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Learning period - What it is and why it matters
Learning period - What it is and why it matters

This article talks about the learning period and how it works.

Updated over a month ago

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.

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