Posts Tagged exoskeletons

[Abstract + References] eConHand: A Wearable Brain-Computer Interface System for Stroke Rehabilitation


Brain-Computer Interface (BCI) combined with assistive robots has been developed as a promising method for stroke rehabilitation. However, most of the current studies are based on complex system setup, expensive and bulky devices. In this work, we designed a wearable Electroencephalography(EEG)-based BCI system for hand function rehabilitation of the stroke. The system consists of a customized EEG cap, a small-sized commercial amplifer and a lightweight hand exoskeleton. In addition, visualized interface was designed for easy use. Six healthy subjects and two stroke patients were recruited to validate the safety and effectiveness of our proposed system. Up to 79.38% averaged online BCI classification accuracy was achieved. This study is a proof of concept, suggesting potential clinical applications in outpatient environments.

2. E. Donchin , K. Spencer and R. Wijesinghe , “The mental prosthesis: assessing the speed of a P300-based brain-computer interface”, IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 2, pp. 174-179, 2000.

3. D. McFarland and J. Wolpaw , “Brain-Computer Interface Operation of Robotic and Prosthetic Devices”, Computer, vol. 41, no. 10, pp. 52-56, 2008.

4. Xiaorong Gao , Dingfeng Xu , Ming Cheng and Shangkai Gao , “A bci-based environmental controller for the motion-disabled”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, no. 2, pp. 137-140, 2003.

5. A. Ramos-Murguialday , D. Broetz , M. Rea et al “Brain-machine interface in chronic stroke rehabilitation: A controlled study”, Annals of Neurology, vol. 74, no. 1, pp. 100-108, 2013.

6. F. Pichiorri , G. Morone , M. Petti et al “Brain-computer interface boosts motor imagery practice during stroke recovery”, Annals of Neurology, vol. 77, no. 5, pp. 851-865, 2015.

7. M. A. Cervera , S. R. Soekadar , J. Ushiba et al “Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis”, Annals of Clinical and Translational Neurology, vol. 5, no. 5, pp. 651-663, 2018.

8. K. Ang , K. Chua , K. Phua et al “A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke”, Clinical EEG and Neuroscience, vol. 46, no. 4, pp. 310-320, 2014.

9. N. Bhagat , A. Venkatakrishnan , B. Abibullaev et al “Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors”, Frontiers in Neuroscience, vol. 10, pp. 122, 2016.

10. J. Webb , Z. G. Xiao , K. P. Aschenbrenner , G. Herrnstadt , and C. Menon , “Towards a portable assistive arm exoskeleton for stroke patient rehabilitation controlled through a brain computer interface”, in Biomedical Robotics and Biomechatronics (BioRob), 2012 4th IEEE RAS & EMBS International Conference, pp. 1299-1304, 2012.

11. A. L. Coffey , D. J. Leamy , and T. E. Ward , “A novel BCI-controlled pneumatic glove system for home-based neurorehabilitation”, in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, pp. 3622-3625, 2014.

12. D. Bundy , L. Souders , K. Baranyai et al “Contralesional Brain-Computer Interface Control of a Powered Exoskeleton for Motor Recovery in Chronic Stroke Survivors”, Stroke, vol. 48, no. 7, pp. 1908-1915, 2017.

13. X. Shu , S. Chen , L. Yao et al “Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients”, Frontiers in Neuroscience, vol. 12, pp. 93, 2018.

14. A. Delorme , T. Mullen , C. Kothe et al “EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing”, Computational Intelligence and Neuroscience, vol. 2011, pp. 1-12, 2011.

15. G. Schalk , D. McFarland , T. Hinterberger , N. Birbaumer and J. Wolpaw , “BCI2000: A General-Purpose Brain-Computer Interface (BCI) System”, IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 1034-1043, 2004.

16. M. H. B. Azhar , A. Casey , and M. Sakel , “A cost-effective BCI assisted technology framework for neurorehabilitation”, The Seventh International Conference on Global Health Challenges, 18th-22nd November, 2018. (In Press)

17. C. M. McCrimmon , M. Wang , L. S. Lopes et al “A small, portable, battery-powered brain-computer interface system for motor rehabilitation”, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2776-2779, 2016.

