Concentric Rheostat Decoupled 3D Force‐Sensing Module for Smart Table Tennis Training

3D force sensing is crucial for improving human–machine interactions and optimizing sports training. Current solutions typically rely on multisensor fusion and algorithmic decoupling, which are prone to signal crosstalk and limited angular resolution. Here, we introduce a concentric rheostat sensor that intrinsically decouples normal and shear forces using a hybrid capacitive–resistive sensing mechanism. The concentric contact resistor directly converts shear force directions into resistance values, achieving high‐resolution angular detection without extensive computational postprocessing. Fabricated via high‐resolution 3D printing of hydrogel, the sensor array is integrated into a smart table tennis paddle for real‐time acquisition of 3D force and impact location during ball strikes. The collected data is processed by a two‐stage machine learning pipeline. In the first stage, multiple linear regression (MLR) extracts robust raw metrics from primitive physical features. Subsequently, a random forest regressor evaluates these metrics to provide personalized performance grades that account for skill interdependence, including stability. This combination of intrinsic 3D force decoupling and data‐driven skill assessment demonstrates a compact, high‐precision platform for intelligent sports training and real‐time skill evaluation.