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Sensors and Materials
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Sensors and Materials, Volume 31, Number 3(2) (2019)
Copyright(C) MYU K.K. All Rights Reserved.
pp. 815-822
S&M1816 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2019.2155
Published: March 19, 2019

Fishing Position Detection by Wearable Sensing to Achieve Fishery Support [PDF]

Hiroaki Taka

(Received October 13, 2018; Accepted December 21, 2018)

Keywords: fishery, wearable sensing, activity recognition, support vector machine

Many types of research focus on supporting fisheries by utilizing information and communication technology (ICT) to realize sustainable and efficient fishing industries. However, most of the previous studies focused on monitoring environmental data and fishing vessels. Our study focused on identifying the timing of when a fisherman catches an octopus in pot drift fishing for North Pacific giant octopus, in order to support fishery industries. A support vector machine (SVM) detects the timing at which a fisherman catches an octopus, using the feature values derived from acceleration data obtained with a wearable device. In this study, the author adopted three types of feature value sets for the SVM: peak frequency of fast Fourier transform (FFT), statistics, and the combination of peak frequency and statistics. From the evaluation results, the SVM was found to achieve a recall of over 90%. Future works include collecting a large amount of data and developing an application to detect the timing of catch.

Corresponding author: Hiroaki Taka


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This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Hiroaki Taka, Fishing Position Detection by Wearable Sensing to Achieve Fishery Support, Sens. Mater., Vol. 31, No. 3, 2019, p. 815-822.



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