Paper Contents
Abstract
This paper presents a comprehensive multimodal system for interpreting human emotions and translating hand gestures into communicative messages. Leveraging image pro- cessing for facial emotion recognition (happy, sad, neutral), a Raspberry Pi camera captures real-time expressions which are processed using machine learning. Simultaneously, hand gesture recognition converts physical gestures into text messages, aiding non-verbal communication. Additionally, an EMG sensor is used for emotion detection through muscle activity, while EEG curves are generated to analyze brain signal patterns, providing another dimension of emotion assessment. This work proposes an integrated assistive framework for enhanced human-machine interaction and support for differently-abled individuals.Index TermsEmotion detection, Image processing, Hand gesture, Raspberry Pi, EMG, EEG, Human-computer interaction
Copyright
Copyright © 2025 Nihal Kumar. This is an open access article distributed under the Creative Commons Attribution License.
