Integrating Multi-Source Signals for Enhanced Contactless Human Sensing in Medical Contexts

This article uses advanced signal processing and machine learning to improve medical contactless human sensing. Advanced medical algorithms enable multi-source sensor data collection and analysis in the suggested strategy. In the beginning, the system combines inputs from multiple sensors and dynamically adjusts them based on SNR. Adaptive Z-score algorithms identify anomalies and resist noise. Iterative adjustments then achieve perfect temporal synchronization of synchronized signals, ensuring data integrity for accurate medical analysis. Advanced feature extraction methods normalize and reduce dimensionality while also capturing temporal and spectral features to improve discrimination. A revised and cross-validated machine learning model uses extracted attributes to identify medical issues in real time. The method is better than others based on performance evaluation, which shows high accuracy (88.4%), precision (83.9%), recall (86.7%), F1-score (85.1%), and error metrics like MAE (3.2), RMSE (3.9), and Cohen's Kappa (0.80). These studies show its accuracy in medical data processing and classification, helping doctors make better judgments.