Introduction

In higher education, space utilisation plays a crucial role: insufficient usage means waste of resources, while overcrowding leads to capacity problems. For housing advisors, measuring attendance is essential to arrange schedules, classrooms, and facilities wisely.

At the same time, this must be done within the frameworks of privacy legislation (GDPR) in the Netherlands and Europe: students must not be profiled. The PLEQ anonymous occupancy sensor offers an innovative solution: it counts people anonymously, in real-time, using TOF technology (Time-of-Flight). In this blog, we explain how it works, the added value it offers for attendance and scheduling, and what to watch for during implementation.

1. What is the PLEQ anonymous occupancy sensor?

The PLEQ sensor is designed to monitor the use of spaces such as lecture halls, workspaces, and study locations without collecting individual personal data:

• Anonymous counting: no images, faces or personal data are stored. The sensor only registers amounts or changes in presence.

• Real-time occupancy data: the sensor continuously provides updates on how many individuals are in a space.

• Integration options: via APIs, dashboards, or connections to building management systems (BMS), data can be exported and used for analyses and decision-making.

• Data security & storage in the EU: data processing and storage occurs in strict environments within the EU, in accordance with GDPR requirements.

For advisors in higher education, this means you have reliable usage data, without GDPR risks, to support your housing advice.

2. Explanation of TOF technology (Time of Flight)

2.1 Basic principle

TOF sensors work by emitting a brief light or infrared pulse and measuring how long it takes for the reflection to return to the sensor. From the travel time of the pulse, the distance to objects or individuals can be determined. Wikipedia

In advanced TOF systems, a “depth map” is created: a distance value is registered per pixel or measurement point. Based on changes between frames, it can be established that individuals are moving or present. terabee.com

2.2 Advantages of TOF compared to classical sensors

• High accuracy & resolution: TOF can detect subtle changes in distance, making it easier to distinguish between objects and individuals. terabee.com

• Works in low light or darkness: the sensor is less sensitive to lighting conditions than traditional PIR sensors. terabee.com

• Privacy-friendly: because only depth data (distance) is registered, identification of individuals is impossible. terabee.com

• Real-time detection: changes can be observed almost instantly, which is useful for dynamic applications. Tofsensors

2.3 Technical limitations & conditions

• Reflections & multiple bounces: reflective surfaces or glass can cause disruptions in the measurement (light returning via multiple paths).

• Interference from sunlight / ambient light: direct sunlight or strong IR sources can affect the measurement.

• Limitations in angle and field of view: properly positioning the sensor (height, viewing angle, overlap) is essential to avoid blind spots. sites.ecse.rpi.edu

• Complexity in busy traffic flows: in situations with many people simultaneously, distinguishing individual movements becomes more complex, bringing a certain margin of error.

• Synchronization & multiplexing: if multiple TOF sensors are close to each other, they can affect one another, requiring them to be synchronized or operate at different frequencies.

In summary: TOF provides powerful capabilities but requires careful application and calibration.

3. How PLEQ uses TOF technology in the sensor for attendance measurement

3.1 From distance measurements to attendance registration

The PLEQ sensor processes incoming distance data and translates changes in presence (e.g., someone enters, leaves) into attendance statistics per minute or time interval. This allows it to register how many students are in a hall during a lecture period.

3.2 Aggregation & interpretation of attendance data

The PLEQ sensor measures real-time movements of individuals at the entrance of a space. Every in- or outflow movement is recorded as a positive or negative value. This raw data is processed directly on the sensor into compact count data, and then aggregated into usable insights. Attendance figures can thus be grouped based on different timing schedules, such as per minute, every five minutes, or every quarter hour.

Additionally, a clear overview of occupancy per room, floor, or building is created. For educational institutions, especially the connection with subject and course offerings is relevant: the system can calculate attendance rates per course, against the number of enrolled students. This reveals the absence rate per subject, crucial information for quality assurance, study guidance, and capacity planning.

The aggregated information is clearly displayed in dashboards, suitable for planners, teachers, and housing advisors. This provides not only figures but also direct applicability in decision-making.

3.3 Linking with schedules & planning

By linking the measurement data to the existing schedule of classes and activities, a powerful analysis tool is created. For example, you can analyze which rooms are structurally underused during scheduled educational activities. Patterns of absence can also become visible, such as lower attendance in the evenings or on specific weekdays.

The sensor data allows for optimising room allocations. Educational spaces can be better aligned with actual needs. Consider using smaller rooms in cases of structurally low attendance, or combining groups with complementary scheduling times.

Moreover, the system provides concrete insights that allow institutions to improve the efficiency of their space usage. Unnecessary use of large rooms leads to higher costs for heating, ventilation, and cleaning. By identifying such insights early, these costs can be limited.

3.4 Installation & calibration: points of attention

For reliable measurements, PLEQ sensors are placed at the entrance of the space, facing downwards. This positioning ensures that every person entering or leaving a room is accurately recorded. In larger halls or spaces with multiple entrances, multiple sensors can be deployed with overlapping measurement fields to exclude any blind spots.

To ensure measurement accuracy, periodic validation is advisable. This can be done by occasionally comparing manual counts with the automated data. This way, one maintains insight into the margin of error and can adjust if necessary.

