Model Evaluation Approaches for Human Activity Recognition from Time-Series Data

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.