Posts Tagged UE

[Abstract] Improving Healthcare Access: A Preliminary Design of a Low-Cost Arm Rehabilitation Device

Abstract

A low-cost continuous passive motion (CPM) machine, the Gannon Exoskeleton for Arm Rehabilitation (GEAR), was designed. The focus of the machine is on the rehabilitation of primary functional movements of the arm. The device developed integrates two mechanisms consisting of a four-bar linkage and a sliding rod prismatic joint mechanism that can be mounted to a normal chair. When seated, the patient is connected to the device via a padded cuff strapped on the elbow. A set of springs have been used to maintain the system stability and help the lifting of the arm. A preliminary analysis via analytical methods is used to determine the initial value of the springs to be used in the mechanism given the desired gravity compensatory force. Subsequently, a multibody simulation was performed with the software simwise 4D by Design Simulation Technologies (DST). The simulation was used to optimize the stiffness of the springs in the mechanism to provide assistance to raising of the patient’s arm. Furthermore, the software can provide a finite element analysis of the stress induced by the springs on the mechanism and the external load of the arm. Finally, a physical prototype of the mechanism was fabricated using polyvinyl chloride (PVC) pipes and commercial metal springs, and the reaching space was measured using motion capture. We believed that the GEAR has the potential to provide effective passive movement to individuals with no access to postoperative or poststroke rehabilitation therapy.

 

via Improving Healthcare Access: A Preliminary Design of a Low-Cost Arm Rehabilitation Device | Journal of Medical Devices | ASME Digital Collection

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[Abstract] Cable driven exoskeleton for upper-limb rehabilitation: A design review

Abstract

One of the primary reasons for long-term disabilities in the world is strokes. The causes of these cerebrovascular diseases are various, i.e., high blood pressure, heart disease, etc. For those who survive strokes, this affectation causes lose in mobility of extremities, requiring the intervention of long session with a therapeutic professional to recover the movement of the impair limb. Hence, the investment to threat this condition is usually high. Those devices permit the user a mean to conduct the therapies without the constant supervision of a professional. Furthermore, exoskeletons are capable of maintaining a detailed recording of the forces and movements developed for the patients throughout the session. However, the construction of an exoskeleton is not cheap principally for the actuation systems, especially if the exoskeleton requires the actuator to be placed at the joints of the user; thus, the actuator at a joint would have to withstand the load of the actuator of the following joint and so on.

Researchers have addressed this drawback by applying cable transmission systems that allow the exoskeleton to place their actuator at a base, reducing the weight of their design and decreasing their cost. Thus, this paper reviews the principal models of cable-driven exoskeleton for stroke rehabilitation focusing on the upper-limb. The analysis departs from the study of the anatomy of the arm in all its extension, including the shoulder, elbow, wrist, fingers, and the thumb. Besides, it also includes the mechanical consideration the researchers have to take in mind to design a proper exoskeleton. Then, the article presents a compendium of the different transmission systems found in the literature, addressing their advantages, disadvantages and their requirements for the design. Lastly, the paper reviews the cable-driven exoskeleton for stroke rehabilitation of the upper limb. Again, for this analysis, it is included the design consideration of each prototype focusing on their advantages in terms of anatomical mechanics.

Source: https://doi.org/10.1016/j.robot.2020.103445

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[ARTICLE] Efficacy of Virtual Reality Combined With Real Instrument Training for Patients With Stroke: A Randomized Controlled Trial – Full Text

Article Outline

  1. Methods
    1. Patients
    2. Instrumentation
    3. Intervention
    4. Outcome measurements
    5. Statistical analysis
  2. Results
    1. Treatment effects
  3. Discussion
    1. Study limitations
  4. Conclusions
  5. Suppliers
  6. References

Abstract

Objective

To investigate the efficacy of real instrument training in virtual reality (VR) environment for improving upper-extremity and cognitive function after stroke.

Design

Single-blind, randomized trial.

Setting

Medical center.

Participants

Enrolled subjects (N=31) were first-episode stroke, assessed for a period of 6 months after stroke onset; age between 20 and 85 years; patients with unilateral paralysis and a Fugl-Meyer assessment upper-extremity scale score >18.

