What Is the Learning Period?
The learning period is the initial 4–6 weeks after InnerSpace is deployed. During this time, the system’s algorithms analyze Wi-Fi signal patterns in your space, deduplicate devices, and fine-tune occupancy accuracy.
Why Is It Necessary?
Unlike sensor-based solutions, InnerSpace leverages existing Wi-Fi networks to detect presence. People often carry multiple devices, and without a learning phase, the system could overcount occupants. The learning period helps the algorithm:
Identify unique individuals rather than just counting devices.
Adjust to Wi-Fi signal variations caused by floor layouts and infrastructure.
Improve long-term accuracy by refining data over time.
How InnerSpace Uses Wi-Fi Data
Captures MAC addresses and RSSI data from devices connecting to Wi-Fi.
Uses machine learning to recognize patterns in device movement.
Assigns multiple devices to a single individual, avoiding duplicate counts.
What to Expect During the Learning Period
Weeks 1–2: Initial occupancy counts available but may fluctuate.
Weeks 2–4: System refines data, improving accuracy.
Weeks 4–6: Stabilized, highly accurate insights ready for decision-making.
How Long Does It Take?
The learning period varies based on traffic volume:
Busy spaces: ~2–4 weeks.
Lower-traffic spaces: Closer to 6 weeks.
Why It’s Worth It
Ensures precise occupancy data for space planning.
Helps right-size real estate and optimize layouts confidently.
Unlocks advanced insights beyond just people counts.
The learning period is a short-term process that ensures long-term, reliable analytics—giving you the data-driven clarity you need to manage your workplace effectively.