Kinematic Indexes’ Reproducibility of Horizontal Reaching Movements

D’Addio, Giovanni; Iuppariello, Luigi; Romano, Maria; Lullo, F.; Pappone, N.; Cesarelli, Mario

doi:10.1007/978-3-319-00846-2_20

Cited by 10 publications

(4 citation statements)

References 4 publications

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“…The latter was defined as the rate of the change of acceleration with respect to time, as described in our previous work [24]:…”

Section: Preprocessingmentioning

confidence: 99%

“…Several biomechanical parameters have been proposed in the scientific literature to date for assessing the quality of movement in healthy and diseased subjects in different rehabilitation settings and tasks [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. Moreover, several studies have aimed to exploit motion analysis data and compare different instrumentations for diagnostic purposes [32,33].…”

Section: Introductionmentioning

confidence: 99%

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

Ponsiglione

Ricciardi

Amato

et al. 2022

Sensors

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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.

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“…The latter was defined as the rate of the change of acceleration with respect to time, as described in our previous work [24]:…”

Section: Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ponsiglione

Ricciardi

Amato

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Quantitative kinematic analysis of the reaching movements has carried out estimating their velocity profile by means of a moving average derivative filter with trade-off features between a low-pass filtering and theoretical derivative high-pass transfer function [21][22][23][24][25][26][27][28].…”

Section: Signal Processingmentioning

confidence: 99%

Submovements composition and quality assessment of reaching movements in subjects with Parkinson's Disease

Iuppariello

Bifulco

Romano

et al. 2015

2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings

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The segmentation of seemingly continuous movements into segments has been theorized for many years. These segments may be considered as 'primitive' movements, or building blocks of more complex movements. The existence of these fragments, or sub-movements as they are called, has been supported by a wide range of studies over the past 100 years. Evidence for the existence of discrete sub-movements underlying continuous human movement has motivated many attempts to 'extract' them. Recently, the sub-movement theory gained a great appeal in the rehabilitation field. In fact, understanding movement deficits following CNS lesions, and the relationships between these deficits and functional ability, is fundamental to the development of successful rehabilitation therapies. So, here a novel sub-movements decomposition method is proposed; it is based on a constrained-Expectation-Maximization. This representation allowed us to explore whether the movements are built up of elementary kinematic units by decomposing each signal into a weighted combination of 2D Gaussian functions. These can be used to assess the quality of reaching movements in subjects with Parkinson's Disease

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“…Such parameters are suitable to investigate the "quality" of movement of healthy and diseased subjects; even more important, they can be monitored in different rehabilitation settings and for different tasks. Approaches in this sense have been proposed for robot-mediated or robot assisted rehabilitation exercises [21], to improve upper limb [22] or lower limb rehabilitation [23], after spinal cord injuries [24], stroke [25], or PD [26], [27], also with the aim of proposing low-cost robot platforms for physiotherapy [28], [29] and intelligent data-driven approaches for gait training [12], motion reaching tasks [30], [31], [32], and motion prediction purposes [33].…”

mentioning

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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Kinematic Indexes’ Reproducibility of Horizontal Reaching Movements

Cited by 10 publications

References 4 publications

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

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

Submovements composition and quality assessment of reaching movements in subjects with Parkinson's Disease

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

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