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      A New Paradigm in Parkinson's Disease Evaluation With Wearable Medical Devices: A Review of STAT-ON TM


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          In the past decade, the use of wearable medical devices has been a great breakthrough in clinical practice, trials, and research. In the Parkinson's disease field, clinical evaluation is time limited, and healthcare professionals need to rely on retrospective data collected through patients' self-filled diaries and administered questionnaires. As this often leads to inaccurate evaluations, a more objective system for symptom monitoring in a patient's daily life is claimed. In this regard, the use of wearable medical devices is crucial. This study aims at presenting a review on STAT-ON TM, a wearable medical device Class IIa, which provides objective information on the distribution and severity of PD motor symptoms in home environments. The sensor analyzes inertial signals, with a set of validated machine learning algorithms running in real time. The device was developed for 12 years, and this review aims at gathering all the results achieved within this time frame. First, a compendium of the complete journey of STAT-ON TM since 2009 is presented, encompassing different studies and developments in funded European and Spanish national projects. Subsequently, the methodology of database construction and machine learning algorithms design and development is described. Finally, clinical validation and external studies of STAT-ON TM are presented.

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          A tutorial on support vector regression

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            Parkinson's disease.

            Parkinson's disease is a neurological disorder with evolving layers of complexity. It has long been characterised by the classical motor features of parkinsonism associated with Lewy bodies and loss of dopaminergic neurons in the substantia nigra. However, the symptomatology of Parkinson's disease is now recognised as heterogeneous, with clinically significant non-motor features. Similarly, its pathology involves extensive regions of the nervous system, various neurotransmitters, and protein aggregates other than just Lewy bodies. The cause of Parkinson's disease remains unknown, but risk of developing Parkinson's disease is no longer viewed as primarily due to environmental factors. Instead, Parkinson's disease seems to result from a complicated interplay of genetic and environmental factors affecting numerous fundamental cellular processes. The complexity of Parkinson's disease is accompanied by clinical challenges, including an inability to make a definitive diagnosis at the earliest stages of the disease and difficulties in the management of symptoms at later stages. Furthermore, there are no treatments that slow the neurodegenerative process. In this Seminar, we review these complexities and challenges of Parkinson's disease.
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              The Nature of Statistical Learning Theory


                Author and article information

                Front Neurol
                Front Neurol
                Front. Neurol.
                Frontiers in Neurology
                Frontiers Media S.A.
                02 June 2022
                : 13
                : 912343
                [1] 1Sense4Care S.L. , Cornellà de Llobregat, Spain
                [2] 2Technical Research Centre for Dependency Care and Autonomous Living, Universitat Politecnica de Catalunya , Barcelona, Spain
                [3] 3Department of Investigation, Consorci Sanitari Alt Penedès - Garraf , Vilanova i la Geltrú, Spain
                Author notes

                Edited by: Emilia Mabel Gatto, Sanatorio de la Trinidad Mitre, Argentina

                Reviewed by: Francesco Asci, Mediterranean Neurological Institute Neuromed (IRCCS), Italy; Luigi Borzì, Politecnico di Torino, Italy

                *Correspondence: Daniel Rodríguez-Martín daniel.rodriguez@ 123456sense4care.com

                This article was submitted to Movement Disorders, a section of the journal Frontiers in Neurology

                Copyright © 2022 Rodríguez-Martín, Cabestany, Pérez-López, Pie, Calvet, Samà, Capra, Català and Rodríguez-Molinero.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                : 05 April 2022
                : 22 April 2022
                Page count
                Figures: 8, Tables: 2, Equations: 0, References: 141, Pages: 21, Words: 15557

                wearables,accelerometer,machine learning (ml),parkinson's disease,medical device
                wearables, accelerometer, machine learning (ml), parkinson's disease, medical device


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