18. J. Meng , B. Edelman , J. Olsoe et al “A Study of the Effects of Electrode Number and Decoding Algorithm on Online EEG-Based BCI Behavioral Performance”, Frontiers in Neuroscience, vol. 12, pp. 227, 2018.

19. T. Mullen , C. Kothe , Y. Chi et al “Real-time neuroimaging and cognitive monitoring using wearable dry EEG”, IEEE Transactions on Biomedical Engineering, vol. 62, no. 11, pp. 2553-2567, 2015.


via eConHand: A Wearable Brain-Computer Interface System for Stroke Rehabilitation – IEEE Conference Publication

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[Abstract] Decoupling Finger Joint Motion in an Exoskeletal Hand: A Design for Robot-assisted Rehabilitation


In this study, a cable-driven exoskeleton device is developed for stroke patients to enable them to perform passive range of motion exercises and teleoperation rehabilitation of their impaired hands. Each exoskeleton finger is controlled by an actuator via two cables. The motions between the metacarpophalangeal and distal/proximal interphalangeal joints are decoupled, through which the movement pattern is analogous to that observed in the human hand. A dynamic model based on the Lagrange method is derived to estimate how cable tension varies with the angular position of the finger joints. Two discernable phases are observed, each of which reflects the motion of the metacarpophalangeal and distal/proximal interphalangeal joints. The tension profiles of exoskeleton fingers predicted by the Lagrange model are verified through a mechatronic integrated platform. The model can precisely estimate the tensions at different movement velocities, and it shows that the characteristics of two independent phases remain the same even for a variety of movement velocities. The feasibility for measuring resistance when manipulating a patient’s finger is demonstrated in human experiments. Specifically, the net force required to move a subject’s finger joints can be accounted for by the Lagrange model.


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[Abstract] Design and development of a portable exoskeleton for hand rehabilitation


Improvement in hand function to promote functional recovery is one of the major goals of stroke rehabilitation. This paper introduces a newly developed exoskeleton for hand rehabilitation with a user-centered design concept, which integrates the requirements of practical use, mechanical structure and control system. The paper also evaluated the function with two prototypes in a local hospital. Results of functional evaluation showed that significant improvements were found in ARAT (P=0.014), WMFT (P=0.020) and FMA_WH (P=0.021). Increase in the mean values of FMA_SE was observed but without significant difference (P=0.071). The improvement in ARAT score reflects the motor recovery in hand and finger functions. The increased FMA scores suggest there is motor improvement in the whole upper limb, and especially in the hand after the training. The product met patients’ requirements and has practical significance. It is portable, cost effective, easy to use and supports multiple control modes to adapt to different rehabilitation phases.


via Design and development of a portable exoskeleton for hand rehabilitation – IEEE Journals & Magazine

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[Abstract + References] A Tendon-driven Upper-limb Rehabilitation Robot – IEEE Conference Publication


Rehabilitation robots are playing an increasingly important role in daily rehabilitation of patients. In recent years, exoskeleton rehabilitation robots have become a research hotspot. However, the existing exoskeleton rehabilitation robots are mainly rigid exoskeletons. During rehabilitation training using such exoskeletons, the patient’s joint rotation center is fixed, which cannot adapt to the actual joint movements, resulting in secondary damage to the patients. Therefore, in this paper, a tendon-driven flexible upper-limb rehabilitation robot is proposed; the structure and connectors of the rehabilitation robot are designed considering the physiological structure of human upper limbs; we also built the prototype and performed experiments to validate the designed robot. The experimental results show that the proposed upper-limb rehabilitation robot can assist the human subject to conduct upper-limb rehabilitation training.

