Modern smart‑building systems regulate indoor climate using fixed sensors and predefined setpoints, yet occupants frequently report discomfort even when measured conditions appear acceptable. Existing systems capture only objective environmental parameters, while human comfort is subjective and influenced by personal and contextual factors. This project addresses that gap by developing a proof-of-concept system that combines distributed environmental sensors, a wearable device and a mobile comfort‑feedback application into a unified monitoring platform.
Project description
Our challenge was to explore whether a combination of distributed environmental sensors, wearable technology and direct user feedback could build a more complete picture of how people actually feel indoors. Not just what the thermostat thinks, but what the person sitting in the room experiences. By linking objective measurements to subjective comfort feedback. We wanted to find patterns that could one day help buildings adapt to the people inside them rather than the other way around.
Project result
To investigate this, a proof-of-concept system was designed and partially realized. The final concept consists of fixed sensor nodes, a wearable node, a Raspberry Pi central hub, Thread-based communication and a Home Assistant dashboard. The sensor nodes measure comfort-related data such as temperature, humidity, pressure, air quality, airflow, light intensity, light color, sound level, presence and battery status.
The project shows that this architecture is technically feasible as a foundation for a human comfort monitoring system. The prototype demonstrates how multiple comfort-related measurements can be collected, processed and displayed in one central platform. Home Assistant also provides a useful interface for viewing and exporting the measured data for later analysis.
Overall, the project demonstrates that distributed sensing can provide a stronger basis for studying human comfort than traditional fixed sensor systems alone. By combining fixed environmental measurements, wearable data and subjective feedback, the system creates a useful platform for future research into human-centered building control.
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Acknowledgements
The student project team (Bram, Daan, Fiore, Hidde, Joep and Shiv) would like to thank the teaching team of Fontys Minor BeCreative and Fontys Makersplayce for their support during the project. Special thanks to go our project coach Harold Benten for his guidance and supervision during the project. We are also thankful to the Built Environment faculty of TU/e and the Lectorate Distributed Sensor Systems of Fontys for providing this interesting project assignment.