Presence Multi-Sensor FP300

The "Absence Confirmation Period" feature is used to accurately determine the "no presence" state, avoiding false alarms caused by people temporarily leaving or slight movement. The radar needs to determine "no presence" for a complete cycle before confirming and triggering the "no presence" state. If someone re-enters or environmental disturbances occur during the cycle, the timer will restart from the next "no presence" state. The actual "no presence" trigger time is related to spatial environmental interference and micro-movement states, and is not strictly equal to the set cycle duration. In complex environments, it may take longer to determine "no presence". Shorter cycles ensure faster triggering of "no presence", while longer cycles improve the accuracy of "no presence" determination.

The device supports both Thread and Zigbee protocols, and can be added to Aqara Home or third-party supported Matter ecosystems.

The device supports wall mounting, desktop placement, or ceiling installation. It can be secured with adhesive, magnets, or expansion screws. For heights over 2 meters, using screws for secure installation is recommended.

It is recommended to face the device towards where people will be, as this makes it easier to detect subtle breathing movements and improves detection accuracy

Avoid installing near air conditioner vents, air purifiers, or fans. After installation, it needs time to learn the new environment. When there is significant interference, false alarms may occur.

Open the Aqara Home App, enter the homepage and click "+" in the upper right corner, and follow the prompts to complete the addition; for third-party Matter ecosystems, start "Add Device" in the corresponding app and scan the device Matter Code to complete.

Enter the Aqara Home App homepage, press the "+", then select the corresponding device, it will ask you to confirm the protocol, and select the Zigbee protocol, download the firmware and select a Hub to complete the addition.

Operating temperature -10℃ to 50℃, operating humidity 0 to 95% RH, non-condensing.

After adding the device, update to the latest version on the app's settings page.

In Zigbee mode, the AI Spatial Learning function can be used to reduce false triggers.

FP300 theoretical battery life: Zigbee 3 years, Matter 2 years * Test results from Aqara laboratory (at room temperature of 25°C, using default dual presence mode, with 4 hours of human presence per day, 240 environmental data reports per day, connected to a single Aqara Home platform). Actual battery life will be affected by multiple factors, including device operating mode, sensor configuration, trigger frequency, network conditions, usage environment, etc., and may differ from theoretical battery life.

· New Device Adaptation Period: When first used or after changing the installation environment, the device needs about 10 minutes (without people present) to learn the spatial environment. To speed up this process, you can execute "AI Space Background Learning" in the "On-Site Configuration" section in Zigbee mode. · Installation Position Optimization: Ensure there are no obstructions in front of the device, and position it to directly face the main area of activity. · Absense Confirmation Period Time Adjustment: Increase the "Absence Confirmation Period" to improve detection accuracy. · Sensitivity Settings: Higher sensitivity provides better detection of stationary people. Recommended settings by scenario: Low Sensitivity: Bathrooms, hallways and other short-stay areas (<20 minutes); Medium Sensitivity: Studies, living rooms and other daily-use areas; High Sensitivity: Bedrooms and other long-stay areas (>8 hours, such as rest/sleep).

· Update firmware: After installation, ensure you're using the latest firmware version. · Set detection range: In "On-Site Configuration" under Zigbee mode, customize detection distance to avoid interference from areas outside the intended detection range. · Re-learn the environment: Execute "AI Spacial Learning" in "On-Site Configurations" under Zigbee mode to allow the device to learn the environment. · Sensitivity settings: Refer to the following scenario-based sensitivity recommendations to adjust sensitivity and reduce interference. Low sensitivity: For bathrooms, hallways and other short-stay areas (<20 minutes); Medium sensitivity: For study rooms, living rooms and other daily use scenarios; High sensitivity: For bedrooms and other long-stay areas (>8 hours, such as resting/sleeping). · Interference sources: Slight movements of hanging lamps, curtain movement and other disturbances have similar characteristics to subtle human activities (such as breathing), and the device may need more time to identify these interferences.

For “Infrared Motion Detected” alerts (IR false positives): Infrared sensing may be sensitive to temperature changes, which may cause false triggers. For automations, we recommend using the “Presence / No Presence” status as the trigger condition instead of “Infrared Motion Detected.” For “Presence Detected” alerts (radar false positives in Dual Presence Mode): Adjust the detection range: In Zigbee mode, customize the detection distance under Real-Time Debugging to reduce interference from areas outside the intended detection zone. Lower the sensitivity: Lower sensitivity provides stronger resistance to interference. Choose the appropriate level for your scenario: Low sensitivity: Bathrooms, hallways, and other areas with short stays (under 20 minutes) Medium sensitivity: Studies, living rooms, and other everyday spaces High sensitivity: Bedrooms and other areas with long periods of occupancy (over 8 hours, such as resting or sleeping)

The FP300, as a low-power millimeter wave radar presence sensor, is limited by power consumption and algorithm complexity and does not include a dedicated pet filtering function (no weight-based filtering or pet filtering switch). You can adjust the radar angle to focus the detection range on human activity areas and avoid ground areas where pets are frequently active, which can reduce some false triggers from pets (medium to large pets may still trigger detection), but it cannot completely filter out pet activity. Pet filtering requires AI models with higher computing power and continuous sampling, typically implemented by mains-powered sensors. If you need precise pet filtering, we recommend choosing the Presence Sensor FP2, which is equipped with "AI High-Precision Human Recognition" and can effectively distinguish interference objects such as pets and robot vacuums.

The FP300, as a low-power millimeter wave radar presence sensor, is limited by power consumption and algorithm complexity and does not include a dedicated pet filtering function (no weight-based filtering or pet filtering switch). You can adjust the radar angle to focus the detection range on human activity areas and avoid ground areas where pets are frequently active, which can reduce some false triggers from pets (medium to large pets may still trigger detection), but it cannot completely filter out pet activity. Pet filtering requires AI models with higher computing power and continuous sampling, typically implemented by mains-powered sensors. If you need precise pet filtering, we recommend choosing the Presence Sensor FP2, which is equipped with "AI High-Precision Human Recognition" and can effectively distinguish interference objects such as pets and robot vacuums.