BeWell: A Smartphone Application to Monitor, Model and Promote Wellbeing

Abstract

Abstract-A key challenge for mobile health is to develop new technology that can assist individuals in maintaining a healthy lifestyle by keeping track of their everyday behaviors. Smartphones embedded with a wide variety of sensors are enabling a new generation of personal health applications that can actively monitor, model and promote wellbeing. Automated wellbeing tracking systems available so far have focused on physical fitness and sleep and often require external non-phone based sensors. In this work, we take a step towards a more comprehensive smartphone based system that can track activities that impact physical, social, and mental wellbeing namely, sleep, physical activity, and social interactions and provides intelligent feedback to promote better health. We present the design, implementation and evaluation of BeWell, an automated wellbeing app for the Android smartphones and demonstrate its feasibility in monitoring multi-dimensional wellbeing. By providing a more complete picture of health, BeWell has the potential to empower individuals to improve their overall wellbeing and identify any early signs of decline.

Efficient Privacy-Preserving Stream Aggregation in Mobile Sensing with Low Aggregation Error

“…These data can be aggregated to obtain the traffic pattern and pollution map. For another example, the average amount of exercise (which can be measured by motion sensors on smartphones [3]) that people do in every day can be used to infer public health conditions.…”

“…Mobile sensing exploits the data contributed by mobile users (via the mobile devices they carry) to infer rich information about people (e.g., health, activity, and social event) and their surrounding (e.g., pollution and weather). Applications of mobile sensing include traffic monitoring [1], environmental monitoring [2], healthcare [3], etc.…”

Towards a fuzzy-based multi-classifier selection module for activity recognition applications

“…One of the works that do address this issue is the one from Lane et al [6], which have designed BeWell, an application in which the embedded accelerometer, microphone and GPS receiver are used to recognize driving, stationary, running and walking activities, analyzing the RAM and CPU load of the application, as well as its impact on power consumption. Chu et al [7] have developed Kobe: a tool that performs profiling and optimization of mobile embedded classifiers to achieve an optimal energy-latency-accuracy tradeoff.…”

Causal Models for Well-Being

“…While Lane et al focus on informing the user of the application through a relatively simple feedback loop, van Sinderen et al use context reasoning and ECA rules to directly interact with the environment. As is the case with Salber et al [86], both Lane et al [59] and van Sinderen et al [121] do not clarify how their methods are used to improve software quality, reduce errors, and reduce development time. Domain knowledge and domain experts are mentioned in neither of the papers.…”

“…Lane et al [59] describe the architecture of a context-aware wellbeing system. The BeWell system consists of a smartphone and an infrastructure part, the smartphone elements providing means for sensing and providing the user with feedback, the infrastructure hosting a web portal and performing calculations on raw data.…”