Room Occupancy Sensors: Buyer’s Guide for IT Teams 2026

Room occupancy sensors solve the gap between booked availability and actual usage that costs organisations real money in wasted space. When a meeting room shows as booked but is actually empty for 40% of the booking duration, or when desks on a floor read as unoccupied while the floor is at 90% capacity, the booking data is lying. Occupancy sensors provide ground truth. This guide covers how the main sensor technologies work, what to look for in a deployment, and how to integrate sensor data with room booking and workplace management platforms.

Quick verdict

For most office environments, a combination of passive infrared (PIR) room sensors (to detect if a room is in use) and desk-level ultrasonic sensors (to count actual desk occupancy) provides the occupancy data needed to drive meaningful space decisions, at a cost that is justifiable against the space savings. Computer vision sensors provide significantly higher accuracy but add GDPR complexity that most IT teams prefer to avoid. Start with PIR for meeting rooms and measure what you have before expanding to desk-level sensing.

The business case for occupancy sensing

The core business case for room occupancy sensors is the gap between booked utilisation and actual utilisation. Industry-wide benchmarks suggest that meeting rooms are physically occupied for 40-60% of their booked time. Ghost bookings (meetings that did not happen), no-shows, early finishes, and over-booking create systematic inaccuracies in the booking data that mislead space planning decisions.

Occupancy sensor data, combined with booking system data, enables:

• Identifying rooms that are chronically overbooked relative to actual use (candidates for rebooking policy changes)
• Releasing ghost meeting rooms automatically when sensors confirm the room is empty 15 minutes after the booking start time
• Accurate floor and building occupancy data for real estate planning decisions
• Cleaning schedule optimisation – clean rooms based on actual use, not fixed schedules
• HVAC scheduling based on actual occupancy rather than assumed occupancy schedules

Sensor technology comparison

Passive infrared (PIR)

PIR sensors detect infrared radiation from warm bodies (people) moving within their field of view. They are binary sensors – they detect motion and presence, not headcount. PIR is the simplest and lowest-cost sensor technology for room-level occupancy detection.

Strengths: low cost (30-80 EUR per unit), no privacy concerns, easy installation (battery-powered options available), standard integration with building automation and signage platforms.

Limitations: poor at detecting stationary presence (a person sitting still for more than 10-15 minutes may not trigger the sensor), susceptible to false positives from HVAC airflow, and cannot count individuals.

Best used for: meeting room-level occupied/vacant detection. Not suitable for desk-level sensing or headcount-required applications.

Ultrasonic / microwave desk sensors

Purpose-built desk occupancy sensors use ultrasonic or microwave (radar) detection to sense presence at an individual workstation level. These sensors detect the subtle movements associated with a person working at a desk (typing, breathing, minor position adjustments) rather than requiring larger motion, making them more accurate than PIR for seated occupancy detection.

Strengths: high accuracy for seated presence detection, privacy-safe (no image data), battery or PoE powered, designed for under-desk or partition-mount installation.

Limitations: higher cost than PIR (60-150 EUR per desk), requires one sensor per workstation, and the volume of devices in a large open-plan deployment (500+ desks) requires careful network and management planning.

Best used for: desk utilisation measurement in open-plan areas, hot desk management, and precise floor-level headcount for HVAC optimisation.

Thermal imaging sensors

Thermal (infrared array) sensors detect heat signatures from people without capturing identifying visual information. Ceiling-mounted units can cover a 10-20 square metre area and count individuals within it. Privacy risk is low (no image data, silhouette resolution only), but higher than PIR or ultrasonic.

Strengths: area coverage (one sensor covers multiple desks or a room), headcount capability, no image data so lower GDPR risk than cameras.

Limitations: 200-600 EUR per unit, more complex GDPR analysis required than PIR (heat silhouettes may be considered biometric data in some interpretations), calibration required for accuracy.

Best used for: open-plan floor areas where individual desk sensors are cost-prohibitive, or meeting rooms where headcount matters for analytics.

Computer vision (camera-based)

AI-equipped cameras count individuals and can provide detailed flow analysis and demographic data. Significantly higher accuracy than any non-visual sensor technology.

Strengths: highest accuracy, zone-level flow analysis, rich analytics data.

Limitations: high cost (400-1,200 EUR per unit), GDPR DPIA mandatory, employee consultation/works council process required in most EU jurisdictions, significant privacy communication overhead. Most IT teams avoid computer vision sensors for internal occupancy sensing for these reasons.

Best used for: public-facing areas (building entry, lobby) where counting rather than identifying is the use case, and where privacy communication is simpler. Not recommended for general office deployment without thorough legal review.

Integration with room booking and workplace platforms

Integration depth varies significantly. Native integrations (the sensor platform speaks directly to the room booking system without middleware) are the cleanest operationally. API-based integrations require middleware development or an integration platform (Zapier, Make, custom code). Before selecting a sensor vendor, verify the integration path to your specific room booking and workplace platform – a sensor system without a clean integration to your booking data is only useful for space analytics, not for the auto-release of empty booked rooms use case.

Deployment planning

Starting with a pilot

Deploy sensors in a representative sample of rooms and floor areas before committing to a full rollout. A 10-15 room pilot in a single floor delivers enough data to validate sensor accuracy, integration reliability, and the practical conclusions that the data enables. Pilots also surface unexpected issues – network coverage gaps, calibration requirements, or data quality problems – before they affect a full deployment.

Network planning

Meeting room sensors typically use Wi-Fi, Zigbee, or LoRaWAN connectivity. Wi-Fi sensors require the building Wi-Fi network to cover all sensor locations reliably, including areas with historically weak coverage (basement meeting rooms, glass-partitioned rooms, server-adjacent areas). Budget for access point additions if needed. Zigbee and LoRaWAN sensors require gateway hardware but have better range and power efficiency than Wi-Fi for large deployments.

Data retention and GDPR

Even non-identifying occupancy data (room occupied: yes/no) may be subject to GDPR if it can be combined with booking system data to infer individual presence. Standard guidance is to retain occupancy data in aggregated form (hourly/daily averages by room) rather than timestamped event logs. Consult your DPO before configuring data retention periods on the sensor platform.

Total cost of ownership

Typical costs for a 50-room meeting room occupancy deployment:

• PIR sensors: 50 rooms x 60 EUR = 3,000 EUR hardware
• Installation (if required, varies by sensor): 0-50 EUR per room for battery-powered sensors
• Sensor platform subscription: 2-8 EUR per sensor per month (check current vendor pricing)
• Integration development: 0 (native integration) to 5,000-15,000 EUR (custom API integration)

The ROI case is typically made against one of two value streams: space consolidation (reducing real estate footprint based on actual utilisation data) or operational savings (HVAC and cleaning cost reduction based on occupancy schedules). Either can justify the sensor investment for deployments of 30+ rooms.

Bottom line

Room occupancy sensors provide the ground-truth data that makes room booking systems and space planning decisions actually accurate. PIR sensors for meeting rooms and ultrasonic sensors for desks are the right starting point for most organisations: proven technology, acceptable cost, no GDPR complexity, and clear integration paths to booking platforms. Start with a pilot deployment, validate the data quality and integration, then scale. For related guidance, see our best room booking systems guide and our overview of desk booking software.