ACOGARE: Acoustic-Based Litter Garbage Recognition Utilizing Smartwatch

Koki Tachibana (), Yugo Nakamura, Yuki Matsuda (), Hirohiko Suwa and Keiichi Yasumoto
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Koki Tachibana: Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma 630-0192, Japan
Yugo Nakamura: Graduate School and Faculty of Information Science and Electrical Engineering, Kyushu University, Nishi-ku, Fukuoka 819-0395, Japan
Yuki Matsuda: Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma 630-0192, Japan
Hirohiko Suwa: Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma 630-0192, Japan
Keiichi Yasumoto: Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma 630-0192, Japan

Sustainability, 2023, vol. 15, issue 13, 1-17

Abstract: Litter has become a social problem. To prevent litter, we consider urban planning, the efficient placement of garbage bins, and interventions with litterers. In order to carry out these actions, we need to comprehensively grasp the types and locations of litter in advance. However, with the existing methods, collecting the types and locations of litter is very costly and has low privacy. In this research, we have proposed the conceptual design to estimate the types and locations of litter using only the sensor data from a smartwatch worn by the user. This system can record the types and locations of litter only when a user raps on the litter and picks it up. Also, we have constructed a sound recognition model to estimate the types of litter by using sound sensor data, and we have carried out experiments. We have confirmed that the model built with other people’s data enabled to estimate the F-measure of 80.2% in a noisy environment through the experiment with 12 participants.

Keywords: machine learning; activity recognition; object detection; acoustic signal processing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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