SGBS: A novel smart garbage bin system for understanding household garbage disposal behaviour

Likotiko, Eunice; Misaki, Shinya; Matsuda, Yuki; Yasumoto, Keiichi

doi:10.23919/icmu50196.2021.9638956

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…To solve Problem 1, we conducted a preliminary study to learn the amount of garbage disposed of each time and predict growth behaviour at a single house [9]. In this study, we designed and developed the initial smart garbage bin prototype embedded with ToF (time of flight) and load cell sensors to track the amount of garbage during disposal.…”

Section: Preliminary Studymentioning

confidence: 99%

“…In our previous study [9], we addressed the first problem by proposing a smart garbage bin system with ToF and weight sensors and the ARIMA model based garbage growth prediction method. In this paper, focusing on solving the second and the third problems, we propose a newly designed and developed smart garbage bin system (SGBS) embedded with multiple sensors to identify the garbage contents disposed of.…”

Section: Introductionmentioning

confidence: 99%

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Garbage Content Estimation Using Internet of Things and Machine Learning

2023

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Much garbage is produced daily in homes due to living activities, including cooking and eating. The garbage must be adequately managed for human well-being and environmental protection. Although the existing IoT-based smart garbage systems have gained high garbage classification accuracy, they still have a problem that they provide a small number of garbage categories, not enough for reasonable practices of household garbage separation. This study presents a new smart garbage bin system, SGBS, embedded with multiple sensors to solve the problem. We deployed temperature, humidity, and gas sensors to know the condition and identify the garbage content disposed of. Then, we introduce a new garbage content estimation method by training a machine learning model using daily collected fuse sensor readings combined with detailed household garbage contents annotations to perform garbage classification tasks. For evaluation, we deployed the designed SGBS in five households over one month. As a result, we confirmed that the leave-one-house cross-validation results showed an accuracy of 91% in 5 kitchen waste contents, also, 89% in 5 paper/softbox contents, and 85% in the 8 garbage categories for the classification tasks. INDEX TERMSIoT-based smart garbage system, Garbage content estimation, Machine learning algorithms VOLUME 4, 2016

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Section: Preliminary Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Garbage Content Estimation Using Internet of Things and Machine Learning

2023

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“…Furthermore, different distances to trash bins and the way of the waste collection are essential factors influencing residents’ WSB as well (Li et al, 2019; Rousta et al, 2015). In recent years, some scholars used experimental approaches to study the effects of different interventions on residents’ WSB and found that factors such as trash bin colour, the distance to trash bins along the walking path, and the amount of waste produced can significantly affect residents’ WSB (Leeabai et al, 2019; 2021; Likotiko et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Can we ‘Nudge’ people to better waste separation behaviours? Policy interventions mediated by habit, sense of separation efficiency and external environmental perceptions

Liang

Feng

et al. 2023

Waste Manag Res

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This article studies the impact of intervention measures with nudge characteristics on residents’ waste separation behaviours under the background of new ‘Beijing Municipal Regulation on Domestic Waste Management’. We analyse whether and how nudge interventions affect residents’ waste separation behaviours through the Ordered Logit model and mediation analysis. Our research results show that three nudge tools, namely, the timing and fixed-point separation system, the cleaning staff guidance system and the uniform clothing and styling, can effectively promote residents from low classification levels to high classification levels. In the process of nudging interventions, factors such as residents’ waste separation habit, external environmental perception and the sense of separation efficiency play a mediating role. Our study’s findings suggest that effective nudge tools are helpful in cultivating residents’ separation habits and hence we recommend that policy makers promote and popularise them in their attempt to improve domestic waste management behaviours.

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