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

## TL;DR

EasiCS is a novel framework that uses clustering algorithms on surface electromyography data to classify cervical spondylosis dysfunction with higher accuracy than existing methods.

## Contribution

The paper introduces EasiCS, a new clustering-based framework for fine-grained classification of cervical spondylosis dysfunction using sEMG data.

## Key findings

- EasiCS outperforms seven existing algorithms in classification accuracy.
- The framework effectively integrates dimension reduction with clustering algorithms.
- EasiCS provides a more precise diagnosis method for cervical spondylosis dysfunction.

## Abstract

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