• Biomedical Engineering Letters
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• v.8 no.4
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• pp.345-353
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• 2018

This study suggested a new EMG-signal-based evaluation method for knee rehabilitation that provides not only fragmentary information like muscle power but also in-depth information like muscle fatigue in the field of rehabilitation which it has not been applied to. In our experiment, nine healthy subjects performed straight leg raise exercises which are widely performed for knee rehabilitation. During the exercises, we recorded the joint angle of the leg and EMG signals from four prime movers of the leg: rectus femoris (RFM), vastus lateralis, vastus medialis, and biceps femoris (BFLH). We extracted two parameters to estimate muscle fatigue from the EMG signals, the zero-crossing rate (ZCR) and amplitude of muscle tension (AMT) that can quantitatively assess muscle fatigue from EMG signals. We found a decrease in the ZCR for the RFM and the BFLH in the muscle fatigue condition for most of the subjects. Also, we found increases in the AMT for the RFM and the BFLH. Based on the results, we quantitatively confirmed that in the state of muscle fatigue, the ZCR shows a decreasing trend whereas the AMT shows an increasing trend. Our results show that both the ZCR and AMT are useful parameters for characterizing the EMG signals in the muscle fatigue condition. In addition, our proposed methods are expected to be useful for developing a navigation system for knee rehabilitation exercises by evaluating the two parameters in two-dimensional parameter space.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.9-16
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• 2023

Speech can actively elicit feelings and attitudes by using words. It is important for researchers to identify the emotional content contained in speech signals as well as the sort of emotion that resulted from the speech that was made. In this study, we studied the emotion recognition system using a database in Arabic, especially in the Saudi dialect, the database is from a YouTube channel called Telfaz11, The four emotions that were examined were anger, happiness, sadness, and neutral. In our experiments, we extracted features from audio signals, such as Mel Frequency Cepstral Coefficient (MFCC) and Zero-Crossing Rate (ZCR), then we classified emotions using many classification algorithms such as machine learning algorithms (Support Vector Machine (SVM) and K-Nearest Neighbor (KNN)) and deep learning algorithms such as (Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM)). Our Experiments showed that the MFCC feature extraction method and CNN model obtained the best accuracy result with 95%, proving the effectiveness of this classification system in recognizing Arabic spoken emotions.

• The Journal of the Acoustical Society of Korea
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• v.14 no.3
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• pp.61-70
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• 1995

In this paper, the PLP cepstrum which is close to human perceptual characteristics was extracted through the spread time area to get the temperal feature. Phonemes were recognized by artificial neural network similar to the learning method of human. The phoneme strings were matched by Markov models which well suited for sequence. Phoneme recognition for the continuous Korean speech had been done using speech blocks in which speech frames were gathered with unequal numbers. We parameterized the blocks using 7th order PLPs, PTP, zero crossing rate and energy, which neural network used as inputs. The 100 data composed of 10 Korean sentences which were taken from the speech two men pronounced five times for each sentence were used for the the recognition. As a result, maximum recognition rate of 94.4% was obtained. The sentence was recognized using Markov models generated by the phoneme strings recognized from earlier results the recognition for the 200 data which two men sounded 10 times for each sentence had been carried out. The sentence recognition rate of 92.5% was obtained.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.1-6
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• 2021

In the field of speech recognition, as the DNN is applied, the use of speech recognition is increasing, but the amount of calculation for parallel training needs to be larger than that of the conventional GMM, and if the amount of data is small, overfitting occurs. To solve this problem, we propose an efficient method for robust voice feature extraction and voice signal noise removal even when the amount of data is small. Speech feature extraction efficiently extracts speech energy by applying the difference in frame energy for speech and the zero-crossing ratio and level-crossing ratio that are affected by the speech signal. In addition, in order to remove noise, the noise of the speech signal is removed by removing the noise of the speech signal with an average predictive improved LMS filter with little loss of speech information while maintaining the intrinsic characteristics of speech in detection of the speech signal. The improved LMS filter uses a method of processing noise on the input speech signal by adjusting the active parameter threshold for the input signal. As a result of comparing the method proposed in this paper with the conventional frame energy method, it was confirmed that the error rate at the start point of speech is 7% and the error rate at the end point is improved by 11%.

• Journal of the Korea Society of Computer and Information
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• v.6 no.2
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• pp.8-13
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• 2001

We demonstrate a pulsed CO$_2$laser with long pulse duration of millisecond order in the low pressure less than 30 Torr. A new power supply for our laser system switches the voltage of AC power line(60㎐) directly. The power supply doesn't need elements such as a rectified bridge, energy-storage capacitors. and a current-limiting resistor in the discharge circuit. In order to control the laser output power, the pulse repetition rate is adjusted up to 60㎐ and the firing angle of SCR gate is varied from 30˚ to 150˚. A ZCS(Zero Crossing Switch) circuit and a PIC one-chip microprocessor are used to control the gate signal of SCR precisely. The maximum laser output is 23W at the total pressure of 18 Torr, the pulse repetition rate of 60㎐, and SCR gate firing angle of 90˚. In addition, the obtained laser pulse width is approximately 3㎳(FWHM)

• Journal of Korea Multimedia Society
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• v.7 no.9
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• pp.1233-1239
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• 2004

