Abstract
Human activity recognition (HAR) has a great impact on human-robot collaboration (HRC), especially in industrial works. However, it is difficult to find industrial activity data. With a goal of making the interactions between humans and robots more straightforward, we organized Bento Packaging Activity Recognition Challenge as a part of The 3rd International Conference on Activity and Behavior Computing. Here, the term Bento refers to a single-serving lunch box originated in Japan. We provided ten Bento packing activities data. The activities are performed by four subjects with a moving conveyor belt. In this work, we analyze and summarize the approaches of submission of the challenge. The challenge started on June 1st, 2021, and continued until August 25th, 2021. The participant teams used the given dataset to predict the ten activities, and they were evaluated using accuracy. The winning team used an ensemble model and achieved around 64% accuracy on testing data. To further improve the accuracy of the testing data, models particularly designed for small data with larger intra-class similarity could help.
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Adachi, K., Alia, S.S., Nahid, N., Kaneko, H., Lago, P., Inoue, S. (2022). Summary of the Bento Packaging Activity Recognition Challenge. In: Ahad, M.A.R., Inoue, S., Roggen, D., Fujinami, K. (eds) Sensor- and Video-Based Activity and Behavior Computing. Smart Innovation, Systems and Technologies, vol 291. Springer, Singapore. https://doi.org/10.1007/978-981-19-0361-8_17
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DOI: https://doi.org/10.1007/978-981-19-0361-8_17
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