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The precise diagnosis is of great significance in developing precisetreatment plans to restore neck function and reduce the burden posed by thecervical spondylosis (CS). However, the current available neck functionassessment method are subjective and coarse-grained. In this paper, based onthe relationship among CS, cervical structure, cervical vertebra function, andsurface electromyography (sEMG), we seek to develop a clustering algorithms onthe sEMG data set collected from the clinical environment and implement thedivision. We proposed and developed the framework EasiCS, which consists ofdimension reduction, clustering algorithm EasiSOM, spectral clusteringalgorithm EasiSC. The EasiCS outperform the commonly used seven algorithmsoverall.

EasiCS: the objective and fine-grained classification method of cervical spondylosis dysfunction