World's first mass-produced
Physical AI Data Collection
Neuro Band
Using 8 channel neural electrophysiological acquisition and 2KHz high sampling rate, we precisely capture every millisecond-level change in human muscle activation, enabling robots to truly understand the underlying causes of human movements.
Technical Comparison
Why Neural Wristbands Are the Next Generation of Human-Computer Interaction
| Features | Traditional Solution | Our Solution |
|---|---|---|
| Signal Type | Visual/Optical Tracking (Surface Motion) | Neurophysiology + EMG (Deep Activation Logic) |
| Sampling Frequency | 30-120Hz | 2000Hz (2KHz) |
| Latency | 50-200ms | <10ms |
| Environmental Adaptability | Relies on lighting and is prone to occlusion. | All-weather operation, unaffected by lighting conditions |
| Intent Understanding | Can only track surface movement trajectories. | Understand the intent and force mechanics behind the movement. |
Advantages of Dual-Mode Collection
Traditional approaches only track hand position and movement trajectories, failing to reveal how fingers exert force, how that force varies, or the true intent behind each gesture.
Neurophysiological signals
Capture motor commands from the nervous system to predict movement intent before muscles actually contract.
Electromyography (EMG) signals
Record electrical activity in muscle fibers to precisely measure force intensity, duration, and coordination patterns.
Fusion Analysis
Dual-mode signal time synchronization to build a complete "Intent → Force → Execution" chain, enabling true motion understanding.
Technical Specifications
Industrial-grade hardware capabilities for demanding applications
Use Cases
Practical applications in humanoid robot and dexterous hand training
Humanoid robot training
Capture expert operations via neural wristbands to rapidly generate high-quality training datasets.
Dexterous hand fine manipulation
Mimic human finger force patterns and coordination strategies to achieve precision assembly and manipulation.
Experience the next generation of human-machine interaction
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