Machine learning-optimized dual-band wearable antenna for real-time remote patient monitoring in biomedical IoT systems

This work presents a machine learning (ML)-optimized dual-band wearable antenna designed specifically for biomedical applications in healthcare monitoring. Fabricated on a Rogers substrate of 40 × 41 mm², the antenna operates at 2.4 GHz and 5.8 GHz with measured bandwidths of 4.5% and 2.9%, gains of 3.8 dBi and 6.0 dBi, and high radiation efficiencies (92% and 91.7%, respectively). Bidirectional and directional radiation patterns are noted in the E-plane, while the H-plane exhibits omnidirectional patterns at the lower and upper bands. To ensure the antenna's safety for biomedical use, specific absorption rate (SAR) assessments were conducted, CST MWS simulations evaluated the SAR of the proposed wearable antenna on arm, chest, and lap placements at 5 mm distance. At 2.4 GHz, 1 g/10 g SAR values were 0.533/0.919 W/kg (arm), 0.864/1.455 W/kg (chest), and 0.892/1.122 W/kg (lap). At 5.8 GHz, results were 0.872/1.241 W/kg (arm), 0.577/1.433 W/kg (chest), and 0.428/1.341 W/kg (lap), all well within safety limits. The proposed healthcare monitoring system integrates a SEN11547 pulse sensor and an LM35 temperature sensor to measure heart rate and body temperature, transmitting the data to the ThingSpeak IoT platform via the NodeMCU ESP-32S Wi-Fi module, ensuring real-time data availability. The heart rate ranged from 65 to 99 BPM, and the body temperature ranged from 30 to 37 °C. The supervised regression ML approach was effectively utilized to predict the antenna's reflection coefficient (S₁₁). Performance evaluation of the models employed metrics such as mean absolute error (MAE), mean squared error (MSE), root mean squared logarithmic error (RMSLE), mean squared logarithmic error (MSLE), and R-squared (R²). The ensemble regression model outperformed others, delivering the lowest errors (MAE: 0.83%, MSE: 1.64%, RMSLE: 0.56%, RMSE: 1.83%, MSLE: 0.44%) and the highest accuracy (R²: 97.79%) while reducing the computational time by 70% compared to conventional methods. Results validate the antenna's reliability and effectiveness for wearable healthcare applications.