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Found 5 papers, 1 papers with code
Analyzing the contribution of different passively collected data to predict Stress and Depression
In this paper, we analyze the contribution of different passively collected sensor data types (WiFi, GPS, Social interaction, Phone Log, Physical Activity, Audio, and Academic features) to predict daily selfreport stress and PHQ-9 depression score.
On the use of Vision-Language models for Visual Sentiment Analysis: a study on CLIP
First, we test on WEBEmo and compare the CLIP-E architectures with state-of-the-art (SOTA) models and with CLIP Zero-Shot.
Predicting the impact of urban change in pedestrian and road safety
In this paper, by considering historical accident data and Street View images, we detail how to automatically predict the impact (increase or decrease) of urban interventions on accident incidence.
Explainable, automated urban interventions to improve pedestrian and vehicle safety
At the moment, urban mobility research and governmental initiatives are mostly focused on motor-related issues, e. g. the problems of congestion and pollution.
Predicting Driver Self-Reported Stress by Analyzing the Road Scene
Several studies have shown the relevance of biosignals in driver stress recognition.
