A Reproducible Stress Prediction Pipeline with Mobile Sensor Data
Recent efforts to predict stress in the wild using mobile technology have increased; however,
the field lacks a common pipeline for assessing the impact of factors such as label encoding …
the field lacks a common pipeline for assessing the impact of factors such as label encoding …
Personal llm agents: Insights and survey about the capability, efficiency and security
Stressor Type Matters!---Exploring Factors Influencing Cross-Dataset Generalizability of Physiological Stress Detection
Automatic stress detection using heart rate variability (HRV) features has gained significant
traction as it utilizes unobtrusive wearable sensors measuring signals like electrocardiogram …
traction as it utilizes unobtrusive wearable sensors measuring signals like electrocardiogram …
DiversityOne: A Multi-Country Smartphone Sensor Dataset for Everyday Life Behavior Modeling
Understanding everyday life behavior of young adults through personal devices, eg,
smartphones and smartwatches, is key for various applications, from enhancing the user …
smartphones and smartwatches, is key for various applications, from enhancing the user …
Keep Sensors in Check: Disentangling Country-Level Generalization Issues in Mobile Sensor-Based Models with Diversity Scores
Machine learning models trained with passive sensor data from mobile devices can be used
to perform various inferences pertaining to activity recognition, context awareness, and …
to perform various inferences pertaining to activity recognition, context awareness, and …
Inferring Mood-While-Eating with Smartphone Sensing and Community-Based Model Personalization
The interplay between mood and eating has been the subject of extensive research within
the fields of nutrition and behavioral science, indicating a strong connection between the …
the fields of nutrition and behavioral science, indicating a strong connection between the …
[HTML][HTML] Design Guidelines for Improving Mobile Sensing Data Collection: Prospective Mixed Methods Study
Background Machine learning models often use passively recorded sensor data streams as
inputs to train machine learning models that predict outcomes captured through ecological …
inputs to train machine learning models that predict outcomes captured through ecological …
Understanding the Social Context of Eating with Multimodal Smartphone Sensing: The Role of Country Diversity
Understanding the social context of eating is crucial for promoting healthy eating behaviors.
Multimodal smartphone sensor data could provide valuable insights into eating behavior …
Multimodal smartphone sensor data could provide valuable insights into eating behavior …
M3BAT: Unsupervised Domain Adaptation for Multimodal Mobile Sensing with Multi-Branch Adversarial Training
Over the years, multimodal mobile sensing has been used extensively for inferences
regarding health and well-being, behavior, and context. However, a significant challenge …
regarding health and well-being, behavior, and context. However, a significant challenge …