scholarly journals Dynamic Characterization and Interaction Control of the CBM-Motus Robot for Upper-Limb Rehabilitation

10.5772/56928 ◽  
2013 ◽  
Vol 10 (10) ◽  
pp. 374 ◽  
Author(s):  
Loredana Zollo ◽  
Antonino Salerno ◽  
Massimo Vespignani ◽  
Dino Accoto ◽  
Massimiliano Passalacqua ◽  
...  
2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Qing Miao ◽  
Mingming Zhang ◽  
Yupu Wang ◽  
Sheng Q. Xie

This paper proposed a bilateral upper-limb rehabilitation device (BULReD) with two degrees of freedom (DOFs). The BULReD is portable for both hospital and home environment, easy to use for therapists and patients, and safer with respect to upper-limb robotic exoskeletons. It was implemented to be able to conduct both passive and interactive training, based on system kinematics and dynamics, as well as the identification of real-time movement intention of human users. Preliminary results demonstrate the potential of the BULReD for clinical applications, with satisfactory position and interaction force tracking performance. Future work will focus on the clinical evaluation of the BULReD on a large sample of poststroke patients.


ROBOT ◽  
2011 ◽  
Vol 33 (3) ◽  
pp. 307-313 ◽  
Author(s):  
Baoguo XU ◽  
Si PENG ◽  
Aiguo SONG

ROBOT ◽  
2012 ◽  
Vol 34 (5) ◽  
pp. 539 ◽  
Author(s):  
Lizheng PAN ◽  
Aiguo SONG ◽  
Guozheng XU ◽  
Huijun LI ◽  
Baoguo XU

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.


2021 ◽  
Vol 18 (4) ◽  
pp. 857-871
Author(s):  
Elio Matteo Curcio ◽  
Giuseppe Carbone

AbstractThis paper addresses the design of a novel bionic robotic device for upper limb rehabilitation tasks at home. The main goal of the design process has been to obtain a rehabilitation device, which can be easily portable and can be managed remotely by a professional therapist. This allows to treat people also in regions that are not easily reachable with a significant cost reduction. Other potential benefits can be envisaged, for instance, in the possibility to keep social distancing while allowing rehabilitation treatments even during a pandemic spread. Specific attention has been devoted to design the main mechatronic components by developing specific kinematics and dynamics models. The design process includes the implementation of a specific control hardware and software. Preliminary experimental tests are reported to show the effectiveness and feasibility of the proposed design solution.


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