| 2022

Can Gait Features Help in Differentiating Parkinson’s Disease Medication States and Severity Levels? A Machine Learning Approach

Chariklia Chatzaki, Vasileios Skaramagkas, Zinovia Kefalopoulou, Nikolaos Tachos, Nicholas Kostikis, Foivos Kanellos, Eleftherios Triantafyllou, Elisabeth Chroni, Dimitrios I. Fotiadis, Manolis Tsiknakis

Biomedical Informatics and eHealth Laboratory, Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Estavromenos, Heraklion, Crete


gait analysis, Parkinson’s disease, ON/OFF medication, MDS-UPDRS, severity levels, insoles, pressure sensors


Parkinson’s disease (PD) is one of the most prevalent neurological diseases, described by complex clinical phenotypes. The manifestations of PD include both motor and non-motor symptoms. We constituted an experimental protocol for the assessment of PD motor signs of lower extremities. Using a pair of sensor insoles, data were recorded from PD patients, Elderly and Adult groups. Assessment of PD patients has been performed by neurologists specialized in movement disorders using the Movement Disorder Society—Unified Parkinson’s Disease Rating Scale (MDS-UPDRS)-Part III: Motor Examination, on both ON and OFF medication states. Using as a reference point the quantified metrics of MDS-UPDRS-Part III, severity levels were explored by classifying normal, mild, moderate, and severe levels of PD. Elaborating the recorded gait data, 18 temporal and spatial characteristics have been extracted. Subsequently, feature selection techniques were applied to reveal the dominant features to be used for four classification tasks. Specifically, for identifying relations between the spatial and temporal gait features on: PD and non-PD groups; PD, Elderly and Adults groups; PD and ON/OFF medication states; MDS-UPDRS: Part III and PD severity levels. AdaBoost, Extra Trees, and Random Forest classifiers, were trained and tested. Results showed a recognition accuracy of 88%, 73% and 81% for, the PD and non-PD groups, PD-related medication states, and PD severity levels relevant to MDS-UPDRS: Part III ratings, respectively.

Moticon's Summary

In their research the authors aimed to identify the severity of Parkinson's disease and patients' medication state based on gait features. Gait data was obtained using Moticon sensor insoles and used to train an machine learning model for the classification of disease and medication state. Furthermore, the accuracy of predicting severity and medication state was compared to expert evaluation and exhibited promising concurrency.

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