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.