I. Introduction

Central nervous system diseases, such as stroke, spinal cord injury and traumatic brain injury, tend to cause movement disorder [1]. Clinical studies have shown that intensive rehabilitation training after cerebral injury help patients recover motoric functions because of the brain plasticity [1], [2]. Traditional movement therapy is highly dependent on physiotherapists and the efficacy is limited by professional knowledge and skill levels of physiotherapists [3]. Upper-limbs recover more slowly than lower limbs because of the complex function of neurons. Meanwhile, the rehabilitation therapies are unaffordable for most patients. Robotic rehabilitation opened another way of rehabilitation training and its efficacy has been validated in clinical trials [3], [4]. Many upper-limb robot devices have been developed for rehabilitation or assistance in various forms. One of the famous devices was MIT-MANUS developed by MIT. This kind of devices are stationary external system where the patient inserts their hand or arm and is robotically assisted or resisted in completing predetermined tasks [3], [5]. Other examples of this type of devices include Lum et al.^{\prime}s MIME [6], Kahn et al.’s ARM Guide [7] and a 2-DOF upper-limb rehabilitation robot developed by Tsinghua



1. M. Hallett, “Plasticity of the human motor cortex and recovery from stroke”, Brain Research Reviews, vol. 36, pp. 169-174, 2001.

2. J. D. Schaechter, “Motor rehabilitation and brain plasticity after hemiparetic stroke”, Progress in Neurobiology, vol. 73, pp. 61-72, 2004.

3. Q. Yang, D. Cao, J. Zhao, “Analysis on State of the Art of upper-limb Rehabilitation Robots”, Jiqiren/robot, vol. 35, pp. 630, 2013.

4. P. Maciejasz, J. Eschweiler, K. Gerlach-Hahn, A. Jansen-Troy, S. Leonhardt, “A survey on robotic devices for upper-limb rehabilitation”, Journal of Neuroengineering & Rehabilitation, vol. 11, pp. 3, 2014.

5. C. J. Nycz, M. A. Delph, G. S. Fischer, “Modeling and design of a tendon actuated flexible robotic exoskeleton for hemiparetic upper-limb rehabilitation”, International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3889-3892, 2015.

6. P. S. Lum, C. G. Burgar, P. C. Shor, “Use of the MIME robotic system to retrain multijoint reaching in post-stroke hemiparesis: why some movement patterns work better than others”, Engineering in Medicine and Biology Society 2003. Proceedings of the International Conference of the IEEE, vol. 2, pp. 1475-1478, 2003.

7. D. J. Reinkensmeyer, L. E. Kahn, M. Averbuch, A. Mckenna-Cole, B. D. Schmit, W. Z. Rymer, “Understanding and treating arm movement impairment after chronic brain injury: progress with the ARM guide”, Journal of Rehabilitation Research & Development, vol. 37, pp. 653-662.

8. Y. Zhang, Z. Wang, L. Ji, S. Bi, “The clinical application of the upper extremity compound movements rehabilitation training robot”, International Conference on Rehabilitation Robotics, pp. 91-94, 2005.

9. H. Fukushima, “Health and wellbeing in the 21st century (No. 4): Early rehabilitation and conditions for which it is appropriate [J]” in Social-human environmentology, pp. 6, 2004.

10. T. G. Sugar, J. He, E. J. Koeneman, J. B. Koeneman, R. Herman, H. Huang et al., “Design and control of RUPERT: a device for robotic upper extremity repetitive therapy”, IEEE Transactions on Neural Systems & Rehabilitation Engineering a Publication of the IEEE Engineering in Medicine & Biology Society, vol. 15, no. 3, pp. 336-46, 2007.

11. J. C Perry, J. Rosen, S. Burns, “Upper-Limb Powered Exoskeleton Design”, Mechatronics IEEE/ASME Transactions on, vol. 12, pp. 408-417, 2007.

12. A. U. Pehlivan, O. Celik, M. K. O’Malley, “Mechanical design of a distal arm exoskeleton for stroke and spinal cord injury rehabilitation”, IEEE International Conference on Rehabilitation Robotics IEEE Int Conf Rehabil Robot, pp. 5975428, 2011.

