LAUSR

Thumb actions, outperforming conventional methods such as hand gestures or wrist gestures in terms of dexterity, agility and intuitiveness, have long been sought‐after for achieving immersive interactive experiences in robotic control and AR/VR platforms. However, accurate mapping of such dynamic, subtle thumb actions remains a difficult challenging. Here, a wireless, wrist‐affixed soft ultra‐conformal neuromuscular interface system (UniSyst) is reported to capture high‐fidelity surface electromyography crucial for decoding dynamic subtle thumb actions. The UniSyst, which can be easily fabricated at scale and in mass quantities, features a 16‐channel soft, stretchable sensing array for broad, high‐resolution data capture and a stiff design for plug‐and‐play interface with external rigid wireless acquisition module. This soft‐stiff design ensures consistent electrode‐skin contact under substantial skin deformations, thus avoiding undesired motion artifacts commonly observed with rigid alternatives. Facilitated with a lightweight 1D convolution neural network deep learning classifier, this system shows remarkable recognition accuracy over that of traditional forearm placements, fairly compared through concurrently collected signals from the wrist and the forearm of 12 participants. In practical scenarios, the soft UniSyst exhibits rapid, precise thumb‐controlled interactive capabilities, adeptly managing human–machine communications in both digital platforms and immersive gaming controls.