565 citations · 588 across the 7 of their papers we have counts for
4 papers · 1 filter
Enhancing Performance and User Engagement in Everyday Stress Monitoring: A Context-Aware Active Reinforcement Learning Approach
Seyed Amir Hossein Aqajari, Ziyu Wang, Ali Tazarv +6
In today's fast-paced world, accurately monitoring stress levels is crucial. Sensor-based stress monitoring systems often need large datasets for training effective models. However…
Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings
Ali Tazarv, Sina Labbaf, Amir Rahmani +2
Most existing sensor-based monitoring frameworks presume that a large available labeled dataset is processed to train accurate detection models. However, in settings where personal…
Personalized Stress Monitoring using Wearable Sensors in Everyday Settings
Ali Tazarv, Sina Labbaf, Stephanie M. Reich +3
Since stress contributes to a broad range of mental and physical health problems, the objective assessment of stress is essential for behavioral and physiological studies. Although…
A Deep Learning Approach to Predict Blood Pressure from PPG Signals
Ali Tazarv, Marco Levorato
Blood Pressure (BP) is one of the four primary vital signs indicating the status of the body's vital (life-sustaining) functions. BP is difficult to continuously monitor using a sp…