13. S Koo, T. P. Andriacchi, “The Knee Joint Center of Rotation is Predominantly on the Lateral Side during Normal Walking[J]”, Journal of Biomechanics, vol. 41, no. 6, pp. 1269, 2008.

14. Y. Mao, S. K. Agrawal, “Transition from mechanical arm to human arm with CAREX: A cable driven ARm EXoskeleton (CAREX) for neural rehabilitation”, Proc. IEEE Int. Conf. Robot. Autom., pp. 2457-2462, 2012.

15. Y. Mao, X. Jin, G. G. Dutta, J. P. Scholz, S. K. Agrawal, “Human movement training with a cable driven ARm EXoskeleton (CAREX)”, IEEE Trans. Neural Syst. Rehabil. Eng., vol. 23, no. 1, pp. 84-92, Jan. 2015.

16. DJ Reinkensmeyer, JL Emken, SC. Cramer, “Robotics motor learning and neurologic recovery”, Annual Review of Biomedical Engineering, vol. 6, no. 1, pp. 497-525, 2004.

17. QZ Yang, CF Cao, JH. Zhao, “Analysis of the status of the research of the upper-limb rehabilitative robot”, Robot, vol. 35, no. 5, pp. 630-640, 2013.

18. XZ Jiang, XH Huang, CH Xiong et al., “Position Control of a Rehabilitation Robotic Joint Based on Neuron Proportion-Integral and Feedforward Control”, Journal of Computational & Nonlinear Dynamics, vol. 7, no. 2, pp. 024502, 2012.

19. ZC Chen, Z. Huang, “Motor relearning in the application of the rehabilitation therapy for stroke”, Chinese Journal of Rehabilitation Medicine, vol. 22, no. 11, pp. 1053-1056, 2007.

20. JC Perry, J Rosen, S. Burns, “Upper-Limb Powered Exoskeleton Design[J]”, IEEE/ASME Transactions on Mechatronics, vol. 12, no. 4, pp. 408-417, 2007.

21. C LV, Research on rehabilitation robot for upper-limb hemiplegia, Shanghai China:, 2011.

22. Y K Woo, G H Cho, E Y. Yoo, Effect of PNF Applied to the Unaffected Side on Muscle Tone of Affected Side in Patients with Hemiplegia[J], vol. 9, no. 2, 2002.

23. JH Liang, JP Tong, X. Li, “Observation of the curative effect of continuous passive movement of joints in the treatment of lower limb spasticity”, Theory and practice of rehabilitation in China, vol. 14, no. 11, pp. 1067-1067, 2008.


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[ARTICLE] Randomized controlled trial of robot-assisted gait training with dorsiflexion assistance on chronic stroke patients wearing ankle-foot-orthosis – Full Text



Robot-assisted ankle-foot-orthosis (AFO) can provide immediate powered ankle assistance in post-stroke gait training. Our research team has developed a novel lightweight portable robot-assisted AFO which is capable of detecting walking intentions using sensor feedback of wearer’s gait pattern. This study aims to investigate the therapeutic effects of robot-assisted gait training with ankle dorsiflexion assistance.


This was a double-blinded randomized controlled trial. Nineteen chronic stroke patients with motor impairment at ankle participated in 20-session robot-assisted gait training for about five weeks, with 30-min over-ground walking and stair ambulation practices. Robot-assisted AFO either provided active powered ankle assistance during swing phase in Robotic Group (n = 9), or torque impedance at ankle joint as passive AFO in Sham Group (n = 10). Functional assessments were performed before and after the 20-session gait training with 3-month Follow-up. Primary outcome measure was gait independency assessed by Functional Ambulatory Category (FAC). Secondary outcome measures were clinical scores including Fugl-Meyer Assessment (FMA), Modified Ashworth Scale (MAS), Berg Balance Scale (BBS), Timed 10-Meter Walk Test (10MWT), Six-minute Walk Test (SMWT), supplemented by gait analysis. All outcome measures were performed in unassisted gait after patients had taken off the robot-assisted AFO. Repeated-measures analysis of covariance was conducted to test the group differences referenced to clinical scores before training.


