Mechanisms of Motor Recovery in Chronic and Subacute Stroke Patients Following a Robot-Aided Training

Mazzoleni, Stefano; Puzzolante, L.; Zollo, Loredana; Dario, P.; Posteraro, Federico

doi:10.1109/toh.2013.73

Cited by 25 publications

(13 citation statements)

References 25 publications

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“…Second, VR has been shown effective at improving upper limb movements for reaching and grasping tasks involving proximal segments and global arm movements, in individuals with stroke in both acute and chronic stages [ 11 , 13 ]. Third, distal fine motor control has also been effectively improved using VR, generally combined with robotic-like devices [ 2 , 15 , 16 ]. Fourth, controlled trials suggest that VR may be beneficial to improve upper limb function and performance in activities of daily living, to a greater extent than same dosage of conventional therapy [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Effect of a mixed reality-based intervention on arm, hand, and finger function on chronic stroke

Colomer

Llorens

Noé

et al. 2016

J NeuroEngineering Rehabil

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BackgroundVirtual and mixed reality systems have been suggested to promote motor recovery after stroke. Basing on the existing evidence on motor learning, we have developed a portable and low-cost mixed reality tabletop system that transforms a conventional table in a virtual environment for upper limb rehabilitation. The system allows intensive and customized training of a wide range of arm, hand, and finger movements and enables interaction with tangible objects, while providing audiovisual feedback of the participants’ performance in gamified tasks. This study evaluates the clinical effectiveness and the acceptance of an experimental intervention with the system in chronic stroke survivors.MethodsThirty individuals with stroke were included in a reversal (A-B-A) study. Phase A consisted of 30 sessions of conventional physical therapy. Phase B consisted of 30 training sessions with the experimental system. Both interventions involved flexion and extension of the elbow, wrist, and fingers, and grasping of different objects. Sessions were 45-min long and were administered three to five days a week. The body structures (Modified Ashworth Scale), functions (Motricity Index, Fugl-Meyer Assessment Scale), activities (Manual Function Test, Wolf Motor Function Test, Box and Blocks Test, Nine Hole Peg Test), and participation (Motor Activity Log) were assessed before and after each phase. Acceptance of the system was also assessed after phase B (System Usability Scale, Intrinsic Motivation Inventory).ResultsSignificant improvement was detected after the intervention with the system in the activity, both in arm function measured by the Wolf Motor Function Test (p < 0.01) and finger dexterity measured by the Box and Blocks Test (p < 0.01) and the Nine Hole Peg Test (p < 0.01); and participation (p < 0.01), which was maintained to the end of the study. The experimental system was reported as highly usable, enjoyable, and motivating.ConclusionsOur results support the clinical effectiveness of mixed reality interventions that satisfy the motor learning principles for upper limb rehabilitation in chronic stroke survivors. This characteristic, together with the low cost of the system, its portability, and its acceptance could promote the integration of these systems in the clinical practice as an alternative to more expensive systems, such as robotic instruments.Electronic supplementary materialThe online version of this article (doi:10.1186/s12984-016-0153-6) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Effect of a mixed reality-based intervention on arm, hand, and finger function on chronic stroke

Colomer

Llorens

Noé

et al. 2016

J NeuroEngineering Rehabil

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“…Robotic devices have been identified as an effective means to administer [ 8 – 10 ] or complement these therapeutic treatments [ 11 ], and neuroprosthetics [ 12 , 13 ] may directly enhance motor function. By shifting to robotics-based treatments, physical therapists may be able to simultaneously treat a greater number of patients and obtain performance measures from the data gathered through the robotic device’s sensors [ 14 ], which has been demonstrated to be useful in assessing motor performance [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Increasing Patient Engagement in Rehabilitation Exercises Using Computer-Based Citizen Science

