Abstract
Recognizing bio-signals, such as EMG, EEG, EOG and ECG, is a promising theme of study since it provides with a convenient means for human-machine interaction. Various approaches of determining features of bio-signals were known for discerning predefined motions/intentions of human, but most of them are applicable mostly only to a single subject, due to inherent characteristics of bio-signals. Lately, several new types of pattern classifier with known features have been proposed to cope with the problem of subject-dependency, but their error rates are still conspicuous when accommodating multiple subjects. Based on the soft computing techniques, this paper presents a comparative experimental study to minimize the subject-dependency. It is shown that the induced feature vector set obtained by the proposed algorithm has less subject-dependency than other existing methods.
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References
Basmajian, J. V. and C. J. De Luca. (1985). Muscle Alive, fifth edition, Williams & Wilkins.
Bigland, B. and O. C. J. Lippold. (1954). “The Relation Between Force, Velocity and Integrated Electrical Activity in Human Muscles,” J. Physiology 123, 214–224.
Chan, F. H. Y., Y.-S. Yang, F. K. Lam, et al. (Sept. 2000). “Fuzzy EMG Classification for Prosthesis Control,” IEEE Tr. on Rehab. Eng. 8 (3), 305–311.
Fukuda, O., T. Tsuji, A. Otsuka, et al. (1998). “An EMG-based Rehabilitation Aid for Prosthetic Control,” IEEE Int'l Workshop on Robot and Human Communication, Sept. 30–Oct. 2, 1998, Takamutsu, Kagawa, Japan, 1, 214–219.
Fukunaga, K. (1972). Introduction to Statistical Pattern Recognition, first edition, Academic Press.
Graupe, D. and W. K. Cline. (March 1975). “Functional Separation of EMG Signals via ARMA Identification Methods for Prosthesis Control Purposes,” IEEE Tr. on Systems, Man, and Cybernetics SMC-5 (2), 252–259.
Guger, C., A. Schulogl, G. Pfurtscheller, et al. (2001). “Rapid Prototyping of an EEG-Based Brain-Computer Interface (BCI),” IEEE Tr. on Neural Systems and Rehab. Eng. 9 (1), 49–58.
Han, J.-S., W.-C. Bang, Z. Bien, et al. (2000). “New EMG Pattern Recognition Based on Soft Computing Techniques and its Application to Control of a Rehabilitation Robotic Arm,” Proc. 6th International Conference on Soft Computing, Oct. 1–4, 2000, Iizuka, Japan, 890–897.
Hong, S.-H. et al. (1991). “A Study on the Pattern Classification of the EMG Signals Using Neural Network and Probabilistic Model,” Journal of IEEK 28 (10), 85–95.
Kwon, S. H. and H. C. Kim. (1999). “EOG-Based Glasses-Type Wireless Mouse for the Disabled,” Proc. of the First Joint of BMES/EMBS conf. 1, 592.
Nishikawa, D., W. Yu, H. Yokoi, et al. (1999). “EMG Prosthetic Hand Controller Discriminating Ten Motions using Real-Time Learning Method,” IROS '99, Kyungju, Korea, 3, 1592–1597.
Pawlak, Z. (1991). Rough Sets: Theoretical Aspects and Reasoning About Data, Kluwer Academic Publishers.
Saridis, G. N. and T. Gootee. (June 1982). “EMG Pattern Analysis and Classification for a Prosthetic Arm,” IEEE Tr. Biomed. Eng. 29, 403–
Simpson, P. (Sept. 1992) “Fuzzy Min-Max Neural Networks – Part 1: Classification,” IEEE Tr. On Neural Networks 3, 776–786.
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Han, JS., Bang, WC. & Bien, Z.Z. Feature Set Extraction Algorithm based on Soft Computing Techniques and Its Application to EMG Pattern Classification. Fuzzy Optimization and Decision Making 1, 269–286 (2002). https://doi.org/10.1023/A:1019688829453
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DOI: https://doi.org/10.1023/A:1019688829453