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Mobile sensing systems have been widely used as a practical approach to collect behavioral and health-related information from individuals and to provide timely intervention to promote health and well being, such as mental health and chronic care. As the objectives of mobile sensing could be either <italic>personalized medicine for individuals</italic> or <italic>public health for populations</italic>, in this work, we review the design of these mobile sensing systems, and propose to categorize the design of these systems in two paradigms&#x2014;1) <italic>personal sensing</italic> and 2) <italic>crowdsensing</italic> paradigms. While both sensing paradigms might incorporate common ubiquitous sensing technologies, such as <italic>wearable sensors</italic>, <italic>mobility monitoring</italic>, <italic>mobile data offloading</italic>, and <italic>cloud-based data analytics</italic> to collect and process sensing data from individuals, we present two novel taxonomy systems based on the: 1) <italic>sensing objectives</italic> (e.g., goals of mobile health (mHealth) sensing systems and how technologies achieve the goals) and 2) <italic>the sensing systems design and implementation (D&#x0026;I)</italic> (e.g., designs of mHealth sensing systems and how technologies are implemented). With respect to the two paradigms and two taxonomy systems, this work systematically reviews this field. Specifically, we first present technical reviews on the mHealth sensing systems in eight common/popular healthcare issues, ranging from depression and anxiety to COVID-19. By summarizing the mHealth sensing systems, we comprehensively survey the research works using the two taxonomy systems, where we systematically review the <italic>sensing objectives</italic> and <italic>sensing systems D&#x0026;I</italic> while mapping the related research works onto the life-cycles of mHealth Sensing, i.e.: 1) <italic>sensing task creation and participation</italic>; 2) <italic>(health surveillance and data collection</italic>; and 3) <italic>data analysis and knowledge discovery</italic>. In addition to summarization, the proposed taxonomy systems also help the potential directions of mobile sensing for health from both personalized medicine and population health perspectives. Finally, we attempt to test and discuss the validity of our scientific approaches to the survey.

From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques