Development of a revolute-type kinematic model for human upper limb using a matrix approach

Gillawat, Anil Kumar

doi:10.1017/s0263574721001387

Cited by 1 publication

(1 citation statement)

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“…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.…”

Section: A Data and Signal Processingmentioning

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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