• Journal of Internet of Things and Convergence
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• v.10 no.3
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• pp.25-33
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• 2024

In tomographic image reconstruction, the focus is on developing CT image reconstruction methods that can maintain high image quality while reducing patient radiation exposure. Typically, statistical image reconstruction methods have the ability to generate high-quality and accurate images while significantly reducing patient radiation exposure. However, in cases like CT image reconstruction, which involve multi-dimensional parameter estimation, the degree of the Hessian matrix of the penalty function is very large, making it impossible to calculate. To solve this problem, the author proposed the PEMG-1 algorithm. However, the PEMG-1 algorithm has issues with the convergence speed, which is typical of statistical image reconstruction methods, and increasing the penalty log-likelihood. In this study, we propose a reconstruction algorithm that ensures fast convergence speed and monotonic increase in likelihood. The basic structure of this algorithm involves sequentially updating groups of pixels instead of updating all parameters simultaneously with each iteration.

• Journal of Biomedical Engineering Research
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• v.16 no.4
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• pp.507-516
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• 1995

The nonstationary identifier in the DCT domain is suggested in this study for the identification of AR parameters of above-lesion upper-trunk electromyographic (EMG) signals as a means of developing a reliable real time signal to control functional electrical stimulation (FES) in paraplegics to enable primitive walking. As paraplegic shifts his posture from one attitude to another, there is transition period where the signal is clearly nonstationary. Also as muscle fatigues, nonstationarities become more prevalent even during stable postures. So, it requires a develpment of time varying nonstationary EMG signal identifier. In this paper, time varying nonstationary EMG signals are transformed into DCT domain and the transformed EMG signals are modeled and analyzed in the transform domain. In the DCT domain, we verified reduction of condition number and increment of the smallest eigenvalue of input correlation matrix that influences numerical properties and mean square error were compared with SLS algorithm, and the proposed algorithm is implemented using IMS T-805 parallel processing computer for real time application.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.54-61
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• 2009

This paper presents a new electromyogram (EMG) pattern recognition method based on the Hierarchical Temporal Memory (HTM) algorithm which is originally devised for image pattern recognition. In the modified HTM algorithm, a simplified two-level structure with spatial pooler, temporal pooler, and supervised mapper is proposed for efficient learning and classification of the EMG signals. To enhance the recognition performance, the category information is utilized not only in the supervised mapper but also in the temporal pooler. The experimental results show that the ten kinds of hand motion are successfully recognized.

• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.48-57
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• 2003

This paper describes an approach for classifying myoelectric patterns using a multilayer perceptrons (MLP's) with genetic algorithm and hidden Markov models (HMM's) hybrid classifier. Genetic Algorithms play a role of selecting Multilayer Perceptron's optimized initial connection weights by its typical global search. The dynamic aspects of EMG are important for tasks such as continuous prosthetic control or various time length EMG signal recognition, which have not been successfully mastered by the most neural approaches. It is known that the hidden Markov model (HMM) is suitable for modeling temporal patterns. In contrast, the multilayer feedforward networks are suitable for static patterns. And, a lot of investigators have shown that the HMM's to be an excellent tool for handling the dynamical problems. Considering these facts, we suggest the combination of ANN and HMM algorithms that might lead to further improved EMG recognition systems.

• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.385-391
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• 2023

In the elderly population, sarcopenia occurs due to physical aging, leading to movement restrictions and loss of function. This results in dependence on daily activities and limitations in participation, ultimately decreasing the overall quality of life. In this study, we propose an algorithm designed to enable patients with sarcopenia to perform sit-to-stand and stand-to-sit movements seamlessly in their daily lives. The algorithm incorporates a wearable robot for muscle support and includes algorithms for standing and seated muscle strength support. To validate the algorithm's performance, EMG sensors were attached to the Rectus Femoris and Biceps Femoris muscles. The participants underwent two scenarios: one without wearing the device and one with the device providing muscle strength support, performing sit-to-stand and stand-to-sit motions for one minute in each case. The results showed a 16% increase in the EMG peak value of the Rectus Femoris muscle during standing motion (p=0.009). On the right side, there was a roughly 20% decrease (p=0.018) during standing and a 21% decrease (p=0.014) during sitting motion. In the future, we aim to gather additional data to further refine the algorithm. Our goal is to develop an optimal muscle strength support algorithm based on this data, making it applicable for real-life use by patients with sarcopenia.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.05
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• pp.34-38
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• 1991

This paper describes and discusses the employment of EMG pattern analysis to provide upper-motor-neuron paraplegics with patient-responsive control of FES (functional electrical stimulation) for the purpose of walker-supported walking. The use of above - lesion EMG signals as a solution to the control problem is considered. The AR (autoregressive) parameters are identified by Kalman filter algorithm using DSP chip and classified by fuzzy theory. The control and stimuli part of the below-lesion are based on microprocessor(8031). The designed stimulator is a 4-channel version. The experiments described above have only attempted to discriminate between standing function and sit-down function. A further advantage of the this system is applied for motor rehabilitation of social readaption of paralyzed humans.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.10 no.4
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• pp.329-336
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• 2016

Deaf people using sign language are experiencing social inequalities and financial losses due to communication restrictions. In this paper, real-time pattern recognition algorithm was applied to distinguish American Sign Language using an armband sensor(8-channel EMG sensors and one IMU) to enable communication between the deaf and the hearing people. The validation test was carried out with 11 people. Learning pattern classifier was established by gradually increasing the number of training database. Results showed that the recognition accuracy was over 97% with 20 training samples and over 99% with 30 training samples. The present study shows that sign language recognition using armband sensor is more convenient and well-performed.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.418-421
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• 2009

This paper presents a human-machine interaction based on a realtime upper limb motion prediction method using surface electromyography (sEMG). The motions were predicted using an artificial neural network algorithm and sEMG signals which are acquired from five muscles, and then a manipulator was controlled to follow after the predicted motions. Upper limb motions were restricted to 2D vertical plane with the contact condition between a user and an end-effector of manipulator. In order to demonstrate the feasibility of the proposed method, experiments using developed method and using a goniometer were performed. The results showed that the proposed real-time motion prediction method can be implemented a human-machine interaction system.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.713-716
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• 1987

A new approach to the real-time measurement of muscle fatigue by using adaptive filtering algorithm is proposed. Unlike previously reported methods, it can estimate the muscle fatigue at every sample as the EMG signal statistics change. As a result, the muscle conduction velocity ranged between 4.2-5m/s at low tension and 3-4m/s at fatigue state.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.4
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• pp.371-376
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• 2017

In this paper, we propose an algorithm that can recognize the pattern regardless of the sensor position when performing EMG pattern recognition using circular EMG system equipment. Fourteen features were extracted by using the data obtained by measuring the eight channel EMG signals of six motions for 1 second. In addition, 112 features extracted from 8 channels were analyzed to perform principal component analysis, and only the data with high influence was cut out to 8 input signals. All experiments were performed using k-NN classifier and data was verified using 5-fold cross validation. When learning data in machine learning, the results vary greatly depending on what data is learned. EMG Accuracy of 99.3% was confirmed when using the learning data used in the previous studies. However, even if the position of the sensor was changed by only 22.5 degrees, it was clearly dropped to 67.28% accuracy. The accuracy of the proposed method is 98% and the accuracy of the proposed method is about 98% even if the sensor position is changed. Using these results, it is expected that the convenience of the users using the circular EMG system can be greatly increased.