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Conventional learning-based methods often suffer from limited adaptability and high computational latency, especially when deployed on low-power embedded hardware. A neuromorphic computing framework based on a synaptic convolutional neural network with memory autapses (SCN-MA) is introduced for real-time gait environment recognition. A structured gait experiment was conducted to collect pneumatic mechanomyography (pMMG) and stretch signals under multiple walking conditions, forming a multimodal dataset for training and evaluation. The framework adopts multiple binary gait classification models trained in parallel, which enhances modularity and scalability. A reinforcement learning based ensemble policy is then trained to integrate the outputs of these models for final decision-making. The complete system is implemented on the Zynq-based FPGA platform, enabling end-to-end prediction with low latency and high energy efficiency. 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