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.
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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