TAGL: Temporal-Guided Adaptive Graph Learning Network for Coordinated Movement Classification

Deciphering coordinated movements is integral to understanding the daily activities and interactions between the nervous system and muscles, especially in robot-assisted rehabilitation. This study proposes a novel temporal-guided adaptive graph learning (TAGL) network to recognize coordinated movements from functional near-infrared spectroscopy (fNIRS) data. The temporal-guided node construction module is designed to build graph nodes while considering spatiotemporal and causal dependencies. Given the brain network's affinity for learning asymmetric structures, an adaptive edge learning module is devised, integrating a multihead attention mechanism for the tailored acquisition of directional edge connections among nodes. The TAGL model undergoes evaluation on both a proprietary fNIRS dataset featuring eight circular finger movements and a public fNIRS dataset involving three distinct actions. Comparative experiments with state-of-the-art methods reveal its superior performance, showcasing its potential in deciphering coordinated movements effectively.