This paper presents a wearable gait recognition sensor system for exoskeleton robots, designed to enhance load-bearing ability and prevent injuries. The system integrates pressure sensor arrays based on laser-induced graphene (LIG) into an insole to detect real-time plantar pressure at key positions. The LIG/PDMS composite material, fabricated through laser processing, provides high sensitivity and reliability. The sensor system is integrated with an exoskeleton robot, and a machine learning algorithm, specifically a support vector machine (SVM), is used to achieve accurate gait recognition. Experimental results show a 99.85% accuracy in gait recognition, verified through testing on an exoskeleton robot. The system's effectiveness is further demonstrated in real-world applications, highlighting its potential for human-robot interaction control in rehabilitation and other fields.This paper presents a wearable gait recognition sensor system for exoskeleton robots, designed to enhance load-bearing ability and prevent injuries. The system integrates pressure sensor arrays based on laser-induced graphene (LIG) into an insole to detect real-time plantar pressure at key positions. The LIG/PDMS composite material, fabricated through laser processing, provides high sensitivity and reliability. The sensor system is integrated with an exoskeleton robot, and a machine learning algorithm, specifically a support vector machine (SVM), is used to achieve accurate gait recognition. Experimental results show a 99.85% accuracy in gait recognition, verified through testing on an exoskeleton robot. The system's effectiveness is further demonstrated in real-world applications, highlighting its potential for human-robot interaction control in rehabilitation and other fields.