Visualizing College Students' Geo-Temporal Context-Varying Significant Phone Call Patterns

Students often face enormous changes and challenges in life when starting college education, such as meeting new people, more responsibilities in life, and being away from family and their comfort zones. These sudden changes often lead to an elevation of stress and anxiety, affecting a student's mental health, well-being, and academic progress. This situation can even get worse with the outbreak of a pandemic, such as COVID-19, due to lockdown and associated disruption of the academic calendar. In this work, we use different visualizations and statistical techniques to find various geographical places and temporal factors that affect students' phone call patterns (in terms of call duration and call frequency) to promote the design and delivery of future smartphone-based health interventions to help students coping with sudden changes in lifestyles. From our detailed analysis of an 18-month longitudinal study dataset collected from a cohort of 464 freshmen, we obtain insights on phone call pattern variations during different temporal contexts, e.g., epochs of a day, days of a week, and in various geographical contexts (i.e., places of interest).

Predicting Next Call Duration: A Future Direction to Promote Mental Health in the Age of Lockdown

When high school students leave their homes for a college education, they often face enormous changes and challenges in life, such as meeting new people, more responsibilities in life, and being away from family and their comfort zones. These sudden changes often lead to an elevation of stress and anxiety, affecting a student's health and well-being. Situations can even get worse in the age of global pandemics, such as COVID-19, when regular life and social activities are significantly disrupted due to lockdown or stay-at-home orders. Therefore, predicting phone call patterns (a measure of social engagement) based on various factors and activities of a person can be helpful to foster social engagement and promote health and well-being during sudden lifestyle changes. In this work, we investigate a cohort of 370 on-campus college students over three consecutive semesters and breaks between them to find various geo-temporal factors and activities that affect students' phone call behaviors and develop models that can predict the next call duration with a correlation of up to 0.89 between the actual and predicted duration using individual-level generalized linear models. Findings from this work can further be extended to other populations, and thereby, our findings will enable the design and delivery of new smartphone-based health interventions (guided feedback) to help people to adapt and cope up with situations that affect their lifestyle and social activities.

Effect of Noise on Generic Cough Models

Respiratory diseases, such as chronic obstructive pulmonary disease (COPD) and asthma, are two major reasons for people's death across the globe. In addition to these common inflammatory respiratory diseases, some human transmissible respiratory diseases, such as coronaviruses, cause a global pandemic. One major symptom of these inflammatory respiratory diseases is coughing. Identifying coughing using smartphone-microphone recordings is easily doable from a remote setup and can help physicians and researchers early guess a situation for an individual and a community. However, smartphone-microphone recordings can be affected by environmental noises and that can impact the performance of models that are developed to detect coughing from microphone recording. Thereby, in this work, we present a detailed analysis of noise impacts on cough detection models. We develop models using voluntary coughs and other background sounds obtained from three public datasets and test the performance of those models while detecting various types of coughs, including COPD and COVID-19, obtain from three separate datasets in the presence of background noises. © 2021 IEEE.

Deriving College Students' Phone Call Patterns to Improve Student Life

When high school students leave their homes for a college education, the students often face enormous changes and challenges in life, such as meeting new people, more responsibilities in life, and being away from family and their comfort zones. These sudden changes often lead to an elevation of stress and anxiety, affecting a student's mental health and well-being and academic progress. With the outbreak of a pandemic, such as COVID-19, this transition moment can worsen with frequent, long-lasting lockdown and associated academic disruption. To help this young population, researchers are increasingly relying on smartphones and wearables, such as smartwatches, to continuously monitor students' daily lives to identify various factors that can affect students' phone call patterns associated with their health and well-being and academic success. In this work, we use different visualizations and statistical techniques to find various geographical places and temporal factors that affect students' phone call patterns (in terms of phone call duration and frequency) to foster the design and delivery of future smartphone-based health interventions using predictive models;thereby, potentially helping students adjust to college life with or without the presence of an emergency, such as pandemic that adversely impacts academic calendar and student life. From our detailed analysis of an 18-month dataset collected from a cohort of 464 freshmen, we obtain insights on communication pattern variations during different temporal contexts, e.g., epochs of a day, days of a week, the parts of a semester, social events, and in various geographical contexts (i.e., places of interest). Finally, we also obtain a negative correlation of-0.29 between physical activity and phone call duration, which can help provide guided feedback to improve future health behaviors. © 2013 IEEE.