Wearable sEMG monitoring system toward axial symptoms assessment in Parkinson’s disease. Preliminary study

Monitoring axial impairment in Parkinson’s disease (PD) requires reliable and objective tools that go beyond traditional clinical evaluation. This study presents a wearable monitoring device based on surface EMG for assessing the severity of axial motor symptoms during a standing task. Thirty-one PD participants performed a 60 s eyes open standing task while bilateral sEMG was acquired from thoracic and lumbar back muscles. Signals were filtered, and a 40 s segment was analysed. Twelve time and frequency domain features were extracted, normalized and converted into asymmetry indices for each right–left pair. Principal Component Analysis (PCA) reduced data dimensionality, and Canonical Correlation Analysis (CCA) linearly combined the PCA components with commonly used clinical scales to derive an integrated biomarker of axial symptom severity. Empirical correlations obtained through 5,000 permutations showed stronger and more stable associations than analytical estimates. The proposed monitoring device provides a quantitative framework that minimizes operator variability and merges multiple clinical dimensions into a single objective index, supporting continuous and standardized monitoring of axial symptoms in Parkinson’s disease.