Environment Knowledge-Driven Generic Models to Detect Coughs From Audio Recordings.

Millions of people are dying due to respiratory diseases, such as COVID-19 and asthma, which are often characterized by some common symptoms, including coughing. Therefore, objective reporting of cough symptoms utilizing environment-adaptive machine-learning models with microphone sensing can directly contribute to respiratory disease diagnosis and patient care. In this work, we present three generic modeling approaches - , , and approaches considering three potential scenarios, i.

Design Factors of Longitudinal Smartphone-based Health Surveys.

Phone-based surveys are increasingly being used in healthcare settings to collect data from potentially large numbers of subjects, e.g., to evaluate their levels of satisfaction with medical providers, to study behaviors and trends of specific populations, and to track their health and wellness.

Visualization of Time-Series Sensor Data to Inform the Design of Just-In-Time Adaptive Stress Interventions.

We investigate needs, challenges, and opportunities in visualizing time-series sensor data on stress to inform the design of just-in-time adaptive interventions (JITAIs). We identify seven key challenges: massive volume and variety of data, complexity in identifying stressors, scalability of space, multifaceted relationship between stress and time, a need for representation at multiple granularities, interperson variability, and limited understanding of JITAI design requirements due to its novelty. We propose four new visualizations based on one million minutes of sensor data (n=70).

Estimating Drivers' Stress from GPS Traces.

Driving is known to be a daily stressor. Measurement of driver's stress in real-time can enable better stress management by increasing self-awareness. Recent advances in sensing technology has made it feasible to continuously assess driver's stress in real-time, but it requires equipping the driver with these sensors and/or instrumenting the car.