Cognitively aligned pattern learning: a knowledge-sensitive adaptive framework for multi-class pattern recognition in...

Multi-class surface electromyography (sEMG) gesture recognition systems frequently suffer from persistent and structured misclassification patterns, particularly among closely related gesture classes, which limit long-term stability and adaptability. To address this challenge, this paper introduces Cognitively Aligned Pattern Learning (CAPL), a hybrid adaptive model based on implementing Stacked One-versus-One (SOvO) multi-class decomposition with knowledge management and agile learning conceptualizations. CAPL explicitly represents recurring misclassification instances as structured knowledge objects and uses an error-centric, selective adaptation mechanism that only refines the involved class pairs, preventing global retraining and catastrophic forgetting. The proposed framework is formalized and operationalized mathematically, which is emulated through an architecture called KACL-Net that illustrates how knowledge maintenance, feedback based on confusion, and loops of learning may be incorporated in multiclass pattern recognition pipelines. Emulated comparative evaluation suggests potential performance improvements. Conceptual and emulated comparative analysis suggests that CAPL may provide improved classification stability and reduced recurrent misclassification trends compared with conventional OvO and SOvO approaches. Further simulation of robustness with noise and electrode motion conditions suggests a higher level of stability and accuracy gains of up to 2.6% over SOvO.These are conceptual and emulated performance trends instead of results of a full deployment real-time implementation. In general, CAPL proposes a cognitively inspired paradigm for knowledge-sensitive adaptive multiclass learning that is cognitively inspired, scalable, and interpretable, providing an alternative and scalable approach to the challenge of sEMG-based human-machine interaction systems, where conventional classification strategies typically remain fixed after training.