Reliable gait phase classification is essential for wearable-based locomotion analysis. Although gait cycle percentage prediction and gait phase classification are biomechanically related, knowledge transfer across these distinct objectives remains underexplored. In this paper, we propose a regression-to-classification transfer learning framework that utilizes temporal representations learned from continuous gait cycle progression to improve discrete phase recognition. We pre-train neural backbones on a regression task and transfer the learned representations to the classification task through model transfer (fine-tuning backbone weights) and feature transfer (using the backbone as a fixed feature extractor). To identify the optimal configuration for resource-constrained environments, we compare a compact Deep Neural Network (DNN) with 0.3 M parameters and a Transformer model across multiple sliding window sizes. Our experimental results demonstrate that model transfer achieves a superior F1-score of 0.9788, outperforming the feature transfer baseline and models trained from scratch. Efficiency tests show that the compact DNN achieves a Central Processing Unit (CPU) latency below 0.07 ms, supporting real-time data processing. Validation on an independent dataset further confirms cross-population robustness, achieving a classification accuracy of 92.3%. These findings suggest that regression pre-training captures effective temporal features, providing a practical framework for wearable-based gait analysis.