Effects of matrix completion on the classification of undersampled human activity data streams

Classification of activities of daily living is of paramount importance in modern healthcare applications. However, hardware monitoring constraints lead frequently to missing raw values, dramatically affecting the performance of machine learning algorithms. In this work, we study the problem of efficient estimation of missing linear acceleration and angular velocity measurements, experimenting on a public Human Activity Recognition (HAR) dataset. We exploit the data correlation to formulate the problem as an instance of low-rank Matrix Completion (MC) within a general classification framework. We consider the effects of our proposed reconstruction method on the classification accuracy as related to the size of the training and test sets, and the single versus collective recovery. Additionally, we compare the performance of our approach with popular imputation and expectation maximization algorithms for treating missing measurements, in conjunction with several state-of-the-art classifiers. The results highlight that robust and efficient classification is feasible even with a substantially reduced amount of measurements.

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