In a speech coding system using excitation source of voiced and unvoiced, it would be involved a distortion of speech qualify in case coexist with a voiced and an unvoiced consonants in a frame. This paper present a new multi-pulse coding method by using V/S/TSIUVC switching, individual pitch pulses and TSIUVC approximation-synthesis method in order to restrict a distortion of speech quality. The TSIUVC is extracted by using the zero crossing rate and individual pitch pulse. And the TSIUVC extraction rate was 91% for female voice and 96.2% for male voice respectively. The important thing is that the frequency information of 0.347kHz below and 2.813kHz above can be made with high quality synthesis waveform within TSIUVC. I evaluate the MPC use V/UV and the FBD-MPC use V/S/TSIUVC. As a result, I knew that synthesis speech of the FBD-MPC was better in speech quality than synthesis speech of the MPC.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.2
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• pp.121-128
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• 2008

Accurate voice activity detection have a great impact on performance of speech applications including speech recognition, speech coding, and speech communication. In this paper, we propose methods for voice activity detection that can adapt to various car noise situations during driving. Existing voice activity detection used various method such as time energy, frequency energy, zero crossing rate, and spectral entropy that have a weak point of rapid. decline performance in noisy environments. In this paper, the approach is based on existing spectral entropy for VAD that we propose voice activity detection method using MFB(Met-frequency filter banks) spectral entropy, gradient FFT(Fast Fourier Transform) spectral entropy. and gradient MFB spectral entropy. FFT multiplied by Mel-scale is MFB and Mel-scale is non linear scale when human sound perception reflects characteristic of speech. Proposed MFB spectral entropy method clearly improve the ability to discriminate between speech and non-speech for various in noisy car environments that achieves 93.21% accuracy as a result of experiments. Compared to the spectral entropy method, the proposed voice activity detection gives an average improvement in the correct detection rate of more than 3.2%.

• Journal of the Ergonomics Society of Korea
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• v.26 no.2
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• pp.157-165
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• 2007

The Work-Related Musculoskeletal Disorders (WMSDs) can be occurred by various factors such as repetition, forceful exertions and awkward postures. Especially, occurrences of the WMSDs on the waist and lower limb are reported in workplaces, demanded standing postures for a long time, in service and manufacturing industry. The static and standing postures without movement for a long time increase work loads to the lower limb and the waist. Accordingly, anti-fatigue mat or anti-fatigue insole is used as a preventing device of the WMSDs. However anti-fatigue mats are limited in space and movement. In this study, multi-elastic insoles are designed and shown the effects of the workload reduction for a long time under the standing work. The foot pressures and EMG (Electromyography) are measured at 0 hour and after 2 hours by 6 health students in their twenties. The 6 prototype insoles are designed with three elastic (Low, Medium and High). These insoles are compared with no insole (insole type 7) as control group. The EMG measurement was conducted to waist (erector spinae muscle), thigh (vastus lateralis muscle) and calf (gastrocnemius muscle). The foot pressure is analyzed by mean pressure value and the EMG analysis is investigated through MF (Median Frequency), MPF (Mean Power Frequency) and ZCR (Zero Crossing Rate). The results of the foot pressure show that the multi-elastic insoles had smaller foot pressure value than that of no-insole. Moreover, Insole 2 and Insole 3 have the smallest increasing rate in foot pressure. The EMG results show that the multi-elastic insoles had smaller EMG shift value than that of no-insole in 2 hour, and then shift value shows the smallest value in Insole 2. Therefore, this study presents that the multi-elastic insoles have reducing effects of the work load for a long time standing work in both side of foot pressure and EMG.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.6
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• pp.1180-1184
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• 2006

In a speech coding system using excitation source of voiced and unvoiced, it would be a distortion of speech quality in a voiced and an unvoiced consonants in a frame. In this paper, I propose a new multi-pulse coding method make use of V/S/TSIUVC switching and TSIUVC approximation-synthesis method in order to restrict a distortion of speech quality. The TSIUVC is extracted by using the zero crossing rate and individual pitch pulse. And the TSIUVC extraction rate was 91% for female voice and 96.2% for male voice. The important thing is that the frequency information of 0.547kHz below and 2.813kHz above can be made with high quality synthesis waveform within TSIUVC. I evaluated the MPC of V/UV and FBD-MPC of V/S/TSIUVC. As a result, the synthesis speech of FBD-MPC was better in speech quality than the MPC.

• Journal of Korea Society of Industrial Information Systems
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• v.6 no.3
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• pp.79-86
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• 2001

The CTD(Cumulative Trauma Disorder) as a new kind of occupational disease occurs mainly to workers on handling line under the highly-specialized industrial environments. This study took into account their exposure to Cumulative Trauma Disorders(CTD) by the utilization of EMG system, with respect to worker's muscle fatigue test according to fulfillment of iterative and simple task. The findings of this study were as follows : From the result of AEMG test analysis, worker's fatigue extent according to elapsed time of task was inclined to be increased continually. On the other hand, after its task ending, their fatigue extent was inclined to be decreased than before-circumstance of refractory brick lifting. The transference of MF(Median Frequency) and MPF(Mean Power Frequency) had highly significant difference between muscle fatigue and the elapsed time of work. Especially, their fatigue extent to erectorspinae and multifidus to lift firebrick was increased in the mean time. The transference of ZCR(Zero Crossing Rate) had considerable significant difference between muscle fatigue and the elapsed time of work. In short, as the work went of the muscle fatigue extent increased gradually. Thus, it can be concluded that the fatigue of erectorspinae and multifidus extent according to fulfillment of iterative and simple task is gradually being increased.