Abstract
The Cooking Activity Recognition Challenge tasked the competitors with recognizing food preparation using motion capture and acceleration sensors. This paper summarizes our submission to this competition, describing how we reordered the training data, relabeled it and how we handcrafted features for this dataset. Our classification pipeline first detected basic user actions (micro-activities); using them it recognized the recipe, and then used the recipe to refine the original micro-activity predictions. After the post-processing step using a Hidden Markov Model, we achieved the competition score of 95% on the training data with cross-validation.
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Brown, R.T., Komaiko, K.D., Shi, Y., Fung, K.Z., Boscardin, W.J., Au-Yeung, A., Tarasovsky, G., Jacob, R., Steinman, M.A.: Bringing functional status into a big data world: validation of national Veterans Affairs functional status data. PLoS One (2017). https://doi.org/10.1371/journal.pone.0178726
Shen, J., Naeim, A.: Telehealth in older adults with cancer in the United States: The emerging use of wearable sensors. J. Geriatr. Oncol. (2017). https://doi.org/10.1016/j.jgo.2017.08.008
Alia, S.S., Lago, P., Takeda, S., Adachi, K., Benaissa, B., Ahad, M.A.R., Inoue, S.: Summary of the cooking activity recognition challenge. Human activity recognition challenge, smart innovation, systems and technologies. Springer Nature, Berlin (2020)
Lago, P., Takeda, S., Kohei, A., Alia, S.S., Matsuki, M., Benaissa, B., Inoue, S., Charpillet, F.: Cooking activity dataset with macro and micro activities. IEEE DataPort (2020). https://doi.org/10.21227/hyzg-9m49
Lago, P., Takeda, S., Alia, S.S., Adachi, K., Benaissa, B., Charpillet, F., Inoue, S.: A dataset for complex activity recognition with micro and macro activities in a cooking scenario’, preprint (2020)
Kubota, A., Iqbal, T., Shah, J.A., Riek, L.D.: Activity recognition in manufacturing: the roles of motion capture and sEMG+inertial wearables in detecting fine vs. gross motion. In: International Conference on Robotics and Automation (ICRA), Montreal, Canada (2019)
Mobark, M., Chuprat, S., Mantoro, T.: Improving the accuracy of complex activities recognition using accelerometer-embedded mobile phone classifiers. In: 2017 second international conference on informatics and computing (ICIC). Jayapura, Indonesia (2017)
Unity.: https://unity.com/
scikit-learn.: https://scikit-learn.org/stable/
Cvetković, B., Szeklicki, R., Janko, V., Lutomski, P., Luštrek, M.: Real-time activity monitoring with a wristband and a smartphone. Inf. Fus. 43, 77–93 (2018)
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Picard, C., Janko, V., Reščič, N., Gjoreski, M., Luštrek, M. (2021). Identification of Cooking Preparation Using Motion Capture Data: A Submission to the Cooking Activity Recognition Challenge. In: Ahad, M.A.R., Lago, P., Inoue, S. (eds) Human Activity Recognition Challenge. Smart Innovation, Systems and Technologies, vol 199. Springer, Singapore. https://doi.org/10.1007/978-981-15-8269-1_9
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DOI: https://doi.org/10.1007/978-981-15-8269-1_9
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