Interventions

Both groups were trained 30 minutes per day, 3 days a week, for 6 weeks, with the experimental group performing the VR combined real instrument training and the control group performing conventional occupational therapy.

Main Outcome Measures

Manual Muscle Test, modified Ashworth scale, Fugl-Meyer upper motor scale, hand grip, Box and Block, 9-Hole Peg Test (9-HPT), Korean Mini-Mental State Examination, and Korean-Montreal Cognitive Assessment.

Results

The experimental group showed greater therapeutic effects in a time-dependent manner than the control group, especially on the motor power of wrist extension, spasticity of elbow flexion and wrist extension, and Box and Block Tests. Patients in the experimental group, but not the control group, also showed significant improvements on the lateral, palmar, and tip pinch power, Box and Block, and 9-HPTs from before to immediately after training. Significantly greater improvements in the tip pinch power immediately after training and spasticity of elbow flexion 4 weeks after training completion were noted in the experimental group.

Conclusions

VR combined real instrument training was effective at promoting recovery of patients’ upper-extremity and cognitive function, and thus may be an innovative translational neurorehabilitation strategy after stroke.

Stroke is currently the leading cause of disability and death worldwide, and stroke survivors often experience chronic functional impairment and cognition deficits, which are associated with a reduced quality of life including difficulties in social and personal relationships.1, 2 It is well known that patients with stroke have a limited use of their upper extremities owing to motor dysfunction, and such patients experience sensory-motor deficits that affect their ability to perform daily activities. Stroke increases the risk of dementia 4 to 12 times,3 and up to 69% of subjects have a poststroke cognitive impairment.4 Consequently, the aims of the current rehabilitation strategies for these patients are to improve functional ability and cognitive impairments through optimal and comprehensive rehabilitation processes.

Previous studies have reported that a considerable amount of practice using real instruments is required to stimulate functional improvement and neuroplastic changes.5, 6 Conventional occupational therapies promote the recovery of upper-extremity dysfunction by utilizing task-oriented repetition training with real instruments.7, 8 Conventional therapy using real instruments is essential for poststroke rehabilitation, but environmental, individual, and financial limitations are associated with it.9, 10

Over the past 2 decades, the advancement of computer technology has resulted in the development of interventions that involve virtual reality (VR) devices, which are defined as computer hardware and software systems that generate simulations of imagined environments via visual, auditory, and tactile feedback.11 VR environments may be perceptual, such as creating situations with multiple sensory feedback regarding the patients’ kinematic movements, which are passive or active assisted in a virtual environment, and providing high-intensity repetitive multisensory interaction and goal-oriented tasks.12 Repetition and intensity are key factors for promoting neural plasticity in patients with brain damage.13 Additionally, studies have reported that VR training promotes motor recovery and cognition by inducing experience-dependent neural plasticity through repetitive tasks of varying time, high intensity, and complexity levels.14 Various studies have revealed that adaptive neuroplasticity, defined as the reorganization of movement representation in the motor cortex, premotor cortex, supplementary motor area, and somatosensory cortex due to synaptic efficacy and remodeling of the dendritic spines, can be induced by conducting repetitive goal-oriented tasks in VR-based interventions after stroke.15, 16, 17

Recently, various reports have highlighted the potential utility of VR-based rehabilitation strategies for improving upper-limb motor weakness,18, 19 cognitive dysfunction, and balance in patients poststroke.20, 21, 22 Furthermore, research has shown that compared to conventional therapy, VR training can improve the quality of neurologic rehabilitation and enhance productivity.23 Even more, it has more beneficial effects in poststroke rehabilitation, such as an increased motivation and engagement,24 cost, and usability.25, 26, 27 In addition, VR training is able to facilitate an increased therapy time without necessarily having to rely on a therapist.28 For these reasons, the number of complex and realistic VR-based interventions is increasing in neurorehabilitation programs in order to enhance the variability and adaptability of the intervention, as well as patients’ motivation, after stroke. However, comparing the effects of VR training with conventional therapy is still unclear. According to previous mentions, the combination of VR and real instruments is expected to have a synergy effect rather than a conventional occupational therapy in patients with stroke, and we investigated to see the clinical effect by using actual devices combined with a VR system to perform numerous tasks related to real daily activities.

