Abstract
Skeleton-based motion capture (MoCap) systems have been widely used in the game and film industry for mimicking complex human actions for a long time. MoCap data has also proved its effectiveness in human activity recognition tasks. However, it is a quite challenging task for smaller datasets. The lack of such data for industrial activities further adds to the difficulties. In this work, we have proposed an ensemble-based machine learning methodology that is targeted to work better on MoCap datasets. The experiments have been performed on the MoCap data given in the Bento Packaging Activity Recognition Challenge 2021. Bento is a Japanese word that resembles lunch-box. Upon processing the raw MoCap data at first, we have achieved an astonishing accuracy of 98% on tenfold cross-Validation and 82% on leave-one-out cross-validation by using the proposed ensemble model.
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Nazmus Sakib, A.H.M., Basak, P., Doha Uddin, S., Mustavi Tasin, S., Ahad, M.A.R. (2022). Can Ensemble of Classifiers Provide Better Recognition Results in Packaging Activity?. 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_10
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