Statistical Analysis and Kinematic Assessment of Upper Limb Reaching Task in Parkinson’s Disease

Abstract

The impact of neurodegenerative disorders is twofold; they affect both quality of life and healthcare expenditure. In the case of Parkinson’s disease, several strategies have been attempted to support the pharmacological treatment with rehabilitation protocols aimed at restoring motor function. In this scenario, the study of upper limb control mechanisms is particularly relevant due to the complexity of the joints involved in the movement of the arm. For these reasons, it is difficult to define proper indicators of the rehabilitation outcome. In this work, we propose a methodology to analyze and extract an ensemble of kinematic parameters from signals acquired during a complex upper limb reaching task. The methodology is tested in both healthy subjects and Parkinson’s disease patients (N = 12), and a statistical analysis is carried out to establish the value of the extracted kinematic features in distinguishing between the two groups under study. The parameters with the greatest number of significances across the submovements are duration, mean velocity, maximum velocity, maximum acceleration, and smoothness. Results allowed the identification of a subset of significant kinematic parameters that could serve as a proof-of-concept for a future definition of potential indicators of the rehabilitation outcome in Parkinson’s disease.

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

“…2 shows, smoothness and duration demonstrated both informative. These results are consistent with those found in the previous study [55] where the statical analysis revealed both smoothness and duration were statistically significant. It is worth noting the among the top-5 features both the BLR and the ML reported maximum jerk.…”

“…Signal processing has been carried out in Matlab (Math-Works, R2021a, Natick, MA, USA). In accordance with other methodological approaches to process motion signals proposed in the literature [55], [59], [60], [61], [62], after the signal acquisition, velocity, acceleration, and jerk profiles have been estimated from discrete position signal versus time information by means of a derivative operation. Based on the velocity profile, eight submovements have been detected by means of an iterative algorithm aimed at identifying the onset and offset of each submovement from the detection of local minima and maxima of the velocity curve above and below a predetermined threshold.…”

“…In our previous paper [55], we have extracted, from the available motion signals, a set of features which have been demonstrated, through a statistical analysis, to be useful for distinguishing PD patients from healthy controls. Starting from the study developed in [55], the main contribution of this paper is assessing whether these features may serve as a basis to define a potential predictive model to diagnose PD.…”

“…In particular, ML models are implemented twice. The first analysis consists in including all the features from [55] and model the data by using a leaveone-out cross-validation method. In the second phase, the dataset is split into two parts, as per hold-out cross-validation, and the wrapper method is exploited in order to find the best subset of features that could maximize the model performance,…”

The Use of Head-Mounted Display Systems for Upper Limb Kinematic Analysis in Post-Stroke Patients: A Perspective Review on Benefits, Challenges and Other Solutions

“…Conversely, complex kinematic analyses, like upper limb reaching tasks that involve numerous degrees of freedom, require the integration of multiple sensors and more sophisticated mathematical models [45].…”