Di Coste, F

La Pietra, G

Pinardi, S

English

Institutional Research Information System University of Turin

07 January 2023AperTO - Archivio Istituzionale Open Access dell'Università di TorinoOriginal Citation:New Approaches to Personalized Training Programs for Chronic PatientsTerms of use:Open Access(Article begins on next page)Anyone can freely access the full text of works made available as "Open Access". Works made availableunder a Creative Commons license can be used according to the terms and conditions of said license. Useof all other works requires consent of the right holder (author or publisher) if not exempted from copyrightprotection by the applicable law.Availability:This is the author's manuscriptThis version is available http://hdl.handle.net/2318/1869643 since 2022-07-15T17:45:47Z MEDICINE 4.0: NEW APPROACHES TO PERSONALIZED TRAINING PROGRAMS FOR CHRONIC PATIENTS.  Pinardi S1, Di Coste F2, La Pietra G3   1,3 Educational Factory srl, Milano, Italy 2Associazione Italiana Health Coaching, Roma, Italy 1 corresponding author.  Abstract  The personal sensors are now commonly found in smartphones, bracelet, smart watches, and other similar market-devices. These electronic artefacts can profitably be used to detect and classify human movements in everyday life, especially in patient with chronic disease like Multiple Sclerosis’, Parkinson’s, Alzheimer’s. The practical benefits are evident:  wearable instruments can follow the patient everywhere, they  are non intrusive and respect intimacy. This enables a new approach to telemedicine and medicine 4.0: continuous monitoring of the patients activities during daily life using off-the-shelf tools, can be exploited to evaluate biomechanical capabilities, human behavior, and even cognitive abilities. On the other hand, these methodologies rise important questions: are the relative results reliable enough to ensure a correct medical diagnosis and to support the therapy, and under which conditions and why?   A recent proposed approach, the Ff x IVFf transform for sensor analysis,  a supervised machine-learning method  developed by one of the authors, has made it possible to achieve high precision on inertial and environmental sensors in real-time analysis. Thanks to this method  it is also possible to reduce the number of worn body sensors without losing overall accuracy (98.4%, p < 0.01, with only two wearables). On this evidence we  implement a new model of healthcare that is based on patient involvement, where assistance is brought from the clinic to the home with invariance in the quality of patient services, giving social, psychological and economic benefits.  The use of intelligent analysis give flexibility to the approach and objectivity to data.   The continuous monitoring allows to identify epiphenomena that may not be noticed in a normal medical scrutiny.  Cluster analysis on these data allows the patient to be profiled on objective aspects and to tune medical and pharmacological therapies to measurable evidences and individual responses.   Such an approach – intelligent, adaptive and innovative –  can dramatically reduce socio-assistive costs by eliminating (or at least reducing) the need  for  “in-person” care,  and reducing the time of hospitalization of the patient,  introducing  a new type and more personalized form of social medicine enhanced by intelligent approaches.       References Sandroff BM, Motl RW, Scudder MR, DeLuca J. Systematic, Evidence-Based Review of Exercise, Physical Activity, and Physical Fitness Effects on Cognition in Persons with Multiple Sclerosis. Neuropsychol Rev. 2016 Sep;26(3):271-294   Motl RW, Sandroff BM, DeLuca J. Exercise Training and Cognitive Rehabilitation: A Symbiotic Approach for Rehabilitating Walking and Cognitive Functions in Multiple Sclerosis? Neurorehabil Neural Repair. 2016 Jul;30(6):499-511   D'Amico E, Leone C, Hayrettin T, Patti F. Can we define a rehabilitation strategy for cognitive impairment in progressive multiple sclerosis? A critical appraisal. Mult Scler. 2016 Apr;22(5):581-9   Pinardi S, Sartori F, Melen R, Integrating Knowledge Artifacts And Inertial Measurement Units sensors for decision support,  KITA, Portugal, 2016.  Mileo A, Pinardi S, and Bisiani R, Movement Recognition using Context: Lexical Approach Based on Coherence, MCR 2010, Lisbon, August 2010.  Pinardi S, Bisiani R, Movement Recognition, a  Lexical approach, Proceedings of the 6th Int. Conf. on Intelligent Environments, Vol 8, pp.170-177, IOS Press 2010, Kuala Lumpur, Malaysia, 18-19 July 2010.   

New Approaches to Personalized Training Programs for Chronic Patients

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New Approaches to Personalized Training Programs for Chronic Patients

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