Dynamic Weighted Bar for Upper Limb Rehabilitation

2016 ◽  
Vol 10 (4) ◽  
Author(s):  
Taylor C. Hornung ◽  
Stephen J. Piazza ◽  
Everett C. Hills ◽  
Jason Z. Moore

This paper explores the design of a dynamically weighted therapy bar, which can provide real-time quantitative performance information and adjustments during rehabilitation exercise. In contrast, typical therapy equipment is passive, offering no feedback to the patient or clinician. The dynamic weighted bar (DWB) was designed and fabricated containing an inertial sensor which tracks the orientation of the bar and adjusts the position of an internal weight accordingly, in turn providing a targeted force imbalance between the patient's two arms. Step input experiments were performed on the device while it was held in various stationary positions. The DWB was able to successfully function and transmit motion information. It was able to produce a center of mass shift of 101.6 mm, and a complete travel time between 0.96 s and 1.41 s over the entire length. The use of the DWB device can offer many benefits during rehabilitation including access to more quantitative information for clinicians as well as the potential for more personalized therapy programs.

2016 ◽  
Vol 833 ◽  
pp. 196-201 ◽  
Author(s):  
Shahrol Mohamaddan ◽  
Annisa Jamali ◽  
Noor Aliah Abd Majid ◽  
Mohamad Syazwan Zafwan Mohamad Suffian

Stroke is the third largest cause of death in Malaysia. Different approaches including hardware development and simulation were conducted to support the conventional rehabilitation courses. New upper limb rehabilitation robot prototype was developed for this research. The prototype consists of horizontal and vertical movement exercise. The prototype was modeled and simulated using ergonomics optimization software known as AnyBody. This paper presents the analysis of human upper limb muscles during rehabilitation exercise using virtual human model. The result shows that eleven muscle areas were affected during the rehabilitation exercise using new prototype.


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.


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