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NeuroComm: A Wearable Interface for Real-Time Emotion and Gesture-Based Communication

CSEF · 2026 Computational Science (Senior Division)

Ginny Zhang

Overview

Non-verbal individuals often experience difficulty communicating basic needs and emotional states. This project investigates whether physiological signals from wearable sensors could be used to translate gestures and emotional signals into simple communication outputs. The research question tested whether combining electromyography (EMG) and galvanic skin response (GSR) signals could improve the accuracy of interpreting gestures and emotional states. A wearable prototype system called NeuroComm was developed. EMG sensors detected muscle activity associated with hand gestures, while a GSR sensor measured changes in skin conductance related to emotional changes. Signal data was collected and processed using a classification system to identify patterns corresponding to predefined gestures and emotional conditions. The signals were then mapped to communication phrases that could be delivered through visual output. System performance was analyzed by classification accuracy. Results showed that integrating both EMG and GSR signals improved the system’s ability to distinguish gestures and emotional states compared with using certain signals alone. The prototype demonstrated reliable real-time responses with low latency. These findings suggest that combining wearable biosensors with machine learning can help with the development of assistive communication technologies that help individuals with limited speech express needs and emotions more efficiently.

Competition history

  • CSEF 2026 Computational Science (Senior Division) · Entry S-07-03

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