Current masterslave hand mapping in the humancomputer interaction has shortcomings such as lack of feedback, poor mapping accuracy and poor wearability. A masterslave mapping system based on fabric strain sensor and feedback mechanism is presented to address these issues. The strain sensitive unit is constituted of Lycra fabric surface spinning graphene/polyaniline composite conductive materials, in which the silver conductive adhesive is used as the electrode. The fabric strain sensor is layout in masterslave hand to construct 2×5 array. The gesture recognition model is obtained by combining stretch sensor information and the improved DH algorithm. The BP neural network is used to model the information of the masterslave strain sensor. Combining with the online optimization algorithm, the feedback mechanism of the strain sensor is introduced to realize the efficient and accurate mapping of the masterslave hand gestures, and the gesture mapping with feedback mechanism is established. The strain characteristics of the sensors are tested and the mapping accuracy of the gesture mapping system with and without feedback mechanism is compared. The experimental results indicate that the masterslave control system based on fabric strain sensor and feedback mechanism can improve mapping precision and wearability.