Peer-indexed preprint, applied ML systems, and an active cohort with MIT Critical Data × NSRI 2026.
High school students often experience reduced productivity during study sessions due to environmental distractions — noise, poor lighting, temperature fluctuations — and unmanaged emotional states. Commercial affective computing systems are typically expensive and inaccessible. This study develops and evaluates a low-cost Raspberry Pi-based IoT desk assistant that monitors ambient conditions and delivers real-time visual feedback to enhance emotional awareness and focus. The system integrates multiple sensors with a custom animated dashboard, applying emotional modeling, sensor calibration, and UI/UX design principles to promote sustained productivity.
Read Full Preprint at viXraSelected from a competitive applicant pool for guided research with mentorship from researchers in data science and analytics. Participating in weekly seminars and collaborative discussions on real-world data challenges across the 2026 cohort.