Overview of context-sensitive technologies for well-being

Abstract

Today smart devices such as smartphones, smartwatches and activity trackers are widely available and accepted in most developed societies. These devices present a broad set of sensors capable of extracting detailed information about different situations of daily life, which, if used for good, have the potential to improve the quality of life not only for individuals but also for the society in general. One of the key areas where this type of information can help to improve the quality of life is in healthcare since it allows to monitor and infer the current level of well-being of the smart devices carriers. In this paper, some of the available literature about well-being sensing through context-aware data is reviewed. Also, the main types of mechanisms used in these studies are identified. These mechanisms are related to monitoring, generalization, inference, feedback, energy management and privacy. Furthermore, a description of the mechanisms used in each study is presented.

The Ethics of Digital Well-Being: A Thematic Review

“…Data privacy was an important concept for many of the social domains but has a specific importance in health and healthcare due to the sensitive nature of the data involved. The ethical risks surrounding use of personal and sensitive data are widely addressed (Ahn 2011;Freitas et al 2017;Lehavot et al 2012;Sinche et al 2017;Soraghan et al 2015). They range from the risks that the exposure of health-related information may pose for an individual's well-being due to real and perceived stigmatization (Mittelstadt 2017a), to concerns about the inference of sensitive information from seemingly benign (and in some cases public) datasets (Horvitz and Mulligan 2015).…”

“…Several human-computer interaction studies focus on the link between stress and individual well-being (Andrushevich et al 2017;Baras et al 2016;Freitas et al 2017;Garcia-Ceja et al 2016) and propose some form of automated measurement to infer an individual's psychological state (e.g., detecting levels of occupational stress from the accelerometer of a smartphone). Although some contributions highlight ethical issues such as privacy (Garcia-Ceja et al 2016), there is a notable gap concerning how the process of automated measurement could itself lead to lower levels of well-being.…”

Can Machines Read our Minds?

“…Social media platforms have been interested in the possibility of inferring private psychological traits from samples of users' behaviour for a while, as evidenced by a patent filed by Facebook in 2012, and subsequently granted in 2014, which 8 As one tangential remark, however, it is interesting to note that some studies in areas such as HCI and affective computing do note the importance of situational and contextual factors in inferring psychological traits and mental states (e.g. Baras 2016;Freitas 2017), and see developments in ubiquitous computing (e.g. IoT devices) as promising developments for improving our ability to accurately incorporate this type of contextualising data.…”

The Ethics of Digital Well-Being: A Thematic Review

“…Data privacy was an important concept for many of the social domains but has a specific importance in health and healthcare due to the sensitive nature of the data involved. The ethical risks surrounding use of personal and sensitive data are widely addressed (Ahn, 2011;Freitas et al, 2017;Lehavot et al, 2012;Sinche et al, 2017;Soraghan et al, 2015). They range from the risks that the exposure of health-related information may pose for an individual's well-being due to real and perceived stigmatization (Mittelstadt 2017b), to concerns about the inference of sensitive information from seemingly benign (and in some cases public) datasets (Horvitz & Mulligan, 2015).…”

“…5 This can be best illustrated through a critique of some of the reviewed publications that discuss stress management in employment and education. Several human-computer interaction studies focus on the link between stress and individual well-being (Andrushevich et al, 2017;Baras et al, 2016;Freitas et al, 2017;Garcia-Ceja et al, 2016) and propose some form of automated measurement to infer an individual's psychological state (e.g., detecting levels of occupational stress from the accelerometer of a smartphone). Although some contributions highlight ethical issues such as privacy (Garcia-Ceja et al, 2016), there is a notable gap concerning how the process of automated measurement could itself lead to lower levels of well-being.…”