Abstract
Exercise Recognition (ExR) is relevant in many high impact domains, from healthcare to recreational activities to sports sciences. Like Human Activity Recognition (HAR), ExR faces many challenges when deployed in the real-world. For instance, typical lab performances of Machine Learning (ML) models, are hard to replicate, due to differences in personal nuances, traits and ambulatory rhythms. Thus effective transferability of a trained ExR model, depends on its ability to adapt and personalise to a new user or a user group. This calls for new experimental design strategies that are person-aware, and able to organise train and test data differently from standard ML practice. Specifically, we look at person-agnostic and person-aware methods of train-test data creation, and compare them to identify best practices on a comparative study of personalised ExR model transfer. Our findings show that ExR when compared to results with other HAR tasks, to be a far more challenging personalisation problem and also confirms the utility of metric learning algorithms for personalised model transfer.
This work is part funded by selfBACK which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 689043.
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Wijekoon, A., Wiratunga, N. (2020). Evaluating the Transferability of Personalised Exercise Recognition Models. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_3
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DOI: https://doi.org/10.1007/978-3-030-48791-1_3
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