Indoor Occupant Counting Based on RF Backscattering

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Ithaca, New York
Project Term:
04/26/2018 - 05/25/2021

Technology Description:

Cornell University will develop an occupant monitoring system to enable more efficient control of HVAC systems in commercial buildings. The system is based on a combination of "active" radio frequency identification (RFID) readers and "passive" tags. Instead of requiring occupants to wear tags, the tags, as coordinated landmarks, will be distributed around a commercial area to enable an accurate occupancy count. When occupants, stationary or moving, are present among the RFID reader and multiple tags, their interference on the backscattering paths can be exploited to gain insights on the room population. The distributed tags will operate without the need for a power source. The system will employ efficient biomechanical models and inverse imaging algorithms to estimate the size, posture, and motion of the collected geometry and distinguish people from furniture and pets. Occupancy data is then sent to the building control system to manage the heating, cooling and air flow in order to maximize building energy efficiency while providing optimal human comfort.

Potential Impact:

If successful, SENSOR projects will dramatically reduce the amount of energy needed to effectively heat, cool, and ventilate buildings without sacrificing occupant comfort.


Lower electricity consumption by buildings eases strain on the grid, helping to improve resilience and reduce demand during peak hours, when the threat of blackouts is greatest.


Using significantly less energy could help reduce emissions attributed to power generation. In addition, improved interior air quality could help prevent negative effects on human health.


Buildings will require less energy to operate, reducing heating, cooling, and ventilation costs for businesses and families. In addition, better controlled ventilation may lead to improved indoor air quality (ensured by an accurate occupant count, and validated via widespread CO2 detection) may lead to improved worker productivity and academic performance.


ARPA-E Program Director:
Dr. Marina Sofos
Project Contact:
Dr. Edwin Kan
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