Emerging Interfaces

This Wristband Reads Your Gesture Intent Before Your Hand Actually Moves

Wearable Devices' Mudra Link decodes surface nerve conductance signals through three sensors combined with a 6-DoF IMU, giving touch-free device control that doesn't depend on a camera seeing your hand at all.

Wearable Devices Ltd. used the Augmented World Expo 2026 in Long Beach to showcase its full neural input ecosystem — the Mudra Link wristband, the enterprise-grade Mudra Pro hardware platform, and Mudra Studio, a generative-AI tool for building gesture interactions from text prompts. At its center is a specific technical bet: instead of watching a hand move through space with a camera, Mudra Link reads the neural and muscular signal that precedes the movement, using three Surface Nerve Conductance (SNC) sensors combined with a six-degrees-of-freedom IMU.

Watch: Mudra Link — Neural Gesture-Control Wristband (YouTube)

How “reading before moving” actually works

The SNC sensors pick up the electrical activity your nervous system generates on its way to a muscle contraction, and proprietary algorithms decode that signal to infer gesture intent — effectively catching the command before the physical gesture is fully executed. Combined with the IMU’s positional data, the system can register a directional or selection gesture with what the company describes as ultra-low latency, since it isn’t waiting on a camera frame to capture a completed hand motion and a vision model to classify it afterward. At AWE, attendees demoed this directly on the show floor, controlling applications in both precision pointer mode and directional pad mode.

Three products, one shared bet

Mudra Link is the consumer-facing gesture wristband; Mudra Pro is the next-generation enterprise platform layering in EMG, IMU, and PPG sensors for deeper intent recognition aimed at AI, XR, and enterprise deployments; and Mudra Studio is the software layer — a generative-AI tool that lets developers build, test, and deploy custom gesture-based interactions from text prompts rather than hand-coding a gesture classifier, extending the ecosystem to third-party hardware rather than locking it to Wearable Devices’ own products.

Why camera-free matters beyond the latency claim

Camera-based gesture and hand tracking — the approach behind most consumer XR headsets and the MediaPipe-style computer-vision pipelines many creative coders already use — has real limitations: it needs a clear line of sight, struggles in low light or heavy occlusion, and raises privacy questions the moment it’s always-on and pointed at a room rather than a hand. A neural-signal wristband sidesteps all three: it works with the hand out of frame entirely, functions identically in the dark, and never captures anything resembling a video feed of its surroundings. The company’s own pitch — a private, socially acceptable control layer that doesn’t need line of sight — is a direct response to those specific weaknesses, and it’s a genuinely different technical bet than the vision-model approach the rest of the gesture-tracking field has converged on.