After 20-session robot-assisted gait training with ankle dorsiflexion assistance, the active ankle assistance in Robotic Group induced changes in gait pattern with improved gait independency (all patients FAC ≥ 5 post-training and 3-month follow-up), motor recovery, walking speed, and greater confidence in affected side loading response (vertical ground reaction force + 1.49 N/kg, peak braking force + 0.24 N/kg) with heel strike instead of flat foot touch-down at initial contact (foot tilting + 1.91°). Sham Group reported reduction in affected leg range of motion (ankle dorsiflexion − 2.36° and knee flexion − 8.48°) during swing.


Robot-assisted gait training with ankle dorsiflexion assistance could improve gait independency and help stroke patients developing confidence in weight acceptance, but future development of robot-assisted AFO should consider more lightweight and custom-fit design.


Stroke is caused by intracranial haemorrhage or thrombosis, which cuts off arterial supply to brain tissue and usually damages the motor pathway of the central nervous system affecting one side of the body. About half of the stroke survivors cannot walk at stroke onset, but they have 60% chance to regain independent walking after rehabilitation [1]. Reduced descending neural drive to the paretic ankle joint causes muscle weakness and spasticity, often accompanied with drop foot which is characterized by the foot pointing downward and dragging on the ground during walking [23]. To maintain sufficient foot clearance in swing phase, people with dropped foot have to compensate either by hip hiking with exaggerated flexion in hip and knee joints, or circumduction gait with the body leaning on the unaffected side and the leg swinging outward through an arc away from the midline [456]. These inefficient asymmetric gait patterns hinder the walking ability and contribute to slower walking speed [78], increasing risk of falling [910], and greater energy expenditure [11]. Poor mobility results in sedentary lifestyle and limited physical exercise [12], which further deteriorates lower-limb functionality.

Foot drop can be managed using ankle-foot-orthosis (AFO), which is rigid or articulated ankle brace that controls ankle range of motion (ROM). Meta-analysis shows walking in conventional AFO has immediate or short-term beneficial effects on gait pattern and mobility of stroke patients, including an overall increase in ankle dorsiflexion throughout gait cycle, improvements in Functional Ambulatory Category (FAC), walking speed, and stairs-climbing speed [131415]. Recent development in robot-assisted AFO demonstrates power assistance at ankle joint can facilitate walking of patients presenting with foot drop, by actively assisting ankle dorsiflexion for foot clearance in swing phase and minimizing occurrence of foot slap at initial contact [161718]. Previous studies only evaluated the immediate effects of stroke patients walking in passive AFO [1415] or robot-assisted AFO [1920], but they were not sure whether any assistive effects could be carried over to unassisted gait after the patients had taken off the devices, i.e. the therapeutic effects.

Neuroscience studies suggest the brain is capable of altering its functions and structures for adapting to internal and external environment; an ability known as neuroplasticity [22122]. Researches show intensive repetitive skill training can enhance neuroplasticity and promote motor relearning of stroke patients [2324], which is achievable utilizing robot-assistance in clinical setting. The Anklebot that was developed in MIT can provide power assistance to stroke patients performing repetitive voluntary ankle sagittal movements in seated position, and a single-arm pilot study reports stroke patients (n = 8) had improved volitional ankle control and spatial-temporal gait parameters after 6-week 18-session training using the Anklebot [25]; 30-min seated skill training at ankle joint can induce plastic changes in cortical excitability in area controlling dorsiflexor [26]. Thus robot-assisted AFO with dorsiflexion assistance can potentially stimulate motor recovery of stroke patients with foot drop problem. Neuroscience studies further show the functional outcome of neuroplasticity is task-specific and dependent on the training nature [2212227]. It implies that in order to improve independent walking ability, stroke patients are expected to practise real over-ground walking instead of seated training. Incorporation of stair ambulation into gait training could facilitate generalization towards activity of daily-living, which requires stroke patients to perform skilled ankle dorsiflexion and plantarflexion when they are negotiating steps. Another characteristics of neuroplasticity is the importance of salient experiences for motor relearning from error correction [22122]. During gait training, powered ankle assistance from a robot-assisted AFO could serve as a source of salient proprioceptive feedback synchronized to gait pattern [28]. The robot can strengthen the experience-driven neuroplasticity by producing this proprioceptive feedback at each successfully triggered ankle power assistance [28]. In summary, researches on experience-driven neuroplasticity suggest stroke patients presenting with foot drop problem can potentially restore some level of independent walking ability through robot-assisted gait training with ankle dorsiflexion assistance on over-ground walking and stair ambulation.