et al. 2015

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Patient motivation is an important factor to consider when developing rehabilitation programs. Here, we explore the effectiveness of active participation in web-based citizen science activities as a means of increasing participant engagement in rehabilitation exercises, through the use of a low-cost haptic joystick interfaced with a laptop computer. Using the joystick, patients navigate a virtual environment representing the site of a citizen science project situated in a polluted canal. Participants are tasked with following a path on a laptop screen representing the canal. The experiment consists of two conditions: in one condition, a citizen science component where participants classify images from the canal is included; and in the other, the citizen science component is absent. Both conditions are tested on a group of young patients undergoing rehabilitation treatments and a group of healthy subjects. A survey administered at the end of both tasks reveals that participants prefer performing the scientific task, and are more likely to choose to repeat it, even at the cost of increasing the time of their rehabilitation exercise. Furthermore, performance indices based on data collected from the joystick indicate significant differences in the trajectories created by patients and healthy subjects, suggesting that the low-cost device can be used in a rehabilitation setting for gauging patient recovery.

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“…This distinguishes our approach from previous methods for kinematic movement assessment that were acquired during speci c exercises with more complex movements [42]. Furthermore, various biomechanical parameters are often used for classi cation such as inter-joint coordination [43,44], temporal e ciency [45][46][47], movement speed [48][49][50], smoothness [51] or accuracy [52]. Such classi cation algorithms tend to be rather device-speci c and are not easily transferable to other hard-and software settings.…”

Section: Discussionmentioning

confidence: 99%

Clinical Validation of Kinematic Assessments of Post-Stroke Upper Limb Movements with a Multi-Joint Arm Exoskeleton

Grimm

Kraugmann

Naros

et al. 2020

Preprint

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Background: Robotic and gravity-balancing exoskeletons, originally designed for the rehabilitation training of neurological patients, are now being increasingly applied in objective and fine-grained sensor-based assessments of upper limb function. However, gravity compensation, inertia and damping properties of the exoskeleton interfere with the natural sensorimotor interaction, proprioceptive and visual feedback during movement execution. This may endanger the validity of the kinematic assessments in relation to the clinical outcome measures that they were supposed to reflect. Here, we appliedMethods: In a proof of concept study involving nineteen severely impaired chronic stroke patients, we assessed sensor-based kinematic data acquired with a multi-joint arm exoskeleton and compared it to the clinical outcome measure Upper Extremity Fugl-Meyer Assessment (UE-FMA) scale. During this assessment, real-time movement feedback of the system’s seven degrees of freedom was provided with a biomorphic 3D virtual representation of the upper limb, including the proximal component of the arm. To align posture and to minimize the exoskeleton-patient interaction, the same position (neutral zero) with a distance of 90 degrees between forearm and upper arm was taken as the starting position for all assessments. Within self-contained tasks, we assessed separately and subsequently the range of motion/spatial posture of four single joints (i.e., joint angles of wrist, elbow, arm, and shoulder movement) and the closing and opening of the hand with a pressure sensor placed in the handle.Results: A strong correlation was observed between wrist and elbow movements within the kinematic parameters (r > 0.7, p<0.003; Bonferroni corrected). A multiple regression model predicted the UE-FMA significantly (F (5, 13) = 12.22, p < 0.0005, adj. R2 = 0.83). Both shoulder rotation and grip pressure added significantly (p < 0.05) to the prediction with the standardized coefficients β of 0.55 and 0.38, respectively.Conclusions: Exoskeleton-based evaluation of single-joint movements and grip force facilitates the assessment of upper limb kinematics after stroke with high structural and convergent validity. Proximal and distal measures may contribute independently to the prediction of the clinical status.

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Mechanisms of Motor Recovery in Chronic and Subacute Stroke Patients Following a Robot-Aided Training

Cited by 25 publications

References 25 publications

Effect of a mixed reality-based intervention on arm, hand, and finger function on chronic stroke

Effect of a mixed reality-based intervention on arm, hand, and finger function on chronic stroke

Increasing Patient Engagement in Rehabilitation Exercises Using Computer-Based Citizen Science

Clinical Validation of Kinematic Assessments of Post-Stroke Upper Limb Movements with a Multi-Joint Arm Exoskeleton

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