Designing robust models for behaviour prediction using sparse data from mobile sensing: a case study of office workers' availability for well-being interventions

S Kucukozer-Cavdar, T Taskaya-Temizel… - ACM Transactions on …, 2021 - dl.acm.org
Understanding in which circumstances office workers take rest breaks is important for
delivering effective mobile notifications and make inferences about their daily lifestyle, eg …

[PDF][PDF] How to predict mood? delving into features of smartphone-based data

D Becker, V Bremer, B Funk, J Asselbergs, H Riper… - 2016 - researchgate.net
Smartphones are increasingly utilized in society and enable scientists to record a wide
range of behavioral and environmental information. These information, referred to as …

Using smartphone app use and lagged-ensemble machine learning for the prediction of work fatigue and boredom

D Lekkas, GD Price, NC Jacobson - Computers in human behavior, 2022 - Elsevier
Intro As smartphone usage becomes increasingly prevalent in the workplace, the physical
and psychological implications of this behavior warrant consideration. Recent research has …

Mobile sensing: Leveraging machine learning for efficient human behavior modeling

EK Barrett, CM Fard, HN Katinas… - 2020 Systems and …, 2020 - ieeexplore.ieee.org
Smartphones can collect millions of data points from each of its users daily, contributing to a
significant change in how the healthcare community approaches health monitoring. This …

Context-aware probabilistic models for predicting future sedentary behaviors of smartphone users

Q He, EO Agu - Journal of Healthcare Informatics Research, 2022 - Springer
Sedentary behaviors are now prevalent as most modern jobs are done while seated.
However, such sedentary behaviors have been found to increase the risk of several ailments …

Smart phone sensing to examine effects of social interactions and non-sedentary work time on mood changes

A Matic, V Osmani, A Popleteev… - Modeling and Using …, 2011 - Springer
The typical approach taken by clinical studies examining the factors that affect mood is to
use questionnaires in order to record the activities that impact the mood. However, recording …

Towards a Safety Culture in Workplaces: Intelligent Rest Breaks and Social Support

W Zhao, J Cheng, T Shen, X Luo - 2023 IEEE Symposium …, 2023 - ieeexplore.ieee.org
Musculoskeletal disorders (MSDs) are pervasive in the workforce and constitute the single
largest category of work-related illness. The root cause for MSDs is complex. However, there …

[HTML][HTML] Unsupervised machine learning for developing personalised behaviour models using activity data

L Fiorini, F Cavallo, P Dario, A Eavis, P Caleb-Solly - Sensors, 2017 - mdpi.com
The goal of this study is to address two major issues that undermine the large scale
deployment of smart home sensing solutions in people's homes. These include the costs …

[HTML][HTML] Stress modelling and prediction in presence of scarce data

A Maxhuni, P Hernandez-Leal, LE Sucar… - Journal of biomedical …, 2016 - Elsevier
Objective Stress at work is a significant occupational health concern. Recent studies have
used various sensing modalities to model stress behaviour based on non-obtrusive data …

Predicting subjective measures of social anxiety from sparsely collected mobile sensor data

H Rashid, S Mendu, KE Daniel, ML Beltzer… - Proceedings of the …, 2020 - dl.acm.org
Exploiting the capabilities of smartphones for monitoring social anxiety shows promise for
advancing our ability to both identify indicators of and treat social anxiety in natural settings …