• Journal of Korea Multimedia Society
• /
• v.22 no.12
• /
• pp.1491-1499
• /
• 2019

Insufficient sleep time and bad sleep quality causes many illnesses and it's research became more and more important. The most common method for measuring sleep quality is the polysomnography(PSG). The PSG is a test used to diagnose sleep disorders. The most common PSG data is obtained from the examiner, which attaches several sensors on a body and takes sleep overnight. However, most of the sleep stage classification in PSG are low accuracy of the classification. In this paper, we have studied algorithm for sleep level classification based on machine learning which can replace PSG. EEG, EOG, and EMG channel signals are studied and tested by using CNN algorithm. In order to compensate the performance, a mixed model using both CNN and DNN models is designed and tested for performance.

• Proceedings of the KOSOMBE Conference
• /
• v.1997 no.05
• /
• pp.383-386
• /
• 1997

In the EMG evaluation, the neuropathy may be diagnosed by a detection of denervation potentials in the group of muscles. These abnomal potentials might be confused with normal endplate spikes. In this paper we present the software algorithm in C, which automatically detects spontaneous activity such as denervation potentials and endplate potentials and distingushes between those potentials. Parameters with statistically significant differences were used for this automated algorithm. It was concluded that the algorithm established in this study will improve accuracy in EMG diagnosis.

• The Journal of Korea Robotics Society
• /
• v.15 no.1
• /
• pp.62-69
• /
• 2020

This paper proposes a pattern recognition and classification algorithm based on a circular structure that can reflect the characteristics of the sEMG (surface electromyogram) signal measured in the arm without putting the placement limitation of electrodes. In order to recognize the same pattern at all times despite the electrode locations, the data acquisition of the circular structure is proposed so that all sEMG channels can be connected to one another. For the performance verification of the sEMG pattern recognition and classification using the developed algorithm, several experiments are conducted. First, although there are no differences in the sEMG signals themselves, the similar patterns are much better identified in the case of the circular structure algorithm than that of conventional linear ones. Second, a comparative analysis is shown with the supervised learning schemes such as MLP, CNN, and LSTM. In the results, the classification recognition accuracy of the circular structure is above 98% in all postures. It is much higher than the results obtained when the linear structure is used. The recognition difference between the circular and linear structures was the biggest with about 4% when the MLP network was used.

• Journal of rehabilitation welfare engineering & assistive technology
• /
• v.7 no.2
• /
• pp.69-74
• /
• 2013

In this paper, we propose the pattern classification algorithm of recognizing wrist movements based on electromyogram(EMG) to raise the recognition rate. We consider 30 characteristics of EMG signals wirh the root mean square(RMS) and the difference absolute standard deviation value(DASDV) for the extraction of precise features from EMG signals. To get the groups of each wrist movement, we estimated 2-dimension features. On this basis, we divide each group into two parts with mean to compare and promote the recognition rate of pattern classification effectively. For the motion classification based on EMG, the k-nearest neighbor(k-NN) is used. In this paper, the recognition rate is 92.59% and 0.84% higher than the study before.

• 제어로봇시스템학회:학술대회논문집
• /
• 1987.10b
• /
• pp.699-701
• /
• 1987

This paper deals With the EMG signal processing to apply the EMG signal to the Prosthetic arm. The EMG signals are generated by the voluntary contractions of the subject's musculature and is coded into binary words by the pulse width modulation. Command strings or sentences are constructed by concatenating several words, and are syntactically described by a context free grammar in Chomsky normal form and is tried to classify the movement pattern by the CYK algorithm.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.84.4-84
• /
• 2001

In this paper, we present a method to classify electromyogram (EMG) signals which are utilized to be control signals for patient-responsive walker-supported system for paraplegics. Patterns of EMG signals for dierent walking motions are classied via adequate filtering, real EMG signal extraction, AR-modeling, and modified self-organizing feature map (MSOFM). More efficient signal processing is done via a data-reducing extraction algorithm. Moreover, MSOFM classifies and determines the classified results are presented for validation.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.2209-2212
• /
• 2003

In this paper, we propose the method to extract the important frequency bands from the EMG signal, and for generation of feature vector using the important frequency bands. The EMG signal is measured with 4 sensor and is recorded as 4 channel’s time series data. The same frequency bands from 4 channel’s frequency components are selected as the important frequency bands. The feature vector is calculated by the function formed using the combination of selected same important frequency bands. The EMG signals acquired from seven wrist motion type are recognized by changing into the feature vector formed. Then, the extraction and generation is performed by using the double combination of the genetic algorithm (GA) and the neural network (NN). Finally, in order to illustrate the effectiveness of the proposed method, computer simulations are done.

• Journal of Biomedical Engineering Research
• /
• v.15 no.3
• /
• pp.305-316
• /
• 1994

All physical data in the real world are nonstationary signals that have the time varying statistical characteristics. Although few algorithms suitable to process the nonstationary signals have ever been suggested, these are treated the nonstationary signals under the assumption that the nonstationary signal is a piece-wise stationary signal. Recently, statistical analysis algorithms for the nonstationary signal have concentrated so much interest. In this paper, nonstationary EMG signals are mapped onto the orthogonal wavelet transform domain so that the eigenvalue spread of its autocorrelation matrix could be more smaller than that in the time domain. Then the model in the wavelet transform domain and an algorithm to estimate the model parameters are suggested. Also, an test signal generated by a white gaussian noise and the EMG signal are identified, and the algorithm performance is considered in the sense of the mean square error and the evaluation parameters.

• Proceedings of the KIEE Conference
• /
• 2004.11c
• /
• pp.6-8
• /
• 2004

The objective of this paper is to develop a powered wheelchair controller based on EMG for users with high-level spinal cord injury using a proportional control scheme. An advantage of EMG is relative convenience of acquisition by a surface electrode to users. Direction information can be easily extracted from two EMG channels and force information can be acquired by proportional relationship between the amplitude of EMG and user's power, respectively. Pattern classification algorithm is a threshold method with a supervised learning process. Furthermore, the emergency situation can be avoided using an interrupt function. We evaluated the performance of powered wheelchair controller by navigating a pre-defined path with three non-handicapped people. The results show the feasibility of EMG as an input interface for powered wheelchair and other devices for the seriously disabled.

• Proceedings of the KIEE Conference
• /
• 1984.07a
• /
• pp.114-116
• /
• 1984