Furthermore, it is important that during installation, consideration is given to the specific room shape, ceiling height, and possible obstacles such as lighting, projectors, or cable ducts. The sensors must be calibrated correctly for their environment, so that their field of observation is optimally utilised and the reliability of the counts is guaranteed.

4. Added value for higher education: measuring attendance & schedule optimisation

4.1 Reliable attendance data as a basis for informed policy

One of the biggest advantages of the PLEQ sensor is the ability to collect objective data on educational attendance. For housing advisors, this means that decisions no longer have to be based on assumptions or incidental observations. You can see exactly which classes consistently have low attendance and respond directly. For example, by reassigning the space, adjusting the schedule, or critically reviewing the number of planned contact hours. This leads to better-grounded choices and prevents unnecessary waste of square meters.

4.2 Cost savings & increased operational efficiency

Room usage inevitably incurs operational costs: heating, ventilation, lighting, and cleaning. When classes are poorly attended, these costs unnecessarily rise. By deploying PLEQ sensors, you gain insight into this mismatch between attendance and space utilisation. Consequently, you can decide to close underutilised rooms or allocate smaller spaces. This results in structural savings on operating costs without compromising educational quality.

4.3 Increased usage intensity of available spaces

With accurate attendance data, educational spaces can be better aligned with real demand. Instead of planning based on enrolments, actual attendance can be used as a starting point. This creates room for consolidating subjects with complementary scheduling times in shared classrooms, thereby increasing the average occupancy rate. This higher usage intensity leads to more efficient use of space, which is especially advantageous in busy campus locations.

4.4 Transparency & accountability within the organisation

The availability of reliable usage reports contributes to greater transparency in decision-making. Faculties and departments can substantiate their space claims or needs with concrete figures. This also provides objective accountability for administrative discussions or budget rounds: you can demonstrate where underutilisation occurs or where expansion is needed. This makes the conversation about space allocation more factual and less sensitive to internal interests or opinions.

4.5 Increased student satisfaction

Spaces that are aligned with actual usage contribute to a more positive experience for students. Classes in overcrowded halls or spaces that feel too large for the attendance can be frustrating. By assigning classrooms more accurately based on attendance data, a better learning environment is created, where comfort, acoustics, and group size are better balanced. Students generally appreciate this with higher satisfaction scores and fewer complaints about facilities.

4.6 Insight into deviation patterns and cause analysis

With sufficient historical data, educational institutions can analyse structural deviations in attendance patterns. This allows you to see if certain times of day, subjects, or teachers realise lower attendance. These insights can then be linked to underlying causes, such as curriculum structure, scheduling at inconvenient times, or the type of subject content. This makes it possible to take targeted improvement measures focused on motivation, planning, or content adjustments.

5. Comparison with alternative technologies

6. Implementation approach & points of attention

6.1 Pilot phase: start small with targeted evaluation

The implementation of occupancy sensors ideally begins with a pilot in a limited number of lecture halls or educational spaces. In this phase, sensors are installed at the entrances of selected classrooms and their operation is carefully observed. The attendance figures from the sensors can be compared with manual counts to check accuracy. At the same time, settings such as detection height and time intervals are calibrated. This pilot allows for a thorough evaluation of the technology, measurement reliability, and usability before large-scale deployment occurs.

6.2 Stakeholder involvement: creating support from the start

A successful implementation requires active involvement from all relevant stakeholders. This includes ICT teams for technical implementation, privacy officers for GDPR compliance, and educational coordinators and teachers who will use the results. Students must also be informed about the purpose and operation of the measurements. It is essential to communicate clearly that the sensors do not collect personal data and that the measurements occur completely anonymously. This transparency is crucial for trust and acceptance.

6.3 Technical prerequisites: the basis for a stable system

For the sensor infrastructure to function well, several technical prerequisites are essential. There must be sufficient network connectivity — depending on the chosen solution via LoRaWAN, WiFi, or Ethernet. Additionally, updates and firmware management must be centrally organised to ensure safety and performance. Also, periodic maintenance and checks are important, for example, to keep the sensors clean and check mounting points. This technical basis is needed to guarantee reliability and continuity in the long term.

6.4 Scalability & linking with space automation

A major advantage of the PLEQ solution is its modular scalability. Once the pilot phase is successfully completed, additional sensors can easily be rolled out to other parts of the building or campus. The system is also prepared for integration with building management systems (BMS) or scheduling software. This can support automatic space management, for example, by switching off spaces when not in use or displaying real-time occupancy to students. These connections make the system not only analytically powerful but also practically usable in daily management.

6.5 Validation & auditing: quality control of measurements

To maintain trust in the measurement data, it is important to periodically conduct audits and validations. During this process, the results of automated measurements are compared with manual counts or observations. Any deviations can be analysed and used to adjust settings or identify technical causes. This continuous quality assurance ensures that the measurement data remains reliable as a basis for policy and scheduling — a necessary step for data-driven housing in higher education.

Conclusion

The PLEQ anonymous occupancy sensor based on TOF technology offers a powerful, privacy-conscious way to map attendance and space utilisation in higher education. For housing advisors, this means: objective data for smart scheduling, cost savings, and better-aligned facilities. With careful implementation, including pilot, calibration, and validation, this can be a valuable pillar of a data-driven housing strategy.

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