gForcePro+ EMG Armband
$ 0
The gForcePro+ EMG armband by OYMotion captures muscle signals and motion data to translate human gestures into real-time inputs for training dexterous robotic systems and AI models.
Available on backorder
Specifications and details:
| Type | EMG + IMU Hand Capture |
|---|---|
| Availability | In production |
| Manufacturer | OYMotion Technology |
| Nationality | China |
| Website | https://www.oymotion.com/ |
| Sensing Technology | 8-Channel EMG + 9-Axis IMU Fusion |
| Gesture Recognition | Up to 16 User-Defined Gestures |
| Data Output | Real-Time EMG and IMU Raw Data |
| Motion Tracking | Quaternion and Euler Angle Support |
| Sampling Rate | Up to 1 kHz (8-bit) / 500 Hz (12-bit) |
| Connectivity | Bluetooth BLE 4.0 Wireless |
| Power Consumption | Ultra-Low (< 0.1W) |
| Processor | ARM Cortex-M4 Embedded |
| Platform Support | Windows, Android, Unity3D, Arduino |
| SDK/API Access | Open SDK with Custom Algorithm Support |
| User Feedback | LED Indicators + Vibration Feedback |
| Integration | Compatible with MCU and Robotics Systems |
| Electrode Type | Medical-Grade Dry EMG Electrodes |
| Wearability | Lightweight, Wireless Armband Design |
| Mobile App Support | gForceAPP for Gesture Training and Calibration |
| Development Resources | Open-Source Projects and Documentation Available |
| Sensor Components | Accelerometer, Gyroscope, Magnetometer (3-Axis Each) |
Description
The OYMotion Technologies gForcePro+ EMG armband enables intuitive human-to-machine interaction by translating muscle activity into digital commands. It captures subtle forearm signals and motion patterns, allowing systems to understand user intent without cameras or external tracking. As a result, developers can build responsive control pipelines for humanoid hands and robotic systems that closely follow natural human gestures. Moreover, the device supports real-time data streaming, which makes it suitable for continuous learning workflows and interactive AI training environments.
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In addition, the armband simplifies data collection for dexterous manipulation tasks by combining gesture recognition with motion awareness in a single wearable interface. It enables fast iteration during training, since users can demonstrate actions directly instead of relying on pre-recorded datasets. Consequently, teams can accelerate the development of foundation models that require high-quality human demonstrations. At the same time, its wireless and lightweight design encourages longer sessions and more natural movement, which improves dataset diversity and realism.
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Website: https://www.opendroids.com/data-collection-glove
