Abstract
This paper reports Bento Packaging Activity Recognition Challenge by team “RitsBen” held in the International Conference on Activity and Behavior Computing (ABC 2021). Our approach used an autocorrelation function in the preprocessing to isolate the data since the dataset was given with repetitive activity. We then use a model that implements convolutional layers and LSTM. The final decision is made by majority vote using sigmoid predictions output from all body parts. The loss is calculated using BCEWithLogitsLoss for each body part. The evaluation results showed that average accuracy of 0.123 was achieved among subjects 1, 2, and 3 in leave-one-subject-out manner. However, we did not achieve high accuracy as the possibility that the extraction of repetitive actions was not correct.
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Fujii, A., Yoshida, K., Shirai, K., Murao, K. (2022). Bento Packaging Activity Recognition with Convolutional LSTM Using Autocorrelation Function and Majority Vote. 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_16
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DOI: https://doi.org/10.1007/978-981-19-0361-8_16
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