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.
