Authors : R. Anand; R. Nithin Reddy; R. Sai Kiran; R. Om Sai Varshitha; Dr. Mohassin Ahmed

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/mr49amn5

Scribd : https://tinyurl.com/2sbedkae

DOI : https://doi.org/10.38124/ijisrt/26mar1295

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This work proposes a smart occupancy detection and activity recognition system for monitoring human presence in indoor environments. The system integrates Passive Infrared (PIR) sensors and Radio Frequency (RF) sensing to identify whether a room is occupied and to observe basic human activities. Sensor data is collected and analyzed using activity recognition techniques to provide real-time information about room usage. Such monitoring can be useful in environments like offices, hospitals, educational institutions, and residential buildings where efficient space utilization is important. Based on the detected occupancy status and activity patterns, the system can automatically manage electrical devices such as lighting, heating, ventilation, and other appliances. This approach helps ensure that devices operate only when required, thereby reducing unnecessary energy consumption and improving overall efficiency. Unlike conventional occupancy detection systems that mainly depend on PIR sensors or pressure-based methods and only detect simple presence, the proposed approach also examines changes in RF signals caused by human movement. This enables the system to detect occupancy and identify activity without requiring wearable devices, providing a convenient, non-intrusive, and privacyconscious solution for modern smart building applications.

Keywords : Smart Occupancy Detection, Radio Frequency (RF) Sensing, Internet of Things (IoT), Wireless Sensors, Energy Efficiency

References :

1. Tekler, Z.D.; Low, R.; Gunay, B.; Andersen, R.K.;   Blessing, L. A scalable Bluetooth Low Energy approach to identify occupancy patterns and profiles in office spaces. Build. Environ. 2020.
2. Ahmad, J.; Larijani, H.; Emmanuel, R.; Mannion, M.; Javed, A. Occupancy detection in non- residential buildings–A survey and novel privacy preserved occupancy monitoring solution.Appl.Comput. Inform. 2020.
3. Chen, Z.; Jiang, C.; Xie, L. Building occupancy estimation and detection: A review. Energy Build.  2018.
4. Ueno T, Sano F, Saeki O, Tsuji K. Effectiveness of an energy-consumption information system on energy savings in residential houses based on monitored data. Appl Energy 2006;83:166-83.
5. Nguyen TA, Aiello M. Energy intelligent buildings based on user activity: A survey. Energy Build 2013;56:244-57.
6. Marinakis V, Doukas H, Karakosta C, Psarras J. An integrated system for buildings’ energy-efficient automation: Application in the tertiary sector. Appl Energy 2013;101:6-14.