Abstract
Over the years, mobile phones have become versatile devices with a multitude of capabilities due to the plethora of embedded sensors that enable them to capture rich data unobtrusively. In a world where people are more conscious regarding their health and well-being, the pervasiveness of smartphones has enabled researchers to build apps that assist people to live healthier lifestyles, and to diagnose and monitor various health conditions. Motivated by the high smartphone coverage among young adults and the unique issues they face, in this review paper, we focus on studies that have used smartphone sensing for the well-being of young adults. We analyze existing work in the domain from two perspectives, namely Data Perspective and System Perspective. For both these perspectives, we propose taxonomies motivated from human science literature, which enable to identify important study areas. Furthermore, we emphasize the importance of diversity-awareness in smartphone sensing, and provide insights and future directions for researchers in ubiquitous and mobile computing, and especially to new researchers who want to understand the basics of smartphone sensing research targeting the well-being of young adults.
Highlights
Smartphones have been rapidly evolving during the past decade due to advancement of technology in a multitude of disciplines such as hardware (CPU, GPU) [1], sensing [2], [3], computer vision [4]–[6], deep learning [7], [8], and human-computer interaction [9]–[11]
In this article, we focus on smartphone sensing research that has dealt with health and well-being, of young adults
In all the studies, even though the target variable belonged to one specific pillar, data belonging to other pillars have been collected using passive sensing and self-reports in order to find relationships with or to infer the target variable
Summary
Smartphones have been rapidly evolving during the past decade due to advancement of technology in a multitude of disciplines such as hardware (CPU, GPU) [1], sensing [2], [3], computer vision [4]–[6], deep learning [7], [8], and human-computer interaction [9]–[11]. We mapped person to P to represent the physical, mental, and social aspects of people in addition to attributes such as age, gender, and this mapping allows us to represent sensors and data sources in smartphone sensing literature as proxies to different pillars of data We use these pillars of data in the context of smartphone sensing as a framework throughout this article. Except for [67], most of the work in this domain still lacks diversity in user populations to provide diversity-related behavioral insights in terms of geographical location
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