In the present study, we developed a novel rehabilitation training that combined the benefits of real instrument training and VR-based intervention. The aim of this study was to investigate whether the VR combined with real instrument training would be an efficient translational intervention for improving the functional abilities of the upper-extremity and cognitive function in patients with stroke.

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[Abstract] An Upper Limb Rehabilitation Training and Evaluation System for Stroke Patients

ABSTRACT

This system combines information technology and rehabilitation medicine. It adopts Motor Imagery (MI) intervention and mental rotation training mode in order to change the traditional inefficient mode of clinical stroke rehabilitation. We developed multi-functional side recognition rehabilitation and evaluation peripheral to evaluate the rehabilitation effect of stroke patients accurately and quantitatively. The healing effect, which reveals the degree of recovery to the patients, will no longer depend on the personal experience of the rehabilitation therapist. Based on the psychological hint and a client designed with Unity 3D, it makes the treatment less boring to stimulate the patients’ initiative during the training. This system confirms that the MI Intervention can to a certain degree improve function of limb motor and sensory feedback by analyzing 38 volunteer patients’ data in Huashan Hospital and Shanghai Jing’an District Central Hospital. Precise and quantitative evaluation results are given for the further treatment.

via An Upper Limb Rehabilitation Training and Evaluation System for Stroke Patients | ZHAO | DEStech Transactions on Computer Science and Engineering

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[Abstract + References] A Mechatronic Mirror-Image Motion Device for Symmetric Upper-Limb Rehabilitation

Abstract

This paper presents an upper-limb rehabilitation device that provides symmetric bilateral movements with motion measurements using inertial sensors. Mirror therapy is one of widely used methods for rehabilitation of impaired side movements because voluntary movement of the unimpaired side facilitates reorganizational changes in the motor cortex. The developed upper-limb exoskeleton was equipped with two brushless DC motors that helped generate three axes of upper-limb movements corresponding to other arm movements that were measured using inertial sensors. In this study, inertial sensors were used to estimate the joint angles for three target upper-limb movements: elbow flexion and extension (flex/ext), wrist flex/ext, and forearm pronation and supination (pro/sup). Elbow flex/ext was performed by the actuator that was directly attached to the elbow joint. The actuation of the forearm pro/sup and wrist flex/ext shared one motor using a developed cable-driven mechanism, and two types of motion were selectively performed. We assessed the feasibility of the proposed mirror-image device with the accuracy and precision of the motion estimation and the actuation of joint movements. An individual could perform most upper-limb movements for activities of daily living using the proposed device.

