  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.
