Upper limb smoothness evaluation in Parkinson's disease: A preliminary study on the effects of a complementary therapy

Pegolo, Elena; Romanato, M.; Riccò, C.; Cucca, A.; Spolaor, Fabiola; Volpe, D.; Sawacha, Z.

doi:10.1016/j.gaitpost.2022.07.096

Cited by 1 publication

(1 citation statement)

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“…In addition, the features extracted according to our methodology, could be also used for other purposes, since they also exhibits clinical relevance. To this regard, other researchers have recently shown, after a rehabilitative therapy of 10 weeks, that smoothness increases while jerk decreases in PD patients, testifying that these features are sensitive enough to be used for rehabilitation purposes [75], therefore suggesting that the research is already going toward this approach.…”

Section: Discussionmentioning

confidence: 99%

Using Features Extracted From Upper Limb Reaching Tasks to Detect Parkinson’s Disease by Means of Machine Learning Models

Cesarelli

Donisi

Amato

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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While in the literature there is much interest in investigating lower limbs gait of patients affected by neurological diseases, such as Parkinson's Disease (PD), fewer publications involving upper limbs movements are available. In previous studies, 24 motion signals (the socalled reaching tasks) of the upper limbs of PD patients and Healthy Controls (HCs) were used to extract several kinematic features through a custom-made software; conversely, the aim of our paper is to investigate the possibility to build models-using these features-for distinguishing PD patients from HCs. First, a binary logistic regression and, then, a Machine Learning (ML) analysis was performed by implementing five algorithms through the Knime Analytics Platform. The ML analysis was performed twice: first, a leave-one out-cross validation was applied; then, a wrapper feature selection method was implemented to identify the best subset of features that could maximize the accuracy. The binary logistic regression achieved an accuracy of 90.5%, demonstrating the importance of the maximum jerk during subjects upper limb motion; the Hosmer-Lemeshow test supported the validity of this model (p-value=0.408). The first ML analysis achieved high evaluation metrics by overcoming 95% of accuracy; the second ML analysis achieved a perfect classification with 100% of

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Section: Discussionmentioning

confidence: 99%