References

1.
Moseley, L. G., Gallace, A., & Spence, C. (2008). Is mirror therapy all it is cracked up to be? Current evidence and future directions. Pain,138(1), 7–10.Google Scholar
2.
Hamzei, F., Läppchen, C. H., et al. (2012). Functional plasticity induced by mirror training: The mirror as the element connecting both hands to one hemisphere. Neurorehabilitation and neural repair,26(5), 484–496.CrossRefGoogle Scholar
3.
Michielsen, M. E., et al. (2011). Motor recovery and cortical reorganization after mirror therapy in chronic stroke patients: A phase II randomized controlled trial. Neurorehabilitation and neural repair,25(3), 223–233.CrossRefGoogle Scholar
4.
Kim, W., Beom, J., et al. (2018). Reliability and validity of attitude and heading reference system motion estimation in a novel mirror therapy system. Journal of Medical and Biological Engineering,38(3), 370–377.CrossRefGoogle Scholar
5.
Nam, H. S., Koh, S., et al. (2017). Recovery of proprioception in the upper extremity by robotic mirror therapy: A clinical pilot study for proof of concept. Journal of Korean Medical Science,32(10), 1568–1575.CrossRefGoogle Scholar
6.
Samuelkamaleshkumar, S., Reethajanetsureka, S., et al. (2014). Mirror therapy enhances motor performance in the paretic upper limb after stroke: A pilot randomized controlled trial. Archives of Physical Medicine and Rehabilitation,95(11), 2000–2005.CrossRefGoogle Scholar
7.
Yue, G., & Cole, K. J. (1992). Strength increases from the motor program: Comparison of training with maximal voluntary and imagined muscle contractions. Journal of Neurophysiology,67(5), 1114–1123.CrossRefGoogle Scholar
8.
Babaiasl, M., Mahdioun, S. H., et al. (2016). A review of technological and clinical aspects of robot-aided rehabilitation of upper-extremity after stroke. Disability and Rehabilitation: Assistive Technology,11(4), 263–280.Google Scholar
9.
Moon, S. B., et al. (2017). Gait analysis of hemiplegic patients in ambulatory rehabilitation training using a wearable lower-limb robot: A pilot study. International Journal of Precision Engineering and Manufacturing,18(12), 1773–1781.CrossRefGoogle Scholar
10.
Dobkin, B. H. (2004). Strategies for stroke rehabilitation. The Lancet Neurology,3(9), 528–536.CrossRefGoogle Scholar
11.
Gillen, G. (2015). Stroke rehabilitation: A function-based approach. Amsterdam: Elsevier.Google Scholar
12.
Lessard, S., Pansodtee, P., et al. (2018). A soft exosuit for flexible upper-extremity rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering,26(8), 1604–1617.CrossRefGoogle Scholar
13.
Colombo, R., & Sanguineti, V. (2018). Assistive controllers and modalities for robot-aided neurorehabilitation. In Rehabilitation robotics (pp. 63–74). Academic Press.Google Scholar
14.
Ercolini, G., Trigili, E., et al. (2019). A novel generation of ergonomic upper-limb wearable robots: Design challenges and solutions. Robotica,37(12), 2056–2072.CrossRefGoogle Scholar
15.
Heo, P., Gu, G., et al. (2012). Current hand exoskeleton technologies for rehabilitation and assistive engineering. Int. J. Precis. Eng. Manuf.,13(5), 807–824.CrossRefGoogle Scholar
16.
Muellbacher, W., Ziemann, U., et al. (2001). Role of the human motor cortex in rapid motor learning. Experimental Brain Research,136(4), 431–438.CrossRefGoogle Scholar
17.
Perry, J. C., Rosen, J., & Burns, S. (2007). Upper-limb powered exoskeleton design. IEEE/ASME Transactions on Mechatronics,12(4), 408–417.CrossRefGoogle Scholar
18.
Hu, X., Yao, C., & Soh, G. S. (2015). Performance evaluation of lower limb ambulatory measurement using reduced Inertial Measurement Units and 3R gait model. In IEEE international conference on rehabilitation robotics (ICORR) (549–554).Google Scholar
19.
Liao, W. W., et al. (2012). Effects of robot-assisted upper limb rehabilitation on daily function and real-world arm activity in patients with chronic stroke: A randomized controlled trial. Clinical Rehabilitation,26(2), 111–120.MathSciNetCrossRefGoogle Scholar
20.
Park, K., Lee, D. J., et al. (2012). Development of mirror image motion system with sEMG for shoulder rehabilitation of post-stroke hemiplegic patients. International Journal of Precision Engineering and Manufacturing,13(8), 1473–1479.CrossRefGoogle Scholar
21.
Lum, P. S., Burgar, C. G., et al. (2006). MIME robotic device for upper-limb neurorehabilitation in subacute stroke subjects: A follow-up study. Journal of Rehabilitation Research and Development,43(5), 631.CrossRefGoogle Scholar
22.
French, J. A., Rose, C. G., & O’malley, M. K. (2014). System characterization of MAHI Exo-II: a robotic exoskeleton for upper extremity rehabilitation. In Proceedings of the ASME dynamic systems and control conference. NIH Public Access.Google Scholar
23.
KATS:The Report of the anthropometry survey, Korea, KATS Report, 2010.Google Scholar
24.
Perreault, S., & Gosselin, C. M. (2008). Cable-driven parallel mechanisms: Application to a locomotion interface. Journal of Mechanical Design,130(10), 102301.CrossRefGoogle Scholar
25.
Abdolshah, S., & Rosati, G. (2017). Improving performance of cable robots by adaptively changing minimum tension in cables. International Journal of Precision Engineering and Manufacturing,18(5), 673–680.CrossRefGoogle Scholar
26.
Cho, G. R., Kim, S. T., & Kim, J. (2018). Backlash compensation for accurate control of biopsy needle manipulators having long cable transmission. International Journal of Precision Engineering and Manufacturing,19(5), 675–684.CrossRefGoogle Scholar
27.
Lee, C., & Park, S. (2018). Estimation of unmeasured golf swing of arm based on the swing dynamics. Int. J. Precis. Eng. Manuf.,19(5), 745–751.CrossRefGoogle Scholar
28.
Lundin, T. M., Grabiner, M. D., & Jahnigen, D. W. (1995). On the assumption of bilateral lower extremity joint moment symmetry during the sit-to-stand task. Journal of Biomechanics,28(1), 109–112.CrossRefGoogle Scholar
29.
El-Gohary, M., & McNames, J. (2012). Shoulder and elbow joint angle tracking with inertial sensors. IEEE Transactions on Biomedical Engineering,59(9), 2635–2641.CrossRefGoogle Scholar
30.
Cutti, A. G., Paolini, G., et al. (2005). Soft tissue artefact assessment in humeral axial rotation. Gait & Posture,21(3), 341–349.CrossRefGoogle Scholar

