Kobiljon Toshnazarov

EasyTrack is a meticulously crafted software solution that addresses the increasing demand for large-scale longitudinal data collection in mHealth applications. It serves the needs of the academic and medical communities by offering a versatile platform capable of accommodating diverse participant populations and their associated sensor data. EasyTrack stands out for its general-purpose functionality, making it adaptable for a wide range of mHealth studies. Additionally, it incorporates a robust data quality (DQ) monitoring mechanism, featuring a user-friendly dashboard and automated problem-detection routines that promptly alert researchers to potential data discrepancies. Furthermore, EasyTrack ensures seamless interoperability with existing data collection platforms.

Stress stimulates physiological responses such as heart activity changes and sweat, which can be detected using wearables like smartwatches and chest sensors. Moreover, smartphones give access to rich situational contexts and allow stress detection research to move toward uncontrolled environments. Our methods incorporate a collection of situational contexts and physiological sensing to accurately predict daily life stress, which is further used for digital therapeutics (DTx), such as timely intervention suggestions to prevent health complications by managing stress levels. In the future, alongside the health benefits, this research direction can help individuals optimize their daily stress levels and enhance productivity by controlling stress levels.

A workout tracking platform with the back-end implementation on Python and gRPC and mobile applications on Dart and Flutter.

I implemented the microservice architecture to separate frequently accessed services from high resource-consuming services to provide better response times. I also added a new innovative feature for detecting workouts utilizing IMU sensors and NFC. The platform was containerized and launched in two instances to avoid active standby, and the database was built on PostgreSQL and deployed over AWS EC2.

I developed a cross-platform mobile application for Android and iOS called HARU, a self-monitoring application for improving mental well-being in cancer patients. The idea was provided by the Behavioral Psychology Lab at Yonsei University, South Korea.

The HARU program is an app-based cognitive behavioral therapy program developed to improve various psychological difficulties of cancer patients. The cognitive-behavioral therapy (CBT) is a treatment that aims to change the way people think and act. The advantage of CBT is that it takes place relatively quickly. Usually, it is done directly face-to-face with a therapist for about 5-10 months, around 50 minutes. We enabled the treatment through an app so that people diagnosed with cancer can access treatment quickly and simply.

I teach at the Faculty of the Department of Software Engineering at New Uzbekistan University, covering the following courses:

• Artificial Intelligence (CS411)
• Object Oriented Programming (CS211)
• Graduation Project (GRAD496)