Paper Info
Title
Designing Robust Models for Behaviour Prediction Using Sparse Data from Mobile Sensing: A Case Study of Office Workers’ Availability for Well-being Interventions
Abstract
AbstractUnderstanding in which circumstances office workers take rest breaks is important for delivering effective mobile notifications and make inferences about their daily lifestyle, e.g., whether they are active and/or have a sedentary life. Previous studies designed for office workers show the effectiveness of rest breaks for preventing work-related conditions. In this article, we propose a hybrid personalised model involving a kernel density estimation model and a generalised linear mixed model to model office workers’ available moments for rest breaks during working hours. We adopt the experience-based sampling method through which we collected office workers’ responses regarding their availability through a mobile application with contextual information extracted by means of the mobile phone sensors. The experiment lasted 10 workdays and involved 19 office workers with a total of 528 responses. Our results show that time, location, ringer mode, and activity are effective features for predicting office workers’ availability. Our method can address sparse sample issues for building individual predictive behavioural models based on limited and unbalanced data. In particular, the proposed method can be considered as a potential solution to the “cold-start problem,” i.e., the negative impact of the lack of individual data when a new application is installed.
Year
DOI
Venue
2021
10.1145/3458753
ACM Transactions on Computing for Healthcare
DocType
Volume
Issue
Journal
2
4
ISSN
Citations 
PageRank 
2691-1957
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Kursat Cagiltay121.74
Tugba Taskaya-Temizel200.34
Abhinav Mehrotra316911.69
Mirco Musolesi43365204.65
Tino P.51606155.22