 

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[Abstract] Comparison of Task Oriented Approach and Mirror Therapy for Poststroke Hand Function Rehabilitation

Abstract

Objective: The purpose of this study was to compare the effectiveness of task-oriented therapy and mirror therapy on improving hand function in post-stroke patients.
Methods: Total subjects 30 were randomly divided into two groups: the task-oriented group (15 patients) and the mirror therapy group (15 patients). The task-oriented group underwent task-oriented training for 45 mins a day for 5 days a week for 4 weeks. The mirror therapy group underwent a mirror therapy program under the same schedule as
task-oriented therapy. The manual dexterity and motor functioning of the hand were evaluated before the intervention and 4 weeks after the intervention by using FMA (Fugl-Meyer assessment) and BBT (Box & Block test).
Results: Hand function of all patients increased significantly after the 4-week intervention program on the evaluation of motor function and manual dexterity by FMA and BBT in both the groups of Task-Oriented approach and Mirror therapy, but Group A Task-oriented approach improved more significantly when compared to Group B Mirror therapy.
Conclusion: The treatment effect was more in patients who received a Task-Oriented approach compared to Mirror therapy. These findings suggest that the Task-Oriented approach was more effective in post stoke hand function rehabilitation.

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[Abstract] Improving Healthcare Access: a Preliminary Design of a Low Cost Arm Rehabilitation Device

Abstract

A low cost continuous passive motion (CPM) machine, the Gannon Exoskeleton for Arm Rehabilitation (GEAR), was designed. The focus of the machine is on the rehabilitation of primary functional movements of the arm. The device developed integrates two mechanisms consisting of a four-bar linkage and a sliding rod prismatic joint mechanism that can be mounted to a normal chair. When seated, the patient is connected to the device via a padded cuff strapped on the elbow. A set of springs have been used to maintain the system stability and help the lifting of the arm. A preliminary analysis via analytical methods is used to determine the initial value of the springs to be used in the mechanism given the desired gravity compensatory force. Subsequently, a multi-body simulation was performed with the software SimWise 4D by Design Simulation Technologies (DST). The simulation was used to optimize the stiffness of the springs in the mechanism to provide assistance to raising of the patient’s arm. Furthermore, the software can provide a finite element analysis of the stress induced by the springs on the mechanism and the external load of the arm. Finally, a physical prototype of the mechanism was fabricated using PVC pipes and commercial metal springs, and the reaching space was measured using motion capture. We believed that the GEAR has the potential to provide effective passive movement to individuals with no access to post-operative or post-stroke rehabilitation therapy.

via Improving Healthcare Access: a Preliminary Design of a Low Cost Arm Rehabilitation Device | Journal of Medical Devices | ASME Digital Collection

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[Abstract + References] A Novel Exoskeleton with Fractional Sliding Mode Control for Upper Limb Rehabilitation

Summary

The robotic intervention has great potential in the rehabilitation of post-stroke patients to regain their lost mobility. In this paper, firstly, we present a design of a novel, 7 degree-of-freedom (DOF) upper limb robotic exoskeleton (u-Rob) that features shoulder scapulohumeral rhythm with a wide range of motions (ROM) compared to other existing exoskeletons. An ergonomic shoulder mechanism with two passive DOF was included in the proposed exoskeleton to provide scapulohumeral motion with corresponding full ROM. Also, the joints of u-Rob have more range of motions compared to its existing counterparts. Secondly, we propose a fractional sliding mode control (FSMC) to control u-Rob. Applying the Lyapunov theory to the proposed control algorithm, we showed the stability of it. To control u-Rob, FSMC has shown effectiveness to handle unmodeled dynamics (e.g. friction, disturbance, etc.) in terms of better tracking and chatter compared to traditional SMC.

