Development of a portable robot and graphical user interface for haptic rehabilitation exercise

Huq, Rajibul; Lu, Elaine; Wang, Rosalie H.; Mihailidis, Alex

doi:10.1109/biorob.2012.6290273

Cited by 12 publications

(3 citation statements)

References 14 publications

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“…The intervention phase consisted of eight weeks of training with an upper limb robotic rehabilitation device in an outpatient clinic, in a therapy program that combined therapy goal setting and ''homework.'' The robot used was an end-effector table-top robot, described in Lu et al 21 and Huq et al 22,23 and shown in Figure 1(a). Figure 1(b) provides a timeline of the sessions involved in the study and shows which time points were used for the analysis presented here.…”

Section: Data Collectionmentioning

confidence: 99%

Smallest real differences for robotic measures of upper extremity function after stroke: Implications for tracking recovery

Zariffa

Myers

Coahran

et al. 2018

Journal of Rehabilitation and Assistive Technologies Engineerin

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Introduction Measurements from upper limb rehabilitation robots could guide therapy progression, if a robotic assessment’s measurement error was small enough to detect changes occurring on a time scale of a few days. To guide this determination, this study evaluated the smallest real differences of robotic measures, and of clinical outcome assessments predicted from these measures. Methods A total of nine older chronic stroke survivors took part in 12-week study with an upper-limb end-effector robot. Fourteen robotic measures were extracted, and used to predict Fugl-Meyer Assessment-Upper Extremity (FMA-UE) and Action Research Arm Test (ARAT) scores using multilinear regression. Smallest real differences and intraclass correlation coefficients were computed for the robotic measures and predicted clinical outcomes, using data from seven baseline sessions. Results Smallest real differences of robotic measures ranged from 8.8% to 26.9% of the available range. Smallest real differences of predicted clinical assessments varied widely depending on the regression model (1.3 to 36.2 for FMA-UE, 1.8 to 59.7 for ARAT), and were not strongly related to a model’s predictive performance or to the smallest real differences of the model inputs. Models with acceptable predictive performance as well as low smallest real differences were identified. Conclusions Smallest real difference evaluations suggest that using robotic assessments to guide therapy progression is feasible.

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Section: Data Collectionmentioning

confidence: 99%

Smallest real differences for robotic measures of upper extremity function after stroke: Implications for tracking recovery

Zariffa

Myers

Coahran

et al. 2018

Journal of Rehabilitation and Assistive Technologies Engineerin

View full text Add to dashboard Cite

show abstract

“…There is a wide range of neurological conditions, but the research described in this paper will focus on adults with stroke. In adulthood, stroke is one of the major causes of disability [8,9]. In the UK alone more than 100,000 people have a stroke each year (currently 1.3 million survivors in the UK) [10] at an estimated cost that exceeds £26 billion per year [11].…”

Section: Introductionmentioning

confidence: 99%

Making Best Use of Home-Based Rehabilitation Robots

2022

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Large-scale clinical trials have shown that rehabilitation robots are as affective as conventional therapy, but the cost-effectiveness is preventing their uptake. This study investigated whether a low-cost rehabilitation robot could be deployed in a home setting for rehabilitation of people recovering from stroke (n = 16) and whether clinical outcome measures correlated well with kinematic measures gathered by the robot. The results support the feasibility of patients independently using the robot with improvement in both clinical measures and kinematic data. We recommend using kinematic data early in an intervention to detect improvement while using a robotic device. The kinematic measures in the assessment task (hits/minute and normalised jerk) adequately pick up changes within a four-week period, thus allowing the rehabilitation regime to be adapted to suit the user’s needs. Estimating the long-term clinical benefit must be explored in future research.

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“…However, in the presence of a variable admittance environment (i.e., different patients) or different trajectories (i.e., robot configurations), the interaction force and configuration will exacerbate inefficiency of the controller with non-optimal gains. For example, a resistive-capacitive impedance control with therapist-adjustable constant stiffness and damping ratios is implemented in the upper extremity rehabilitation manipulandum from the Toronto Rehabilitation Institute (TRI) and Quanser Consulting Inc., but these gains cannot be adjusted optimally using trial and error by the therapist (Huq et al, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Configuration-Dependent Optimal Impedance Control of an Upper Extremity Stroke Rehabilitation Manipulandum

2018

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Development of a portable robot and graphical user interface for haptic rehabilitation exercise

Cited by 12 publications

References 14 publications

Smallest real differences for robotic measures of upper extremity function after stroke: Implications for tracking recovery

Smallest real differences for robotic measures of upper extremity function after stroke: Implications for tracking recovery

Making Best Use of Home-Based Rehabilitation Robots

Configuration-Dependent Optimal Impedance Control of an Upper Extremity Stroke Rehabilitation Manipulandum

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