Optimizing In‐Bed Posture Classification Using Tanh‐Activated Kolmogorov–Arnold Networks (Tanh‐KAN)

In‐bed posture classification plays a crucial role in health monitoring. However, existing research on classification involves a limited range of in‐bed postures. Meanwhile, in classification tasks, Kolmogorov–Arnold networks (KANs), as an emerging neural network architecture, have research gaps in two areas: training strategies and architecture design. In our research, we propose Tanh‐KAN, an efficient variant of KAN for in‐bed posture classification. First, we validate that disabling the spline scaler not only preserves classification accuracy on the PoPu, Pmat, and SPN datasets, but also contributes to a reduction in model parameters and an increase in throughput. Second, we simplified the cubic B ‐spline basis functions in the original KAN using a Tanh‐kernel. Compared to the original KAN, the accuracy remained stable, while the parameters were reduced by approximately 9% and the backpropagation and inference speeds increased by 42.3% and 53.9%, respectively. Experimental results further demonstrate that Tanh‐KAN not only reduces model complexity and accelerates computation but also maintains high accuracy, achieving 99.6% on PoPu, 98.5% on Pmat, and 61.5% on SPN, matching the original KAN’s performance.