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Fishing activity sensing and visualization system using sensor-equipped fishing rod: demo abstract

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Published:16 November 2020Publication History

ABSTRACT

In recent years, many studies and development of Cyber Physical Systems (CPS) have been carried out to feed back analysis results to human users in a physical space by using machine learning, aiming to analyze a huge amount of information obtained from physical space in a cyber space. To apply CPS to sports, a lot of studies have been conducted on sensing and recognizing actions and movements of athletes using machine learning. In this study, we focus on fishing as a sport, and propose a fishing CPS that recognizes anglers' actions in real-time and provides information on the past useful actions that are linked to fishing results depending on time and place as a decision support when the anglers do not make catch. In addition, this paper reports on the development of an IoT (Internet of Things) device that acquires positional information, acceleration and gyroscope information, and a web system that displays results of real-time activity recognition along with the place and time by animation for realizing the fishing CPS. We have evaluated the developed IoT device and web system from the viewpoint of practical use. As a result, we have confirmed that the GPS and acceleration sensors, in the actual breakwater environment, were constantly transmitting data to a server via UDP communication for 4 hours and 40 minutes.

References

  1. Shuichi Fukuda, Seongeon Hong, Hanbit Oh, Hyuckjin Choi, Yuki Matsuda, and Keiichi Yasumoto. 2019. Activity Recognition for Anglers using IMU Sensor Attached to Fishing Rod. Proceedings of the 2019th Autumn Convention of Advanced Marine Science and Technology Society (2019).Google ScholarGoogle Scholar

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  1. Fishing activity sensing and visualization system using sensor-equipped fishing rod: demo abstract

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    • Published in

      cover image ACM Conferences
      SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems
      November 2020
      852 pages
      ISBN:9781450375900
      DOI:10.1145/3384419

      Copyright © 2020 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 16 November 2020

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