OriginFlow bets on sEMG data capture to turn human motion into machine-readable tokens
Embodied AI data collection is gaining a new track: surface electromyography, or sEMG. OriginFlow, a Beijing-based startup founded by Tsinghua PhD student Qin Shentao, is building around that idea with the goal of capturing not just visible hand movements but also the underlying muscle activation, contact feedback, and force changes that happen during real-world manipulation. The company says it was founded in Beijing in August last year and had disclosed by May this year that it had completed an angel round, a strategic round, and a Pre-A1 round, raising more than RMB 500 million in total from investors including Lanchi, Oasis, and Monolith. OriginFlow’s NeuroScale system combines non-invasive sEMG signals with egocentric vision and IMU data, then uses its PULSE foundation model to reconstruct posture, contact force, and drive force from human operations. The startup calls the resulting representation “Human Tokens.” At WAIC, the team showed a PULSE 0.2 demo using 16-channel sEMG input to continuously model hand movement and observe force changes during pinch gestures. Beyond capture hardware, OriginFlow has also built data infrastructure for alignment, labeling, filtering, and training, arguing that scalable embodied intelligence will require far more than raw signal collection alone.
