A wearable multi-joint fingerstall with three different specifications based on Fiber Bragg Grating （FBG） was designed. It could shield the temperature effects on FBG. With good fixing coupling and anti-creep ability， a multi-joint finger motion model was established. Calibration experiments for joint angle measurements were performed by wearing the three types of fingerstalls on a finger model and the fingers of two volunteers. A comparative experiment was conducted using an Inertial Measurement Unit （IMU） sensor and the FBG sensing unit to monitor the angular velocity of the index， middle， and ring fingers. The fingerstall was used to monitor three different hand gestures， and a Backpropagation （BP） neural network was employed for predictive analysis. The sensitivity error of the index finger in comparison with theoretical analysis is less than 7.7%. The average sensitivity for the corresponding joints during the "forward stroke" and "reverse stroke" in the finger model is greater than 5.3 pm/°， and for two Volunteers， the average sensitivity for the corresponding joints during the "forward stroke" and "reverse stroke" is greater than 3.8 pm/°. Calibration experiments show that the fingerstall can accurately monitor the motion sensitivity of different finger joints and is applicable to various populations， the finger sleeve device can shield it from the influence of human body temperature. Bland-Altman analysis of the comparative experiment data indicates consistency betweenthe results， demonstrating that the FBG sensing unit can monitor the motion of the index， middle， and ring fingers effectively. Using the fingerstall to monitor gestures and applying the BP neural network for prediction analysis， the Mean Squared Errors （MSE） for two Volunteers are 0.000 04 and 0.000 002， respectively. Comprehensive experimental results show that the FBG multi-joint fingerstall can precisely monitor the joint motion angle sensitivity and movement， and the BP neural network can be used for prediction.