A survey on robotic devices for upper limb rehabilitation

Maciejasz, Paweł; Eschweiler, Jörg; Gerlach-Hahn, Kurt; Jansen-Troy, Arne; Leonhardt, Steffen

doi:10.1186/1743-0003-11-3

Cited by 892 publications

(608 citation statements)

References 159 publications

(161 reference statements)

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“…Our findings determined that the precision of the participants was significantly better, which may be due to the ease of delimiting the exercise with the Novint Falcon haptic device [44], [45]. However, no significant differences were found in the time of completion of the exercise.…”

Section: Discussionmentioning

confidence: 41%

Haptic Feedback in Motor Hand Virtual Therapy Increases Precision and Generates Less Mental Workload

Ramírez-Fernández¹,

Morán²,

García–Canseco³

2015

Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare

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Abstract-In this work we show that haptic feedback in upper limb motor therapy improves performance and generates a lower mental workload. To demonstrate this, two groups of participants (healthy adults and elders with hand motor problems) used a low-cost haptic device (Novint Falcon) and a non-robotic device (Leap Motion Controller). Participants conducted the same rehabilitation task by using a non-immersive virtual environment. Results show significant differences for all participants regarding precision on the use of the haptic feedback device. Additionally, participants in the older adult group demonstrated a lower mental workload while using the haptic device (Novint Falcon). Finally, qualitative results show that participants preferred to conduct their therapy exercises by using the haptic device, as they found it more useful, easier to use and easier to learn.

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Section: Discussionmentioning

confidence: 41%

Haptic Feedback in Motor Hand Virtual Therapy Increases Precision and Generates Less Mental Workload

Ramírez-Fernández¹,

Morán²,

García–Canseco³

2015

Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare

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“…holding a toothbrush) [18]. During typical ADL, the ranges of motion for fingers' joints are usually in the range [-10,100] • and forces at the fingertips are within the range [0,10] N [8]. These tasks do not impose strict requirements on the movements of single phalanxes, but rather require hand compliance in order to allow adaptation to different objects and shapes.…”

Section: ) Design Requirementsmentioning

confidence: 99%

“…Several studies have shown that manual tasks induce changes, with respect to baseline states, in the spectral power of sensorimotor cortex areas, for two characteristic frequency bands: µ ( [8,12] Hz) and β ( [16,32] Hz). Such results have been confirmed in invasive [23] and non-invasive [24] studies.…”

Section: Brain-machinementioning

confidence: 99%

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mano: A Wearable Hand Exoskeleton for Activities of Daily Living and Neurorehabilitation

Randazzo

Iturrate

Perdikis

et al. 2018

IEEE Robot. Autom. Lett.

110

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Abstract-Hand sensorimotor impairments are among the most common consequences of injuries affecting the central and peripheral nervous systems, leading to a drastic reduction in the quality of life for affected individuals. Combining wearable robotic exoskeletons and human-machine interfaces is a promising avenue for the restoration and substitution of lost and impaired functions for these users. In this study, we present a novel hand exoskeleton, mano, designed to assist and restore hand functions of people with motor disabilities during activities of daily living (ADL) and in neurorehabilitative scenarios. Compared to state-of-the-art devices, our system is fully wearable, portable and minimally obtrusive on the hand. The exoskeleton can actively control flexion and extension of all fingers, while allowing natural somatosensorial interactions with the environment surrounding the users. We evaluated the device from four different perspectives. A mechanical characterization, showing that the exoskeleton can cover more than 70% of healthy hand workspace and it can achieve forces at the fingertips sufficient for ADL. A functional characterization, where we showed how two users who suffered from spinal cord injuries were able to perform several ADL for the first time since their accidents. Thirdly, we evaluated the system from a neuroimaging perspective, showing that the device can elicit EEG brain patterns typical of natural hand motions. We finally exemplified the control of the hand exoskeleton within an exemplar framework, a brain-machine interface scenario, showing how motor intention can be successfully decoded for a continuous control of the device. Overall, our results showed that the device represents an ecological solution for use both in ADL and in scenarios aimed at promoting sensorimotor recovery.

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“…We can classify rehabilitation robots into 2 categories [8]: the exoskeleton type and the end effector type [9]. For the end-effector type, the patient's limb is attached to the robot arm only at the end-point or endpoint gripper.…”

Section: Introductionmentioning

confidence: 99%

A Novel Design and Implementation of a 4-DOF Upper Limb Exoskeleton for Stroke Rehabilitation with Active Assistive Control Strategy

Sutapun¹,

Sangveraphunsiri²

2017

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Abstract. We developed a robot, CUREs (Chulalongkorn University Rehabilitation Robotic Exoskeleton system), for upper extremity rehabilitation. The active assistive control strategy based on the impedance force control is developed and implemented to obtain assistive-resistive paths tracking for rehabilitation activities. The desired trajectory or rehabilitated training pattern for each specific patient need to be assigned first by a medical doctor and a physical therapy. The therapist can program the desired trajectory by guiding the patient arm based on the assigned path pattern and the set of via points will be stored and used for generating the desired trajectory. The desired trajectory will be stored specific for the patient and can be called back anytime. During the rehabilitation, the robot can assist and resist the patient's arm to follow the desired trajectory. If the patient has difficulty moving his arm to track the desired path, the robot will help by adding more torque to help the patient to move his arm to reduce the error between the desired path and the actual posture. And if the patient himself can move his arm tracking the desired path, the robot will not apply any more force to assist or resist. The necessary state variables such as angular position and torque can be recorded during the training. The main purpose of the experiment, follow the medical ethic, is to assure that there is no side effect for using this rehabilitation robot. Five subacute stroke patients participated in this pilot study. All patients have severe upper extremity weakness. The medical doctor will assign the training pattern based on patient condition. The result showed that the Fugl-Meyer Assessment Upper Extremity Scale was improved after 10 days of training in all participants without any sign of side effect.

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A survey on robotic devices for upper limb rehabilitation

Cited by 892 publications

References 159 publications

Haptic Feedback in Motor Hand Virtual Therapy Increases Precision and Generates Less Mental Workload

Haptic Feedback in Motor Hand Virtual Therapy Increases Precision and Generates Less Mental Workload

mano: A Wearable Hand Exoskeleton for Activities of Daily Living and Neurorehabilitation

A Novel Design and Implementation of a 4-DOF Upper Limb Exoskeleton for Stroke Rehabilitation with Active Assistive Control Strategy

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