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JACIII Vol.21 No.1 pp. 109-118
doi: 10.20965/jaciii.2017.p0109
(2017)

Paper:

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Noise Reduction in Swallowing Muscle Activity Measurement Based on Mixture Gaussian Distribution Model

Nobuyuki Ohmori*1, Chihiro Murasawa*1, Jumpei Aizawa*1, Hideya Momose*2, Yoshito Koyama*3, Hiroshi Kurita*3, Hiroaki Yoshida*4, and Masayoshi Kamijo*4

*1Material Technology Department, Nagano Prefecture General Industrial Technology Center
1-18-1 Wakasato, Nagano, Nagano 380-0928, Japan

*2Nishizawa Electric Meters Manufacturing
6249 Sakaki, Sakaki, Nagano 389-0601, Japan

*3School of Medicine, Shinshu University
3-1-1 Asahi, Matsumoto, Nagano 390-8621, Japan

*4Faculty of Textile Science and Technology, Shinshu University
3-15-1 Tokida, Ueda, Nagano 386-8567, Japan

Received:
May 20, 2016
Accepted:
October 17, 2016
Published:
January 20, 2017
Creative Commons License
Keywords:
swallowing，sensor sheet，noise，electromyography
Abstract
For the noninvasive measurement of swallowing muscle activity, surface electromyograms and swallowing sounds are used. The electromyogram electrodes can be placed appropriately only by experts with specialized knowledge about the location of the swallowing muscle group. Therefore, these sensors have not been used for measurements in food development, for which there were no experts. In order to develop a simple swallowing muscle measurement method for food development, we proposed a sensor sheet consisting of multiple electromyogram electrodes and observed that different swallowing muscle activities could be measured depending on the type of food. In this work, we study a calculation method for the elimination of noise, which is inevitable in electromyograms, from the sensor sheet measurement results and prove that the method improves the performance of the swallowing muscle activity measurements.
Cite this article as:
N. Ohmori, C. Murasawa, J. Aizawa, H. Momose, Y. Koyama, H. Kurita, H. Yoshida, and M. Kamijo, “Noise Reduction in Swallowing Muscle Activity Measurement Based on Mixture Gaussian Distribution Model,” J. Adv. Comput. Intell. Intell. Inform., Vol.21 No.1, pp. 109-118, 2017.
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