MyoSem: Aligning Electromyography to Natural-Language Action Semantics for Hand Action Understanding

MyoSem is a new framework that aligns electromyography (EMG) signals with natural language descriptions to enable semantic understanding of hand actions. Rather than classifying gestures into fixed categories, the system allows bidirectional retrieval between EMG signals and text queries, demonstrating strong generalization across users and action types.

MyoSem represents a meaningful advancement in how machines interpret biological signals from muscle activity. The research shifts EMG analysis from rigid classification systems to flexible semantic retrieval, enabling queries like "what action produced this EMG signal?" or "what EMG patterns correspond to this hand motion description?" This paradigm change mirrors broader progress in multimodal AI, where different data types (visual, textual, biological) are mapped into unified semantic spaces for richer understanding.

The framework's technical contribution stems from recognizing that fixed-label classification limits real-world applications. Prosthetic control, gesture recognition, and wearable interfaces require adaptability to new actions, users, and conditions without retraining. MyoSem addresses this by constructing semantic representations from multiple action descriptions, then aligning EMG encodings to this space. The system demonstrates particularly strong performance on cross-user and amputee-transfer scenarios, suggesting practical viability for accessibility applications.

This work has downstream implications for wearable computing and human-computer interfaces. As neural interfaces and EMG-based devices become more prevalent, systems capable of semantic reasoning about muscle signals could enable more intuitive prosthetics and gesture control. The approach also shows how language models can enhance biological signal processing, potentially opening new applications combining NLP with biometric data.

Future development should focus on real-time inference capabilities and integration with commercial wearable platforms. Validating performance on larger, more diverse user populations and exploring other biosignals (EEG, IMU) within similar frameworks would strengthen the research's impact on accessibility technology markets.

• →MyoSem maps EMG signals to semantic space using natural language descriptions, enabling queryable action retrieval instead of fixed-label classification.
• →The framework demonstrates strong generalization to unseen users, new action classes, and amputee-user scenarios, addressing key challenges in wearable applications.
• →Bidirectional EMG-text retrieval enables more flexible human-computer interfaces compared to traditional gesture recognition systems.
• →The research combines multi-view semantic construction with activation-aware signal encoding to align low-level muscle signals with high-level action meanings.
• →This approach represents a paradigm shift toward language-mediated biological signal understanding with potential applications in prosthetics and wearable interaction.