Detection of distorted gait and wearing-off phenomenon in Parkinson's disease patients during Levodopa therapy

Abstract

Levodopa (L-dopa) is the gold-standard medication and the most commonly used substance in the treatment of motor complications in Parkinson's disease (PD) patients. The “Wearing-off” phenomenon is the most frequent complication developed by long-term L-dopa therapy, which results in the reemergence of PD symptoms and lower quality of life in patients. Detecting and monitoring the onset and the duration of wearing-off alongside the persistence of the symptoms, known as “delayed-on”, would enable the patients to receive the medication changes in the required time while preventing them from extravagant use of L-dopa. Home monitoring systems using inertial measurement units have enabled us to measure gait parameters in unsupervised environments. By using patients' medication diaries and their gait parameters obtained from continuous real-world data in the course of two weeks, we developed a system to identify the distorted gait spans during L-dopa therapy utilizing personalized machine learning. Our algorithm differentiates between the two states of medication in effect and the distorted gait states with the mean accuracy of 77% ± 3.37. Furthermore, through each model's feature importance, we found that maximum sensor lift was the most prominent feature affected in the distorted gait sequences. We contribute to a better understanding of the repercussions of wearing-off episodes on gait parameters during L-dopa therapy. Moreover, our proposed system facilitates clinicians in monitoring the severity of these episodes more efficiently.