A Textile-Based Wilkinson-Divider RF Sensor for Finger-State Extraction in Wearable IoT Systems

Gesture-aware wearable interfaces for human-centric Internet-of-Things (IoT) systems require compact sensing front ends that can provide finger-level observability without dense wiring, bulky sensor arrays, high-dimensional RF measurements, or computation-heavy learning models. This work presents a textile guided-wave RF sensing method that repurposes a glove-integrated 1×4 Wilkinson power divider as a four-channel finger-posture transducer at 865MHz. Unlike its conventional role as a passive RF power-distribution network, each output branch is routed along an individual finger region so that extension/flexion perturbs the local guided-wave propagation path and modulates the corresponding input-to-branch transmission magnitude. The structure, fabricated on a flexible cotton substrate, provides four synchronized branch-magnitude observables, |S21|, |S31|, |S41|, and |S51|, associated with Ports 2–5. The branch responses are acquired using software-defined radios and processed through a deterministic edge-processing pipeline comprising signal conditioning, denoising, and adaptive-hysteresis digitization to generate compact binary finger states. An application-level lookup rule then maps the resulting 4-bit state vector to commands for live dashboard visualization. Measurements confirm good multiport matching, with all port reflection coefficients below −10 dB at 865MHz, near-balanced input-to-branch transmission responses, and repeatable posture-dependent magnitude modulation. The extracted branch responses are subsequently converted into representative per-finger binary states and streamed as compact state packets for real-time monitoring. The results support the divider-as-sensor principle as a compact proof-of-concept route toward textile-integrated IoT wearables for gesture-aware HMI, HRI, and connected control applications.