A systematic review of human activity recognition using smartphones
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Smartphones have become a global communication tool and more recently a technology for studying human behavior. Given their numerous built-in sensors, smartphones are able to capture detailed and continuous observations on activities of daily living. However, translation of measurements from these consumer-grade devices into research-grade physical activity patterns remains challenging. Over the years, researchers have proposed various human activity recognition (HAR) systems which vary in algorithmic details and statistical principles. In this paper, we summarize existing approaches to smartphone-based HAR. We systematically screened the literature on Scopus, PubMed, and Web of Science in the areas of data acquisition, data preprocessing, feature extraction, and activity classification. We ultimately identified 72 articles on smartphone-based HAR. To provide an understanding of the literature, we discuss each of these areas separately, identify the most common practices and their alternatives, and propose possible future research directions for this interesting and important field.
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