Decoding of fine hand motor control for kinematic acupuncture needling manipulation

Manual acupuncture depends on precise thumb-index finger coordination, yet the biomechanical principles governing this dexterity remain unresolved. Here, we establish a non-contact, video-based platform integrating marker-labeled motion capture with nonlinear dynamic and multi-modal coupling analyses to decode fine finger coordination across standardized training, animal models, and clinical observation. Tracking eight anatomical joints reveals an asymmetric motor architecture: the distal index finger acts as a temporal pacemaker with high rhythmic stability, whereas the distal thumb functions as an amplitude modulator. Multi-modal coupling resolves two biomechanical modules-an index finger chain with hierarchical phase propagation and a thumb chain with strong intra-module synchronization-linked by millisecond-scale temporal offsets. These patterns are encoded as operator-specific "Acupuncture Kinematic Fingerprints," stratifying practitioners into index-dominant and thumb-dominant strategies. This framework transforms subjective manipulation styles into quantitative kinematic signatures for objective skill assessment, standardized training, and mechanistic studies.