References

1.Stroke Statistics In, (The Internet Stroke Centre 2019).Google Scholar
2.BenjaminE. J.BlahaM. J.ChiuveS. E.CushmanM.DasS. R.DeoR.de FerrantiS. D.FloydJ.FornageM.GillespieC.IsasiC. R.JimenezM. C.JordanL. C.JuddS. E.LacklandD.LichtmanJ. H.LisabethL.LiuS.LongeneckerC. T.MackeyR. H.MatsushitaK.MozaffarianD.MussolinoM. E.NasirK.NeumarR. W.PalaniappanL.PandeyD. K.ThiagarajanR. R.ReevesM. J.RitcheyM.RodriguezC. J.RothG. A.RosamondW. D.SassonC.TowfighiA.TsaoC. W.TurnerM. B.ViraniS. S.VoeksJ. H.WilleyJ. Z.WilkinsJ. T.WuJ. H.AlgerH. M.WongS. S.P. Muntner and On behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee, “Heart disease and stroke statistics-2017 update: A report from the American Heart Association,” Circulation 135(10), e146e603 (2017).CrossRef | Google Scholar
3.Rehabilitation Therapy after a Stroke In, (National Stroke Association, 2019).Google Scholar
4.PoliP.MoroneG.RosatiG. and MasieroS., “Robotic technologies and rehabilitation: New tools for stroke patients’ therapy,” BioMed Res. Int. 20138 (2013).Google Scholar
5.BaiS.ChristensenS. and IslamM. R. U., “An Upper-body Exoskeleton with a Novel Shoulder Mechanism for Assistive Applications,” 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM) (2017) pp. 10411046.Google Scholar
6.BrahmiB.SaadM.LunaC. O.ArchambaultP. S. and RahmanM. H., “Passive and active rehabilitation control of human upper-limb exoskeleton robot with dynamic uncertainties,” Robotica 36(11), 17571779 (2018).CrossRef | Google Scholar
7.CarignanC.TangJ.RoderickS. and NaylorM., “A Configuration-Space Approach to Controlling a Rehabilitation Arm Exoskeleton,” 2007 IEEE 10th International Conference on Rehabilitation Robotics (2007) pp. 179187.Google Scholar
8.ChristensenS. and BaiS.A Novel Shoulder Mechanism with a Double Parallelogram Linkage for Upper-Body Exoskeletons (Springer International PublishingCham2017) pp. 5156.Google Scholar
9.CuiX.ChenW.JinX. and AgrawalS. K., “Design of a 7-DOF Cable-Driven Arm Exoskeleton (CAREX-7) and a controller for dexterous motion training or assistance,” IEEE/ASME Trans. Mechatron. 22(1), 161172 (2017).CrossRef | Google Scholar
10.KiguchiK.EsakiR.TsurutaT.WatanabeK. and FukudaT., “An exoskeleton system for elbow joint motion rehabilitation,” Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003) (2003) vol. 1222, pp. 12281233.Google Scholar
11.KiguchiK. and HayashiY., “An EMG-based control for an upper-limb power-assist exoskeleton robot,” IEE Trans. Syst. Man Cybernetics, Part B (Cybernetics) 42(4), 10641071 (2012).CrossRef | Google Scholar | PubMed
12.KiguchiK.RahmanM. H.SasakiM. and TeramotoK., “Development of a 3DOF mobile exoskeleton robot for human upper-limb motion assist,” Robot. Auton. Syst. 56(8), 678691 (2008).CrossRef | Google Scholar
13.KimB. and DeshpandeA. D., “Controls for the Shoulder Mechanism of an Upper-body Exoskeleton for Promoting Scapulohumeral Rhythm,” 2015 IEEE International Conference on Rehabilitation Robotics (ICORR) (2015) pp. 538542.Google Scholar
14.KimB. and DeshpandeA. D., “An upper-body rehabilitation exoskeleton Harmony with an anatomical shoulder mechanism: Design, modeling, control, and performance evaluation,” Int. J. Rob. Res. 36(4), 414435 (2017).CrossRef | Google Scholar
