A New Method for Prediction and Monitoring of Spondylosis Using 5G Data Transmission Module-Based MEMS Accelerometer

Structural health monitoring plays a vital role in the medical and mechanical field in determining the magnitude, location, and detection of structural deterioration within the spine. Typically, lumbosacral spondylosis affects both the lumbar and sacral regions of the spine situated below the lumbar area, in the midline between the buttocks. In this paper, the authors present a newly designed Microelectromechanical Systems (MEMS) accelerometer with a Fifth-Generation Wireless (5G) data transmission module using cohort design of cervical spine vertebrae (C1-C7) levels and implemented in real time to monitor the spine displacement of several age groups during different activities with higher accuracy. The involved 5G data transmission, which is controlled by an Arduino electronic open-source platform, shows a better outage probability with enhanced cell radius. Applied Cyclic Prefix (CP) will reduce the delay spread in wireless communication by 12.3% as compared with other modulation scheme than Spectrally Decoded Orthogonal Frequency Division Multiplexing (SP-OFDM). Further, the hierarchy architecture of data analysis followed by Pareto distribution and canonical correlation to classify the data to fit within the specified model provides an accuracy of 0.891 in the department analysis of three categories like laptop usage, bus, and bike drivers of data. The attained experimental results confirm the detection rate due to the desired data embedded in the hierarchy data model on the time domain to identify the precise structural change in the spine. The spectrum efficiency has been improved by decreasing guard time and time resources allocated to various users by minimizing subcarrier leakages.