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.

翻译：研究者提出了各种人类活动识别系统(HAR)系统,旨在将智能手机的测量结果转化为各种类型的体育活动。在这次审查中,我们总结了目前对智能手机HAR的现有方法。方法:我们系统搜索了Scopus、PubMed和科学网,以用于2020年12月以前出版的关于智能手机用于HAR的文章的同行审查。我们收集的信息涉及智能手机身体位置、传感器和物理活动类型,以及用于活动识别的数据转换技术和分类计划。结果:我们确定了108篇文章,并介绍了用于数据采集、数据预处理、地物提取和活动分类的各种办法,确定了最常见的做法及其替代办法。结论:智能手机适合于卫生科学中的HAR研究。关于人口层面的影响,今后的研究应侧重于提高所收集的数据质量、处理缺失的数据、纳入更多样化的参与者和活动、关于手机放置的更宽松要求、关于用户配置的更完整的数据源码的共享方法。