The Physical World Perception Model
At the heart of PhysenseAI is a Physical World Perception Model. Every stage — from sensing to behavioural prediction — is driven by one engine. The capability comes from the model itself, not from any single piece of hardware.
Signals → Events → Space → Behavior
The model progressively reconstructs raw sensing signals into an understanding of space.
Signals
Diverse sensing devices continuously capture environmental and activity signals.
Events
Discrete signals are reconstructed into real, continuous events.
Space
Relationships between people, spaces, events and time are established.
Behavior
Behavioural patterns are understood, and deviations and risks are anticipated.
Understanding space without images
In real living and care environments, vision-based approaches hit structural limits. Our environmental perception model doesn't depend on images, so it can go where cameras cannot.
- No images captured — privacy preserved
- Unaffected by light or occlusion
- Runs ambiently over the long term
- Covers visual blind spots
Four core capabilities of the model
Multi-Sensor Sensing
Fusing signals from diverse sensing devices into a unified spatial input.
Event Reconstruction
Reconstructing continuous events from discrete signals rather than handling each alert in isolation.
Spatial & Temporal Understanding
Building the relationships between people, spaces, events and time into a continuously updated spatial state.
Deviation & Prediction
Detecting deviation from historical norms and surfacing potential risk.
One core, mapped onto many physical spaces
The same spatial perception model acts as a core, connecting outward to different kinds of physical environments.