Self Tracking

Self-tracking, using wearable sensors and mobile devices to monitor various aspects of health and behavior, aims to provide personalized insights for improved well-being and clinical decision-making. Current research focuses on developing robust algorithms, including large language models and part-based object detection frameworks, to analyze the often noisy and heterogeneous data generated by these systems, improving accuracy and reducing errors in vital sign monitoring and activity prediction. This work is significant because it facilitates the development of more effective human-AI collaborative tools for healthcare professionals and empowers individuals with data-driven strategies for behavior change, ultimately impacting both clinical practice and public health initiatives.