I-Corps: Translation Potential of Cross-Platform Wearable Interface for Spatial and Ambient Computing

This I-Corps project is based on the development of a wrist-worn interface that enables people to control smart devices through subtle muscle activity and motion. Users currently rely on voice, cameras, touch surfaces, handheld controllers, or smartphones, to control connected devices such as artificial intelligence (AI) glasses, smart home devices, autonomous vehicles, and other forms of ambient computing, which can be unsuitable in private, noisy, hands-busy, or accessibility-sensitive settings. This technology translates wrist and forearm signals into simple digital commands that complement existing input methods across multiple device categories. Applications may include reference devices, software tools, integration support, licensing, and embedded partnerships for wearable, enterprise, industrial, accessibility, robotics, and connected-environment applications. This technology may make interaction with intelligent systems more private, convenient, inclusive, and reliable. This I-Corps project utilizes experiential learning coupled with first-hand investigation of the industry ecosystem to assess the translation potential of a platform that translates hand and body gestures into digital input commands for human-machine interaction. Current electromyography (EMG) interfaces work in controlled demonstrations but often struggle with motion, sweat, sensor placement changes, and day-to-day variability. This technology combines surface electromyography and inertial sensing to translate wrist and forearm activity into private, low power digital commands. The end-to-end sensing-to-inference architecture addresses motion artifacts, sensor-placement changes, calibration burden, false triggers, and variability across users and days, rather than treating gesture recognition as an isolated classification task. Applications include reusable control primitives across glasses, computers, mobile devices, connected systems, and autonomous platforms. This wearable EMG technology may reduce the dependance on speech, cameras, or handheld controls. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.