A systematic review of smartphone-based human activity recognition for health research
This review addresses the problem of synthesizing and guiding smartphone-based HAR methods for health researchers, but it is incremental as it summarizes existing approaches without introducing new techniques.
This systematic review analyzed 108 articles on smartphone-based human activity recognition (HAR) for health research, summarizing common practices in data acquisition, preprocessing, feature extraction, and classification. It concluded that smartphones are well-suited for HAR but highlighted the need for improvements in data quality, participant diversity, and methodological transparency to enhance population-level impact.
Background: Smartphones are now nearly ubiquitous; their numerous built-in sensors enable continuous measurement of activities of daily living, making them especially well-suited for health research. Researchers have proposed various human activity recognition (HAR) systems aimed at translating measurements from smartphones into various types of physical activity. In this review, we summarize the existing approaches to smartphone-based HAR. Methods: We systematically searched Scopus, PubMed, and Web of Science for peer-reviewed articles published up to December 2020 on the use of smartphones for HAR. We extracted information on smartphone body location, sensors, and physical activity types studied and the data transformation techniques and classification schemes used for activity recognition. Results: We identified 108 articles and described the various approaches used for data acquisition, data preprocessing, feature extraction, and activity classification, identifying the most common practices and their alternatives. Conclusions: Smartphones are well-suited for HAR research in the health sciences. For population-level impact, future studies should focus on improving quality of collected data, address missing data, incorporate more diverse participants and activities, relax requirements about phone placement, provide more complete documentation on study participants, and share the source code of the implemented methods and algorithms.