Understanding College Students’ Phone Call Behaviors Towards a Sustainable Mobile Health and Well-Being Solution

During the transition from high school to on-campus college life, students leave home and start facing enormous life changes, including meeting new people, taking on more responsibilities, being away from the family, and dealing with academic challenges. These changes lead to an elevation of stress and anxiety, affecting students’ health and well-being. With the help of smartphones and their rich collection of sensors, we can continuously moni tor various factors that affect students’ behavioral patterns, such as communication behaviors associated with their health, well-being, and academic success. In this work, we try to assess college students’ communication patterns (in terms of phone call duration and frequency) that vary across various geographical contexts (e.g., dormitories, class buildings, dining halls) during different times (e.g., epochs of a day, days of a week) using visualization techniques. The findings from this work will help foster the design and delivery of smartphone-based health interventions, thus helping the students adapt to the changes in life.Durante la transición de la escuela secundaria a la vida universitaria en el campus, un estudiante deja su casa y empieza a enfrentarse a enormes cambios en su vida, como cono cer gente nueva, mayores responsabilidades, estar lejos de la familia y retos académicos. Estos cambios provocan un aumento del estrés y la ansiedad, lo que afecta a la salud y el bienestar del estudiante. Con la ayuda de los smartphones y su enriquecida colección de sensores, podemos monitorizar continuamente varios factores que afectan a los patrones de comportamiento de los estudiantes, como las conductas de comunicación asociadas a su salud, bienestar y éxito académico. En este trabajo tratamos de evaluar los patrones de comunicación de los estudian tes universitarios (en términos de duración y frecuencia de las llamadas telefónicas) que varían a través de varios contextos geográficos (por ejemplo, dormitorios, clases, comedores) durante diferentes momentos (por ejemplo, épocas de un día, días de una semana) utilizando técnicas de visualización. Los resultados de este trabajo ayudarán a fomentar el diseño y la realización de intervenciones sanitarias basadas en los teléfonos inteligentes; de este modo, se ayudará a los estudiantes a adaptarse a los distintos cambios en sus vidas

Transfer Learning to Detect COVID-19 Coughs with Incremental Addition of Patient Coughs to Healthy People's Cough Detection Models

Millions of people have died worldwide from COVID-19. In addition to its high death toll, COVID-19 has led to unbearable suffering for individuals and a huge global burden to the healthcare sector. Therefore, researchers have been trying to develop tools to detect symptoms of this human-transmissible disease remotely to control its rapid spread. Coughing is one of the common symptoms that researchers have been trying to detect objectively from smartphone microphone-sensing. While most of the approaches to detect and track cough symptoms rely on machine learning models developed from a large amount of patient data, this is not possible at the early stage of an outbreak. In this work, we present an incremental transfer learning approach that leverages the relationship between healthy peoples' coughs and COVID-19 patients' coughs to detect COVID-19 coughs with reasonable accuracy using a pre-trained healthy cough detection model and a relatively small set of patient coughs, reducing the need for large patient dataset to train the model. This type of model can be a game changer in detecting the onset of a novel respiratory virus.Comment: This paper has been accepted to publish at EAI International Conference on Wireless Mobile Communication and Healthcare (MobiHealth'23

Probabilistic Matching: Causal Inference under Measurement Errors

The abundance of data produced daily from large variety of sources has boosted the need of novel approaches on causal inference analysis from observational data. Observational data often contain noisy or missing entries. Moreover, causal inference studies may require unobserved high-level information which needs to be inferred from other observed attributes. In such cases, inaccuracies of the applied inference methods will result in noisy outputs. In this study, we propose a novel approach for causal inference when one or more key variables are noisy. Our method utilizes the knowledge about the uncertainty of the real values of key variables in order to reduce the bias induced by noisy measurements. We evaluate our approach in comparison with existing methods both on simulated and real scenarios and we demonstrate that our method reduces the bias and avoids false causal inference conclusions in most cases.Comment: In Proceedings of International Joint Conference Of Neural Networks (IJCNN) 201

Smartphone and Bluetooth Smart Sensor Usage in IoT Applications

Bluetooth Low Energy is an interesting short-range radio technology that could be used for connecting tiny devices into the Internet of Things (IoT) through gateways or cellular networks. For example, they are widely used in various contexts, from building and home automation to wearables. This paper proposes a method to improve the use of smartphones with a smart wireless sensor network acquisition system through Bluetooth Low Energy (BLE). A new BLE Smart Sensor, which acquires environmental data, was designed and calibration methods were performed. A detailed deviation is calculated between reference sensor and sensor node. The data obtained from laboratory experiments were used to evaluate battery life of the node. An Android application for devices such as Smartphones and Tablets can be used to collect data from a smart sensor, which becomes more accurate