A Physics-Informed ConvFormer with State-Space Gating for Smartphone-Based Human Activity Recognition on the WISDM Dataset

Smartphone-based human activity recognition (HAR) requires discriminative temporal modeling and careful assessment of computational cost. This study evaluates a Physics-Informed ConvFormer with State-Space Gating for six-class HAR on the WISDM v1.1 accelerometer dataset. The model combines raw acceleration with deterministic magnitude, directional, gravity-estimate, and dynamic-acceleration features, followed by large-kernel depthwise convolution, a Mamba-inspired causal gate, attention, and a multilayer perceptron. The main subject-level experiment achieved 95.63% best validation accuracy and 83.92% test accuracy. Across four subject-level splits, the full model achieved 88.86 ± 6.16% mean test accuracy, compared with 89.66 ± 5.56% for the compact CNN baseline and 89.54 ± 5.07% for the raw three-channel hybrid variant. The full model used 588,006 parameters and had a measured local CPU forward-pass latency of 21.54 ms per window, whereas the CNN used 125,766 parameters and 0.45 ms. Removing State-Space Gating changed mean accuracy by 0.22 percentage points and reduced latency to 3.80 ms. Removing local convolution or attention reduced mean accuracy by 3.99 or 3.06 percentage points, respectively, within the hybrid backbone. These results show that local convolution and attention are valuable within the hybrid design, while raw-input and no-SSM configurations remain competitive lower-cost alternatives. The study provides a transparent component-level assessment under subject-level separation and defines priorities for future smartphone deployment, orientation-robust evaluation, and longer-context modeling.