Deep learning of smartphone sensor data for personal health assistance

Abstract This paper strives to construct the relevance between smartphone sensor data and personal health via a deep learning method. Firstly, the data captured by smartphone sensors is categorized into groups by deep stacked autoencoders (SAE), which is a traditional deep learning architecture with multi-layer sparse autoencoders for feature extraction and a softmax layer for classification. For instance, accelerometer data from smartphones can be classified in accordance with different user motion states such as sitting, standing, walking, and running. Secondly, the quantitative correlation between divided sensor data and individual health is built. For example, walking and running for a suitable amount of time are beneficial to human health. Finally, a simulation experiment is devised to verify the performance of the proposed method. A 0.15 percent gain on the overall classification accuracy for automatic feature extraction on the human action recognition (HAR) dataset has been achieved compared with the state of the art methods and 0.5 percent gain for manual feature extraction. It is also demonstrated by the experimental results that smartphone sensor data can reveal the health status of the user to some degree and smartphone can serve as an assistant for human health.

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