Autonomic Nervous System Adaptation to Supernumerary Robotic Finger Use: Coherence Analysis of RR Intervals Before and After Training

Supernumerary robotic fingers (SRFs) are wearable assistive devices, which are increasingly incorporated into robotic rehabilitation programs aimed at restoring upper-limb function and promoting task-specific compensation. Despite growing evidence of SRF efficacy in improving motor performance, limited attention has been given to physiological adaptation and autonomic nervous system (ANS) integration during SRF use. This study investigated phase coherence (PC) and amplitude-weighted phase coherence (AWPC) of RR intervals derived from photoplethysmogram (PPG) as noninvasive biomarkers for autonomic nervous system adaptation during SRF-assisted activities of daily living. Thirty healthy participants completed a baseline (no SRF), pre-training SRF application and post-training SRF use, including rest periods protocol. Drinking water, driving and shape sorting were the functional activities of daily living (ADLs) that had to be completed. The results for PC and AWPC in the low (0.04-0.15) and high (0.15-0.4) frequency bands indicated an overall significant reduction in stress associated with SRF use (p <0.05). During the shape sorting task, post-training AWPC was significantly higher than in the pre-training phase (p = 0.037), and PC also increased significantly (p = 0.044), indicating enhanced vagal modulation. Driving task AWPC improved in the high-frequency band increasing from 0.68 ± 0.12 (no SRF) to 0.74 ± 0.10 (pre-training SRF) and 0.79 ± 0.09 (post-training SRF), while PC increased from 0.54 ± 0.11 to 0.62 ± 0.08 after training demonstrating significant task, phase, and frequency-specific alterations in autonomic coherence. This work provides an innovative perspective on physiological embodiment and how robotic compensation/augmentation improve both motor performance and physiological regulation. PD analysis indicated central autonomic adaptation. The current findings support the integration of coherence-based autonomic measures into assistive device evaluation frameworks to optimize training protocols and personalize robotic rehabilitation strategies.