Abstract
As Team 2A&B Core, we contributed a K-nearest neighbour approach for classifying ten different activities for the Bento Packing Activity Recognition Challenge. We used hand-engineered features from motion capture data. Before classifying the data, we scaled it and applied a principal component analysis. We used the K-nearest neighbour classifier to find the best feature, and we found that the best feature was one that was related to a fixed position. Our approach achieved an accuracy of 38.78% on our validation subject and 42.00% on our test subject.
B. Friedrich and T.A.-M. Orsot: Both authors contributed equally to this research.
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Acknowledgements
The experiments were performed at the HPC cluster CARL, located at the University of Oldenburg (Germany), and funded by the DFG through its Major Research Instrumentation Programme (INST 184/157-1 FUGG) and the Ministry of Science and Culture (MWK) of the Lower Saxony State.
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Appendix
Appendix
Sensor modalities | MoCap markers |
Marker left hand | 8 9 10 |
Marker right hand | 4 6 7 |
Features | Hand-engineered |
Language and libraries | |
Window size and post-processing | No windows were used, no post-processing was applied |
Training/testing time | 3.1473 s/0.0027 s |
Machine specifications | 2x Intel Xeon E5-2650 \(12 \times 2.2\) GHz, 256 GB DDR4 RAM, no GPU |
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Friedrich, B., Orsot, T.AM., Hein, A. (2022). Using K-Nearest Neighbours Feature Selection for Activity Recognition. 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_14
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DOI: https://doi.org/10.1007/978-981-19-0361-8_14
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