Research on Data Processing and Pattern Recognition Algorithms of Intelligent Biosensors in Biomedical Testing

Intelligent biosensors are increasingly used in biomedical testing, especially in the field of medical testing, showing great potential. This study mainly discusses the data processing and pattern recognition algorithms of intelligent biosensors in medical testing. In the system design, a power spectral density-based analysis method is proposed for human posture recognition and signal processing to optimize data filtering and feature extraction in the signal processing process. By combining pattern recognition and data processing algorithms, the system realizes efficient human posture detection and diagnosis functions. In the process of system simulation, multiple signal processing techniques along with diverse model simulation approaches are employed to guarantee the precision and resilience of the simulation outcomes. The findings from the study indicate that the suggested algorithm offers substantial benefits in enhancing detection precision and real-time efficiency. Detailed analysis of the system architecture and simulation outputs further corroborates the effectiveness and practicality of the algorithm.