To our knowledge, up to now no randomized controlled trial (RCT) has been carried out to validate the rehabilitation approach of robot-assisted AFO [2930]. The current study aims to evaluate whether gait training with robot-assisted AFO with dorsiflexion assistance can bring greater improvement in independent walking ability than training with passive AFO. In each session, stroke patients were trained in 20-min over-ground walking and 10-min stair ambulation. Assessments on the participating stroke patients focused on functional changes in unassisted gait after they had discontinued to wear the devices, i.e. the therapeutic effects. A meta-analysis study recommends FAC to be the primary outcome measure for clinical trials involving electromechanical gait training [30]. FAC is a reliable measurement of independent walking ability on level ground walking and stair ambulation, which is a good prediction of independent community walking post-stroke [31]. The demonstration of safety and effectiveness of the robot-assisted gait training can have positive impact on post-stroke rehabilitation and can potentially establish a new treatment method for stroke patients presenting with foot drop.[…]


Continue —>  Randomized controlled trial of robot-assisted gait training with dorsiflexion assistance on chronic stroke patients wearing ankle-foot-orthosis | Journal of NeuroEngineering and Rehabilitation | Full Text

Figure 1

Fig. 1a Robot-assisted AFO, and b Stroke patients walking on stairs wearing the robot-assisted AFO

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[Abstract] Development of a Minimal-Intervention-Based Admittance Control Strategy for Upper Extremity Rehabilitation Exoskeleton


The applications of robotics to the rehabilitation training of neuromuscular impairments have received increasing attention due to their promising prospects. The effectiveness of robot-assisted training directly depends on the control strategy applied in the therapy program. This paper presents an upper extremity exoskeleton for the functional recovery training of disabled patients. A minimal-intervention-based admittance control strategy is developed to induce the active participation of patients and maximize the use of recovered motor functions during training. The proposed control strategy can transit among three control modes, including human-conduct mode, robot-assist mode, and motion-restricted mode, based on the real-time position tracking errors of the end-effector. The human-robot interaction in different working areas can be modulated according to the motion intention of patient. Graphical guidance developed in Unity-3-D environment is introduced to provide visual training instructions. Furthermore, to improve training performance, the controller parameters should be adjusted in accordance with the hemiplegia degree of patients. For the patients with severe paralysis, robotic assistance should be increased to guarantee the accomplishment of training. For the patients recovering parts of motor functions, robotic assistance should be reduced to enhance the training intensity of effected limb and improve therapeutic effectiveness. The feasibility and effectiveness of the proposed control scheme are validated via training experiments with two healthy subjects and six stroke patients with different degrees of hemiplegia.

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[Abstract] The eWrist — A wearable wrist exoskeleton with sEMG-based force control for stroke rehabilitation.


Chronic wrist impairment is frequent following stroke and negatively impacts everyday life. Rehabilitation of the dysfunctional limb is possible but requires extensive training and motivation. Wearable training devices might offer new opportunities for rehabilitation. However, few devices are available to train wrist extension even though this movement is highly relevant for many upper limb activities of daily living. As a proof of concept, we developed the eWrist, a wearable one degree-of-freedom powered exoskeleton which supports wrist extension training. Conceptually one might think of an electric bike which provides mechanical support only when the rider moves the pedals, i.e. it enhances motor activity but does not replace it. Stroke patients may not have the ability to produce overt movements, but they might still be able to produce weak muscle activation that can be measured via surface electromyography (sEMG). By combining force and sEMG-based control in an assist-as-needed support strategy, we aim at providing a training device which enhances activity of the wrist extensor muscles in the context of daily life activities, thereby, driving cortical reorganization and recovery. Preliminary results show that the integration of sEMG signals in the control strategy allow for adjustable assistance with respect to a proxy measurement of corticomotor drive.

