposed algorithm is better than general fuzzy decision tree.