Recommended side-mounting height is 1.5–2 m, balancing detection accuracy for standing, seated, and low-activity scenarios. For more coverage, use side-mounting at a height of around 2 m. Recommended ceiling-mounting height is 3–4 m. For blind-spot-free detection, corner installation is preferred.

Yes, this product has an IPX5 protection rating, meaning it is splash-resistant and suitable for use in humid indoor environments such as bathrooms and kitchens. However, keep it away from open flames and areas exposed to direct high-temperature steam.

Corner installation makes full use of the 120° detection angle, eliminating long-distance blind spots on both sides that may occur with wall mounting. This provides more even detection coverage and reduces missed detections.

This is normal. The values stated in the manual represent the maximum detection range under ideal laboratory conditions. In actual use, mounting height, angle, wall/metal reflections, airflow, target posture, and obstructions can all affect the effective range of motion and presence detection, and unavoidable blind spots may exist.

FP400 uses millimeter-wave radar to precisely detect subtle human breathing movements. With proper configuration, it can reliably identify static presence. We recommend setting detection sensitivity to high and extending the Absence Confirmation Period to significantly reduce false negatives when stationary.

No. FP400 only supports occupancy detection (“Presence” / “No Presence”) and motion detection (“motion detected” / “all stationary”). It does not support fall detection or pose recognition and cannot determine whether a person is standing, sitting, or lying down.

Three solutions: ① Enable “AI Enhanced Person Detection” to automatically filter out non-human targets such as pets and robot vacuums (note: infants and small children may occasionally be misclassified as pets); ② Set areas where pets/robot vacuums frequently move as interference sources to ignore micro-movement signals in those areas; ③ Slightly reduce detection sensitivity to reduce environmental interference triggers.

“Ghost targets” are false detections caused by multipath echoes due to strong reflective surfaces such as walls, metal, mirrors, or glass. Setting reflective areas as “Boundary” type or readjusting the installation angle to avoid strong reflective surfaces can significantly improve accuracy. It is recommended to manually set Boundary regions in the app after installation.

Main causes and solutions: ① Micro-movement interference in the environment (curtain movement, fan rotation, air conditioning airflow, pet activity); set the interference source area as “Interference Source” type; ② “Ghost targets” remain; perform an “AI Spatial Learning” manually in the app; ③ Set reflective surface areas as “Boundary” type.

Main causes and solutions: ① Incorrect mounting height or angle—the device is not oriented toward the activity area; adjust the device orientation or lower its mounting height appropriately; ② Detection sensitivity is too low; open your app and increase the sensitivity; ③ The person is in a distant edge area or is heavily obstructed; consider reducing the Absence Confirmation Period to see if performance improves; ④ The person is in a near-motionless state (such as sleeping); ensure sensitivity is increased and appropriately extend the Absence Confirmation Period.

A small deviation is normal. When multiple people are present simultaneously, the person count may have a 1–3 person variance due to overlapping point clouds between multiple targets and/or people obstructing each other from the radar. Therefore, accuracy cannot be guaranteed 100%. In scenarios requiring high personnel count accuracy, we recommend referring only to the “Real-Time People Count” trend in your app and not using it as an exact counting reference.

When two targets (a person or a non-person) are within approximately 1 meter of each other horizontally, the point cloud may overlap, causing the algorithm to become unstable in classification (swapping the person/non-person labels) or causing the target to “stick” to the position of another target. We recommend setting densely occupied areas in your app as a larger “Monitoring Zone” rather than precise positioning.

When a target is within approximately 1 meter of another target, the algorithm pauses classification to prevent point cloud overlap. During this time, the target remains in a “pending confirmation” state (displayed as “…”). Normal operation resumes when the targets separate by more than 1 meter. Optimal detection performance occurs when targets are more than 1 meter apart.

This is a “ghosting” phenomenon, commonly seen near large glass, mirror, or metal surfaces. Signal reflections cause false targets to appear in locations where none exist. We recommend setting reflective areas as “Boundary” type, or performing an “AI Spatial Learning” manually in the app to adapt to the environment.

There are three reasons: ① Some third-party Matter platforms do not support adding or removing zones; we recommend completing all zone configurations in Aqara Home before syncing to third-party platforms; ② After syncing, you need to manually name the corresponding presence sensor in the third-party platform to distinguish the zones; ③ Try removing and re-adding the device in the third-party platform to sync the latest status.

① Monitoring Zone: main functional areas such as living room or bedroom, used to trigger automations for Presence/No Presence; ② Interference Source: objects with continuous micro-movements (e.g., fan, plant, pet activity zone); setting this area filters out its signal; ③ Entry/Exit: location of an entrance/exit; setting it improves detection speed for people entering or leaving; ④ Boundary: reflective surfaces such as walls, glass, or metal; setting this prevents ghosting and false targets.

Connecting to the Aqara ecosystem and accessing the device’s full functionality requires an Aqara hub. If the device is instead added directly to a third-party ecosystem by scanning its Matter code, it can only be used as a single presence sensor and a single light sensor; custom zone configuration and other advanced features are not available outside of Aqara Home.

Main differences: ① Local automation and response speed: the Zigbee protocol combined with an Aqara hub is typically faster; ② Cross-platform compatibility: Thread natively supports Matter, making it easier to integrate with third-party platforms such as Apple Home and Google Home.

First remove the device in the app, then press and hold the reset button for 5 seconds to reset the device. On the pairing screen, tap “Switch Protocol”, select the target protocol, and follow the instructions to complete pairing. Switching protocols will erase all settings, equivalent to a factory reset, so remember to save the current map as a template first.

① Complete all zone and parameter configurations within the Aqara Home app first, then synchronize to third-party platforms via the Matter pairing code; ② Some third-party platforms do not support dynamic zone modification; delete and re-add the device if needed; ③ Zone names must be manually modified in the third-party platform to match actual locations.

When adding a second or subsequent platform, scan the secondary Matter pairing code generated in the Aqara Home app, not the original QR code on the device or manual. Reusing the original code for repeated additions will cause pairing failure.

Troubleshooting steps: ① First confirm the device status is normal and the firmware is up to date in Aqara Home; ② Delete the device from the third-party platform and re-add it using the Matter pairing code; ③ Confirm the Thread protocol connection and ensure the Matter hub supports full functionality; ④ Complete all zone and parameter configurations in Aqara Home before synchronization.