Accurate, real-time identification of gait phases is crucial for clinical assessment, rehabilitation monitoring, and wearable health applications. However, most current smart-insole solutions require high-frequency sampling, increasing power consumption and computational load. To address these limitations, we developed a lightweight, custom-designed smart insole system operating at a low frequency of 5 Hz. The system integrates eight force-sensitive resistors (FSRs) and a tri-axial Inertial Measurement Unit (IMU) per foot. Fourteen healthy participants performed five walking trials on a 10 m. At the same time, synchronized insole sensor data and RGB video recordings were captured, with video annotations serving as the ground truth for gait-phase verification. We systematically evaluated five distinct feature sets using six classical machine learning classifiers with participant-wise cross-validation. Pressure-only features classified using a support vector machine yielded the highest macro-F1 score of 0.915, confirming that low-frequency plantar pressure signals effectively discriminate against gait phases without substantial loss in accuracy. In contrast, IMU-only signals demonstrated significantly lower classification performance, highlighting the limited effectiveness of inertial data at low sampling rates. Additionally, we developed a visual analytics pipeline to enhance interpretability, generating spatial plantar pressure heatmaps and activation-frequency maps that clearly illustrate distinct load patterns for each gait phase. Our findings demonstrate that low-frequency plantar-pressure signals provide sufficient temporal and spatial information for reliable gait-phase detection. This approach offers a practical solution for real-time gait monitoring applications.