Gravity: AI Pose & Gesture Recognition Sensor with Custom Learning (I2C / UART)

Gravity: AI Pose & Gesture Recognition Sensor is an offline AI vision sensor that lets makers add custom static pose and hand-gesture recognition without collecting datasets or training AI models. Using the Windows software, users can quickly teach and assign IDs to up to 8 custom gestures and 8 custom full-body poses.

Once learned, actions are stored locally and the sensor can operate independently from the PC, sending recognition results to Arduino, ESP32, or micro:bit through I2C or UART. Gesture and pose recognition run as separate switchable modes, making the pose detection sensor a simple way to add personalized AI interaction to maker projects, interactive installations, and educational demos.

Add AI Pose Recognition—Without Training an AI Model

Adding custom pose recognition with a general-purpose AI vision board usually involves collecting and labeling data, training a model, converting it for the target hardware, and deploying an inference pipeline. AI Pose & Gesture Recognition Sensor already integrates the required pose-recognition capability, so makers can focus on the interaction itself instead of the AI development process.

In pose mode, the sensor detects 17 human body keypoints at approximately 10 FPS and supports up to 8 custom static poses stored locally. A learned pose can be assigned an ID and used as a trigger for lighting, displays, sound, robots, projection, or interactive installations.

Create Your Own Hand Gesture Controls

For close-range interaction, AI Pose & Gesture Recognition Sensor can learn up to 8 custom static hand gestures instead of limiting projects to a fixed gesture list. Hand mode tracks 21 keypoints with a recommended operating distance of around 0.5 m.

Custom gesture IDs can be used to start or stop a device, switch lighting effects, trigger animations, control a music project, or add personalized input to a desktop device or smart toy.

Gesture mode and full-body pose mode are separate and cannot run simultaneously; users can switch between them through the Windows software or supported communication commands.

Offline Recognition with No Cloud Dependency

Gesture and pose inference run locally on the sensor, so recognition does not require an internet connection or cloud AI service. After custom actions are learned, recognition results can be sent directly to the connected controller for local response.

This makes AI Pose & Gesture Recognition Sensor suitable for classroom demonstrations, standalone maker projects, interactive installations, smart devices, and environments where network access is unavailable or unnecessary.

Compact and Low Power for Embedded Projects

The 37 × 37 mm module is designed for compact maker devices and interactive installations. Typical operating current is approximately 40 mA at 3.3 V, while low-power mode can reduce sleep current to approximately 37 μA at 3.3 V.

A dedicated WAKEUP pin allows the main controller to place the sensor into low-power mode when recognition is not needed, making it more suitable for duty-cycled and battery-powered projects.

Built for Arduino, ESP32 and micro:bit Projects

AI Pose & Gesture Recognition Sensor provides I2C and UART communication together with a Gravity connector and 2.54 mm pin header for integration with common maker controllers. The DFRobot Arduino library provides access to recognition IDs, names, scores, bounding boxes, and hand/body keypoint data.

Arduino and ESP32 can be used directly with the available library and examples. micro:bit is supported through a compatible expansion board and MakeCode support.

For Best Recognition Results