Model-Based Design and Sensitivity Optimization of Frequency-Output Pressure Sensors for Real-Time Monitoring in Intelligent Rowing Systems

This study presents a model-driven approach to the design, calibration, and application of frequency-output pressure sensors integrated within an intelligent system for real-time monitoring of rowing performance. The proposed system captures biomechanical parameters of the “boat–rower” complex across 50 parallel channels with a temporal resolution of 8–12 ms. At the core of the sensing architecture are parametric pressure transducers incorporating strain-gauge primary elements and microelectronic auto-generator circuits featuring negative differential resistance (NDR). These oscillating circuits convert mechanical stress into high-frequency output signals in the 1749.9–1751.9 MHz range, with pressure sensitivities from 0.365 kHz/kPa to 1.370 kHz/kPa. The sensor models are derived using physical energy conversion principles, enabling the formulation of analytical expressions for transformation and sensitivity functions. These models simplify sensitivity tuning and allow clear interpretation of how structural and electronic parameters influence output frequency. The system architecture eliminates the need for analog-to-digital converters and signal amplifiers, reducing cost and power consumption, while enabling wireless ultra high frequency (UHF) transmission of sensor data. Integrated algorithms analyze the influence of biomechanical variables on athlete performance, enabling real-time diagnostics. The proposed model-based methodology offers a scalable and accurate solution for intelligent sports instrumentation and beyond.