Source: The eWrist — A wearable wrist exoskeleton with sEMG-based force control for stroke rehabilitation – IEEE Xplore Document

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[Abstract] Use of Lower-Limb Robotics to Enhance Practice and Participation in Individuals With Neurological Conditions

Purpose: To review lower-limb technology currently available for people with neurological disorders, such as spinal cord injury, stroke, or other conditions. We focus on 3 emerging technologies: treadmill-based training devices, exoskeletons, and other wearable robots.

Summary of Key Points: Efficacy for these devices remains unclear, although preliminary data indicate that specific patient populations may benefit from robotic training used with more traditional physical therapy. Potential benefits include improved lower-limb function and a more typical gait trajectory.

Statement of Conclusions: Use of these devices is limited by insufficient data, cost, and in some cases size of the machine. However, robotic technology is likely to become more prevalent as these machines are enhanced and able to produce targeted physical rehabilitation.

Recommendations for Clinical Practice: Therapists should be aware of these technologies as they continue to advance but understand the limitations and challenges posed with therapeutic/mobility robots.

Source: Use of Lower-Limb Robotics to Enhance Practice and Participa… : Pediatric Physical Therapy

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[Abstract] Preliminary study on the design and control of a pneumatically-actuated hand rehabilitation device


In recent years, the robotic devices have been used in hand rehabilitation training practice. The majority of existing robotic devices for rehabilitation belong to the rigid exoskeleton. However, rigid exoskeletons may have some limitations such as heavy weight, un-safety and inconvenience. This paper presents a device designed to help post-stroke patients to stretch their spastic hands. This hand rehabilitation device actuator is fabricated by soft material, powered with fluid pressure, and embedded in one glove surface. The distinguished features of this device are: safety, low cost, light weight, convenience and pneumatic actuation. In clinical practice, rehabilitation therapists should help the post-stroke patients to stretch fingers to a desired joint position. Therefore, the control objective of the proposed hand rehabilitation device is to drive the patient’s finger bending angle to a predesigned position. To this end, curvature sensors embedded in the glove are used to measure the finger’s bending angle. A commercial data glove is used to collect the actual finger’s bending angle for calibrating the curvature sensors based on a three-layer back-propagation (BP) neural network. Then the error between the designed joint position and the actual joint position can be calculated. An error proportional control strategy is adopted for the positioning control objective (the controller’s input is the pump speed). Finally, experiments are conducted to validate the effectiveness of control method and the capacity of the proposed hand rehabilitation device.

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Source: Preliminary study on the design and control of a pneumatically-actuated hand rehabilitation device – IEEE Xplore Document

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[Abstract] Robotic Devices to Enhance Human Movement Performance.


Robotic exoskeletons and bionic prostheses have moved from science fiction to science reality in the last decade. These robotic devices for assisting human movement are now technically feasible given recent advancements in robotic actuators, sensors, and computer processors. However, despite the ability to build robotic hardware that is wearable by humans, we still do not have optimal controllers to allow humans to move with coordination and grace in synergy with the robotic devices. We consider the history of robotic exoskeletons and bionic limb prostheses to provide a better assessment of the roadblocks that have been overcome and to gauge the roadblocks that still remain. There is a strong need for kinesiologists to work with engineers to better assess the performance of robotic movement assistance devices. In addition, the identification of new performance metrics that can objectively assess multiple dimensions of human performance with robotic exoskeletons and bionic prostheses would aid in moving the field forward. We discuss potential control approaches for these robotic devices, with a preference for incorporating feedforward neural signals from human users to provide a wider repertoire of discrete and adaptive rhythmic movements.

Source: Robotic Devices to Enhance Human Movement Performance: Kinesiology Review: Vol 6, No 1

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