Domain Adaptation for Surface Electromyography Gesture Recognition

The patent segments the domain adaptation problem into multiple source-specific views, where each view corresponds to a specific source domain (user, session, or fatigue state). Instead of treating all source domains uniformly, the method divides them into separate adaptation streams, each with its own adaptation model. This segmentation allows the system to capture domain-specific characteristics while maintaining overall robustness, directly addressing the degradation in recognition accuracy when applying conventional single-domain adaptation methods to multi-source scenarios.

The patent creates a universal gesture recognition model that can function across multiple domains by integrating multiple source-specific adapted models. The final recognition system universally handles data from any source domain by combining the outputs of domain-specific adaptation pathways. This multi-functionality enables the system to maintain high recognition accuracy across different users, sessions, and fatigue states, resolving the contradiction between reliability in specific domains and adaptability across domains.

The patent segments the complex multi-source domain adaptation problem into manageable source-specific sub-problems. Each source domain is handled by a dedicated adaptation module that processes data independently before aggregation. This segmentation reduces the complexity of learning all domain associations simultaneously while maintaining robustness through multiple specialized pathways, rather than using a single complex model that tries to handle all domains uniformly.

The patent introduces a new dimensional structure by organizing multiple source domains into distinct view layers. Instead of flattening all source domains into a single feature space, the method creates a multi-dimensional architecture where each dimension corresponds to a source-specific view. This dimensional organization allows the model to capture complex associations between different source domains and target domain without requiring excessive model complexity, as the structure naturally encodes domain relationships.

The patent segments the single model training process into multiple parallel training streams, each focused on a specific source domain. This segmentation maintains relative training simplicity by allowing each source-specific model to be trained independently on its corresponding data, avoiding the complexity of joint training while still achieving multi-source adaptability through the aggregation of specialized models.

The patent merges multiple source-specific adapted models into a unified target domain gesture recognition system. The individual models, each trained on specific source domain data, are combined through their outputs to form the final recognition decision. This merging approach enables the system to handle the multi-source property of electromyography signals effectively while maintaining the training simplicity of separate specialized models, rather than requiring a single complex model to learn all source domain characteristics simultaneously.