Abstract
There are many evaluation metrics and methods that can be used to quantify and predict a model’s future performance on previously unknown data. In the area of Human Activity Recognition (HAR), the methodology used to determine the training, validation, and test data can have a significant impact on the reported accuracy. HAR data sets typically contain few test subjects with the data from each subject separated into fixed-length segments. Due to the potential leakage of subject-specific information into the training set, cross-validation techniques can yield erroneously high classification accuracy. In this work (Source code available at: https://github.com/imics-lab/model_evaluation_for_HAR.), we examine how variations in evaluation methods impact the reported classification accuracy of a 1D-CNN using two popular HAR data sets.
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Notes
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Several MobiAct subjects did not complete all ADLs were dropped resulting in a non-contiguous subject list. E.g. there is no subject number 14.
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GPU model Tesla P100-PCIE-16 GB at https://www.colab.research.google.com.
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Hinkle, L.B., Metsis, V. (2021). Model Evaluation Approaches for Human Activity Recognition from Time-Series Data. In: Tucker, A., Henriques Abreu, P., Cardoso, J., Pereira Rodrigues, P., Riaño, D. (eds) Artificial Intelligence in Medicine. AIME 2021. Lecture Notes in Computer Science(), vol 12721. Springer, Cham. https://doi.org/10.1007/978-3-030-77211-6_23
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