Design and Execution of Integrated Clinical Pathway: A Simplified Meta-Model and Associated Methodology

Ardito, Carmelo; Caivano, Danilo; Colizzi, Lucio; Dimauro, Giovanni; Verardi, Loredana

doi:10.3390/info11070362

Cited by 8 publications

(2 citation statements)

References 36 publications

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“…Therefore, the proposed featurebased approach can pave the way for clinical assessments of pathologies that determine either motor or cognitive impairments: for instance, VMA capabilities can be assessed to study the alteration to the sense of agency in children with cerebral palsy. In so doing, it would be possible to integrate and enhance the conventional rehabilitative programs by giving additional biomarkers and thus propose new solutions to integrate the existing clinical pathways [75]. In addition, an HMD may be used to design balance experiments while offering an even higher sense of embodiment through a more immersive experience and allowing for the simulation of high fidelity in a safe manner [76][77][78][79].…”

Section: Discussionmentioning

confidence: 99%

A Serious Game for the Assessment of Visuomotor Adaptation Capabilities during Locomotion Tasks Employing an Embodied Avatar in Virtual Reality

Suglia,

Brunetti,

Pasquini

et al. 2023

Sensors

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The study of visuomotor adaptation (VMA) capabilities has been encompassed in various experimental protocols aimed at investigating human motor control strategies and/or cognitive functions. VMA-oriented frameworks can have clinical applications, primarily in the investigation and assessment of neuromotor impairments caused by conditions such as Parkinson’s disease or post-stroke, which affect the lives of tens of thousands of people worldwide. Therefore, they can enhance the understanding of the specific mechanisms of such neuromotor disorders, thus being a potential biomarker for recovery, with the aim of being integrated with conventional rehabilitative programs. Virtual Reality (VR) can be entailed in a framework targeting VMA since it allows the development of visual perturbations in a more customizable and realistic way. Moreover, as has been demonstrated in previous works, a serious game (SG) can further increase engagement thanks to the use of full-body embodied avatars. Most studies implementing VMA frameworks have focused on upper limb tasks and have utilized a cursor as visual feedback for the user. Hence, there is a paucity in the literature about VMA-oriented frameworks targeting locomotion tasks. In this article, the authors present the design, development, and testing of an SG-based framework that addresses VMA in a locomotion activity by controlling a full-body moving avatar in a custom VR environment. This workflow includes a set of metrics to quantitatively assess the participants’ performance. Thirteen healthy children were recruited to evaluate the framework. Several quantitative comparisons and analyses were run to validate the different types of introduced visuomotor perturbations and to evaluate the ability of the proposed metrics to describe the difficulty caused by such perturbations. During the experimental sessions, it emerged that the system is safe, easy to use, and practical in a clinical setting. Despite the limited sample size, which represents the main limitation of the study and can be compensated for with future recruitment, the authors claim the potential of this framework as a useful instrument for quantitatively assessing either motor or cognitive impairments. The proposed feature-based approach gives several objective parameters as additional biomarkers that can integrate the conventional clinical scores. Future studies might investigate the relation between the proposed biomarkers and the clinical scores for specific disorders such as Parkinson’s disease and cerebral palsy.

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Section: Discussionmentioning

confidence: 99%

A Serious Game for the Assessment of Visuomotor Adaptation Capabilities during Locomotion Tasks Employing an Embodied Avatar in Virtual Reality

Suglia,

Brunetti,

Pasquini

et al. 2023

Sensors

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“…Therefore, high expectations are placed in the use of such techniques also for the improvement of health care and clinical practice [ 31 , 32 , 33 , 34 , 35 , 36 ]. Furthermore, numerous portable devices for personal and frequent monitoring of cardiac activity, such as Kardia [ 37 ], D-hearth [ 38 ], and eKuore [ 39 ], are spreading.…”

Section: Introductionmentioning

confidence: 99%

Personal Heart Health Monitoring Based on 1D Convolutional Neural Network

et al. 2021

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The automated detection of suspicious anomalies in electrocardiogram (ECG) recordings allows frequent personal heart health monitoring and can drastically reduce the number of ECGs that need to be manually examined by the cardiologists, excluding those classified as normal, facilitating healthcare decision-making and reducing a considerable amount of time and money. In this paper, we present a system able to automatically detect the suspect of cardiac pathologies in ECG signals from personal monitoring devices, with the aim to alert the patient to send the ECG to the medical specialist for a correct diagnosis and a proper therapy. The main contributes of this work are: (a) the implementation of a binary classifier based on a 1D-CNN architecture for detecting the suspect of anomalies in ECGs, regardless of the kind of cardiac pathology; (b) the analysis was carried out on 21 classes of different cardiac pathologies classified as anomalous; and (c) the possibility to classify anomalies even in ECG segments containing, at the same time, more than one class of cardiac pathologies. Moreover, 1D-CNN based architectures can allow an implementation of the system on cheap smart devices with low computational complexity. The system was tested on the ECG signals from the MIT-BIH ECG Arrhythmia Database for the MLII derivation. Two different experiments were carried out, showing remarkable performance compared to other similar systems. The best result showed high accuracy and recall, computed in terms of ECG segments and even higher accuracy and recall in terms of patients alerted, therefore considering the detection of anomalies with respect to entire ECG recordings.

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