Multimodal Wearable System for Motor Rehabilitation - Design Perspective and Development

Author(s):  
Paolo Perego ◽  
Roberto Sironi ◽  
Martina Scagnoli ◽  
Marcello Fusca ◽  
Emanuele Gruppioni ◽  
...  
2020 ◽  
Author(s):  
Marcello De Angelis ◽  
Luigi Lavorgna ◽  
Antonio Carotenuto ◽  
Martina Petruzzo ◽  
Roberta Lanzillo ◽  
...  

BACKGROUND Clinical trials in multiple sclerosis (MS) have leveraged the use of digital technology to overcome limitations in treatment and disease monitoring. OBJECTIVE To review the use of digital technology in concluded and ongoing MS clinical trials. METHODS In March 2020, we searched for “multiple sclerosis” and “trial” on pubmed.gov and clinicaltrials.gov using “app”, “digital”, “electronic”, “internet” and “mobile” as additional search words, separately. Overall, we included thirty-five studies. RESULTS Digital technology is part of clinical trial interventions to deliver psychotherapy and motor rehabilitation, with exergames, e-training, and robot-assisted exercises. Also, digital technology has become increasingly used to standardise previously existing outcome measures, with automatic acquisitions, reduced inconsistencies, and improved detection of symptoms. Some trials have been developing new patient-centred outcome measures for the detection of symptoms and of treatment side effects and adherence. CONCLUSIONS We will discuss how digital technology has been changing MS clinical trial design, and possible future directions for MS and neurology research.


Author(s):  
Negin Hamzeheinejad ◽  
Daniel Roth ◽  
Samantha Monty ◽  
Julian Breuer ◽  
Anuschka Rodenbergc ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2146
Author(s):  
Manuel Andrés Vélez-Guerrero ◽  
Mauro Callejas-Cuervo ◽  
Stefano Mazzoleni

Processing and control systems based on artificial intelligence (AI) have progressively improved mobile robotic exoskeletons used in upper-limb motor rehabilitation. This systematic review presents the advances and trends of those technologies. A literature search was performed in Scopus, IEEE Xplore, Web of Science, and PubMed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology with three main inclusion criteria: (a) motor or neuromotor rehabilitation for upper limbs, (b) mobile robotic exoskeletons, and (c) AI. The period under investigation spanned from 2016 to 2020, resulting in 30 articles that met the criteria. The literature showed the use of artificial neural networks (40%), adaptive algorithms (20%), and other mixed AI techniques (40%). Additionally, it was found that in only 16% of the articles, developments focused on neuromotor rehabilitation. The main trend in the research is the development of wearable robotic exoskeletons (53%) and the fusion of data collected from multiple sensors that enrich the training of intelligent algorithms. There is a latent need to develop more reliable systems through clinical validation and improvement of technical characteristics, such as weight/dimensions of devices, in order to have positive impacts on the rehabilitation process and improve the interactions among patients, teams of health professionals, and technology.


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