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 …

Modeling User Context from Smartphone Data for Recognition of Health Status

RM Karanth, MS Guyer, NL Twilley… - 2019 Systems and …, 2019 - ieeexplore.ieee.org
Recent advances in sensing technology have made it possible to monitor how behavioral
systems unfold in people's natural settings by leveraging sensors embedded in personal …

Automated mobile sensing strategies generation for human behaviour understanding

N Gao, Z Yu, C Yu, Y Wang, FD Salim, Y Shi - arXiv preprint arXiv …, 2023 - arxiv.org
Mobile sensing plays a crucial role in generating digital traces to understand human daily
lives. However, studying behaviours like mood or sleep quality in smartphone users requires …

Leveraging collaborative-filtering for personalized behavior modeling: a case study of depression detection among college students

X Xu, P Chikersal, JM Dutcher, YS Sefidgar… - Proceedings of the …, 2021 - dl.acm.org
The prevalence of mobile phones and wearable devices enables the passive capturing and
modeling of human behavior at an unprecedented resolution and scale. Past research has …

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 …

[PDF][PDF] Multimodal Behavioral Sensing for Precision Mental Health Care

P Chikersal - 2023 - kilthub.cmu.edu
Mental health disorders are increasing in occurrence. They are the largest cause of disability
worldwide and the strongest predictor of suicide. Despite their prevalence, the majority of …

GLOBEM: cross-dataset generalization of longitudinal human behavior modeling

X Xu, X Liu, H Zhang, W Wang, S Nepal… - Proceedings of the …, 2023 - dl.acm.org
There is a growing body of research revealing that longitudinal passive sensing data from
smartphones and wearable devices can capture daily behavior signals for human behavior …

[HTML][HTML] Software architecture patterns for extending sensing capabilities and data formatting in mobile sensing

JE Bardram - Sensors, 2022 - mdpi.com
Mobile sensing—that is, the ability to unobtrusively collect sensor data from built-in phone
and attached wearable sensors—have proven to be a powerful approach to understanding …

Multi-modal data collection for measuring health, behavior, and living environment of large-scale participant cohorts: Conceptual framework and findings from …

C Wu, H Fritz, Z Nagy, JP Maestre, E Thomaz… - arXiv preprint arXiv …, 2020 - arxiv.org
As mobile technologies become ever more sensor-rich, portable, and ubiquitous, data
captured by smart devices are lending rich insights into users' daily lives with …

Inferring human behavior using mobile and wearable devices

J Favela - Proceedings of the 23rd Brazillian symposium on …, 2017 - dl.acm.org
Mobile, wearable, and ambient sensing is making possible the inference of activities and
behavioral patterns of individuals and populations. This data-driven approach to discovery …