Monitoring the finger rehabilitation process is critical for preventing accidental injuries and achieving superior personalized therapeutic outcomes. However， current research focusing specifically on monitoring this process remains limited. To address this gap， this study aims to accurately detect finger joint movement angles and assess contact forces during rehabilitation training. The primary objective is to develop a solution that enables comprehensive monitoring of both non-grip and grip rehabilitation exercises.

This work proposes a novel optical Fiber Bragg Grating （FBG） finger exoskeleton rehabilitation device. The core of the device consists of two specialized FBG fingerstalls： a monitoring fingerstall for tracking movement and a tactile fingerstall for measuring force， both integrated with advanced FBG sensor units. To validate the design， a mathematical model of the rehabilitation device was established， and its rationality was rigorously analyzed. The experimental methodology included extensive calibration and testing： the FBG sensor units underwent sensitivity， connectivity， fit， and creep resistance tests； the monitoring fingerstall was calibrated for motion angle and its angular velocity sensing performance was compared against an IMU sensor using Bland-Altman analysis on two volunteers； and the tactile fingerstall was calibrated for force， with its sensory capabilities further analyzed using arc-length， linear， and BP neural network methods. Finally， volunteers conducted grip rehabilitation training experiments to assess contact force by measuring six different objects.

The experimental results comprehensively demonstrated the high performance of the proposed system. The FBG sensor units exhibited excellent connectivity， with an average sensitivity exceeding 0.911 98 nm/N， an average fit greater than 0.995 5， and outstanding creep resistance （maximum wavelength shift of 0.9 pm）. The monitoring fingerstall achieved an average angular sensitivity greater than 9.5 pm/（°）. Critically， its performance in sensing finger angular velocity was nearly identical to that of a commercial IMU sensor， and it successfully recognized six different hand gestures. For the tactile fingerstall， sensitivity measurements at symmetric points were nearly identical， with average sensitivity at points 1/2， 3/4， and 5/6 being higher than 19.5 pm/N， 13.45 pm/N， and 9.1 pm/N， respectively. The arc-length and linear analysis methods yielded low errors （8.45% and 8.38%）， confirming model reliability， while BP neural network analysis resulted in an extremely low mean squared error of 0.000 003 309 6. The grip experiments revealed that object volume had a more significant impact on the tactile fingerstall's readings than object mass.

This study successfully developed and validated a sophisticated FBG-based finger exoskeleton rehabilitation device. The system demonstrated high accuracy， reliability， and multi-functionality in monitoring finger joint angles， assessing contact forces， and recognizing complex hand gestures. The findings confirm that the proposed device and its underlying mathematical model are effective for comprehensive finger rehabilitation monitoring. Consequently， this work provides substantial theoretical and technical support for the practical implementation of FBG intelligent sensing technology in clinical finger rehabilitation applications， paving the way for more data-driven and personalized therapeutic interventions.