Bewell: A smartphone application to monitor, model and promote wellbeing

By Nicholas Donald Lane, Mashfiqui Mohammod, Mu Lin, Xiaochao Yang, Hong Lu, Shahid Ali, Afsaneh Doryab, E. Berke, Tanzeem Choudhury, Andrew T. Campbell. International Conference on Pervasive Computing Technologies for Healthcare, 2011

469 citations recorded by the source corpus.

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.

Subfields: Electrical and Electronic Engineering, General Health Professions, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/158dad9eaa0461e6e5d404ea91de7a547640cb7d

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In Electrical and Electronic Engineering

In General Health Professions

In Human-Computer Interaction

Citations in the corpus

Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing (2018, methodology): “The StudentLife sensing app is built based on our prior sensing work [1, 46, 74, 75] for Android.”
StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones (2014, methodology): “We build on our prior work on the BeWell App [27] to provide a framework for automatic sensing in StudentLife.”
Smart Tourist - Passive Mobility Tracking Through Mobile Application (2014, background): “To this end, mobile crowd sensing enables applications to continuously sense relevant sensor data for applications like intelligent transport systems [4], personal healthcare systems [5], etc. to process data to provide information.”
Fusion of Smartphone Motion Sensors for Physical Activity Recognition (2014, unknown): “Physical activity recognition using wearable sensors has enabled the scientific community to develop novel applications, especially in the area of healthcare and assisted living [1,2].”
Using Smartphones to Collect Behavioral Data in Psychological Science: Opportunities, Practical Considerations, and Challenges (2016, unknown): “Social-interaction behaviors include initiated and received communications (via call and text logs; Boase & Ling, 2013; Chittaranjan, Blom, & GaticaPerez, 2011; Kobayashi & Boase, 2012), ambient conversation (via microphone; Lane et al., 2011; Rabbi, Ali, Choudhury, & Berke, 2011), speaking rates an”
Activity recognition with smartphone sensors (2014, methodology): “Other classification methods used in activity recognition with mobile sensors include Gaussian Mixture Models[48, 49], Artificial Neural Networks (ANN)[50-52], Naive Bayes (NB)[53], Rule-Based classifier[54], and a fuzzy inference system[55].”
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