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Health, Healthcare, and Wellbeing / Passive Sensing, Behavior Modeling, and Mental State Prediction

Missing behavioral data reduces accuracy in depression prediction.

lightbulbPractical problemPassive Sensing, Behavior Modeling, and Mental State Prediction

Missing behavioral data reduces accuracy in depression prediction.

UbiComp '24Imputation Strategies for Longitudinal Behavioral Studies: Predicting Depression Using GLOBEM Datasets

Similar questions

lightbulbPractical problemPassive Sensing, Behavior Modeling, and Mental State Prediction

Depression in perimenopausal women is often overlooked, leading to insufficient diagnosis and treatment.

UbiComp '24Smart Rings for Early Detection of Peri-menopausal Depression: A Research Protocol

lightbulbPractical problemPassive Sensing, Behavior Modeling, and Mental State Prediction

Users struggle to plan long-term based on existing short-term mental health prediction tools.

UbiComp '24A Long-Term Perspective on Planning the Future: The Effectiveness of Weekly Mental Health Prediction

lightbulbPractical problemPassive Sensing, Behavior Modeling, and Mental State Prediction

Depression diagnosis relies on subjective methods with high error rates and bias.

CHI '24MoodCapture: Depression Detection using In-the-Wild Smartphone Images

lightbulbPractical problemPassive Sensing, Behavior Modeling, and Mental State Prediction

Depression detection models generalize poorly across datasets, limiting their practical utility.

UbiComp '23GLOBEM: Cross-Dataset Generalization of Longitudinal Human Behavior Modeling

lightbulbPractical problemPassive Sensing, Behavior Modeling, and Mental State Prediction

Mental health management for nursing home residents is insufficient, and emotional fluctuations go unnoticed.

UbiComp '23Predicting and Analyzing Emotion of Elderly People in Care Facilities

lightbulbPractical problemPassive Sensing, Behavior Modeling, and Mental State Prediction

Mental health assessment relies on self-report, which is prone to bias and unsuitable for everyday environments.

CHI '23Smartphone-derived Virtual Keyboard Dynamics Coupled with Accelerometer Data as a Window into Understanding Brain Health

Related papers

MoodCapture: Depression Detection using In-the-Wild Smartphone Images

Subigya Nepal, Arvind Pillai, Weichen Wang

GLOBEM: Cross-Dataset Generalization of Longitudinal Human Behavior Modeling

Xuhai Xu, Xin Liu, Han Zhang

Smartphone-derived Virtual Keyboard Dynamics Coupled with Accelerometer Data as a Window into Understanding Brain Health

Emma Ning, Andrea T. Cladek, Mindy K. Ross

EmoGlass: an End-to-End AI-Enabled Wearable Platform for Enhancing Self-Awareness of Emotional Health

Zihan Yan, Yufei Wu, Yang Zhang

Semantic Gap in Predicting Mental Wellbeing through Passive Sensing

Vedant Das Swain, Victor Chen, Shrija Mishra