Surface Electromyography Features-Based Analysis of Tibia Bone Fractures for Post-Orthopedics Surgery Follow-up

Abstract

The importance of assessing bone fracture healing is to ensure restored mobility and avoid long-term issues during orthopedic follow-up. In this assessment the role of statistical analysis to understand the relationships between muscle strength and surface electromyography (sEMG) parameters is paramount. This analysis aims to identify key sEMG indicators to monitor muscle fatigue caused by fractures and to correlate it with the healing process during rehabilitation. Quantifying these relationships can provide evidence-based insights on recovery and optimizing the treatment. Surface electromyography (sEMG) has emerged as a non-invasive technique for assessing muscle activity and fatigue levels. This study explores the application of sEMG signal analysis through quantitative estimation of muscle fatigue in individuals undergoing fracture rehabilitation. This study investigates the important features of surface electromyography (sEMG) signals which can quantitatively assess the fracture healing for follow-up in orthopedics surgery. sEMG data was collected from 16 subjects, considering both normal and abnormal (fractured) legs, by performing ankle dorsiflexion and plantarflexion movements. A total of 43 features from both time and frequency domain were extracted from the sEMG signals recorded on the tibialis anterior and posterior muscles. Principal Component Analysis (PCA) was employed for dimensionality reduction and interpreting the distinct clustering of normal and abnormal muscle groups. The biplot analysis identified key features such as wavelength form (ewl), integrated EMG (iemg), and kurtosis (kur), that contributed to group separation. Furthermore, the muscle fatigue index was computed from the specific sEMG features such as MNF and ARV and paired t-test was performed on the mean of the fatigue index between the control and the abnormal group. We found that the fatigue index was significantly different between these groups with a p-value of 0.034 for single-tailed t-test for the chosen significance level of =0.05. Our analysis shows that features from sEMG signal can be used for monitoring the muscle fatigue and guide physicians in personalized follow-up of fracture recovery.