Abstract
Background/Objective: High school students often face reduced productivity due to environmental factors (e.g., noise, poor lighting, temperature changes) and unmanaged emotional states during study sessions. Existing affective computing systems are typically expensive, complex, or inaccessible for personal use. This study develops and preliminarily evaluates a low-cost, Raspberry Pi-based IoT desk assistant that monitors ambient conditions and provides real-time visual feedback to promote emotional awareness and focus.
Methods: The prototype integrates a Raspberry Pi 4 and and Ardunio Uno R3 with DHT22 (temperature/ humidity), photoresistor (light), and microphone module (sound) sensors. NodeRed JavaScript scripts handle data collection, rule-based inference for alerts, and a simple web dashboard with gauges, done as the C++ script on the Arduino to gather sensor data and communicate via serial connection. Sensors were calibrated using C++. Privacy is ensured via local processing. Preliminary evaluation involved 8 high school peers (ages 15-17) testing the system over 1-2 weeks, using pre- and post-use self-reported focus surveys (1-10 Likert scale) and qualitative feedback via Google Forms.
Results: Calibration showed high reliability (temperature +/- 0.520°C, light 92% consistency, sound 88%). Par ticipants reported an average 25-30% increase in perceived focus under suboptimal conditions, with themes of improved distraction awareness and environmental adjustments.
Conclusions: This prototype demonstrates feasibility for accessible emotion-aware tools in student produc tivity enhancement, with potential scalability for educational settings. Future work includes machine learning integration and larger trials to address limitations (small sample, subjective measures).
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