15.LiuL.ShiY.-Y. and XieL., “A novel multi-dof exoskeleton robot for upper limb rehabilitation,” J. Mech. Med. Biol. 16(08), 1640023 (2016).CrossRef | Google Scholar
16.MahdavianM.ToudeshkiA. G. and Yousefi-KomaA., “Design and Fabrication of a 3DoF Upper Limb Exoskeleton,” 2015 3rd RSI International Conference on Robotics and Mechatronics (ICROM) (2015) pp. 342346.Google Scholar
17.MiheljM.NefT. and RienerR., “ARMin II – 7 DoF Rehabilitation Robot: Mechanics and Kinematics,” Proceedings 2007 IEEE International Conference on Robotics and Automation (2007) pp. 41204125.Google Scholar
18.NefT.GuidaliM.Klamroth-MarganskaV. and RienerR., “ARMin – Exoskeleton Robot for Stroke Rehabilitation,” In: World Congress on Medical Physics and Biomedical Engineering (DösselO. and SchlegelW. C., eds.) September 7–12, 2009, Munich, Germany (Springer Berlin HeidelbergBerlin, Heidelberg, 2009) pp. 127130.Google Scholar
19.NefT.GuidaliM. and RienerR., “ARMin III – Arm therapy exoskeleton with an ergonomic shoulder actuation,” Appl. Bionics Biomech. 6(2), (2009) pp. 127142.CrossRef | Google Scholar
20.NefT.MiheljM.KieferG.PerndlC.MullerR. and RienerR., “ARMin – Exoskeleton for Arm Therapy in Stroke Patients,” 2007 IEEE 10th International Conference on Rehabilitation Robotics (2007) pp. 6874.Google Scholar
21.NefT.MiheljM. and RienerR., “ARMin: A robot for patient-cooperative arm therapy,” Med. Biol. Eng. Comput. 45(9), 887900 (2007).CrossRef | Google Scholar | PubMed
22.NefT. and RienerR., “Shoulder Actuation Mechanisms for Arm Rehabilitation Exoskeletons,” 2008 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (2008) pp. 862868.Google Scholar
23.OttenA.VoortC.StienenA.AartsR.van AsseldonkE. and van der KooijH., “LIMPACT: A hydraulically powered self-aligning upper limb exoskeleton,” IEEE/ASME Trans. Mechatron. 20(5), 22852298 (2015).CrossRef | Google Scholar
24.PanD.GaoF.MiaoY. and CaoR., “Co-simulation research of a novel exoskeleton-human robot system on humanoid gaits with fuzzy-PID/PID algorithms,” Adv. Eng. Software 793646 (2015).CrossRef | Google Scholar
25.PerryJ. C.RosenJ. and BurnsS., “Upper-limb powered exoskeleton design,” IEEE/ASME Trans. Mechatron. 12(4), 408417 (2007).CrossRef | Google Scholar
26.Piña-MartnezE.RobertsR.Rodriguez-LealE.Flores-ArredondoJ. H. and SotoR., “A Novel Exoskeleton for Continuous Monitoring of the Upper-Limb During Gross Motor Rehabilitation,” In: Converging Clinical and Engineering Research on Neurorehabilitation II: Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR 2016), October 18–21, 2016, Segovia, Spain (IbáñezJ.González-VargasJ.AzornJ. M.AkayM. and PonsJ. L., eds.) (Springer International PublishingCham2017) pp. 11991203.CrossRef | Google Scholar
27.RahmanM. H.RahmanM. J.CristobalO. L.SaadM.KennéJ. P. and ArchambaultP. S., “Development of a whole arm wearable robotic exoskeleton for rehabilitation and to assist upper limb movements,” Robotica 33(1), 1939 (2014).CrossRef | Google Scholar
28.StroppaF.LoconsoleC.MarcheschiS. and FrisoliA., “A Robot-Assisted Neuro-Rehabilitation System for Post-Stroke Patients’ Motor Skill Evaluation with ALEx Exoskeleton,” In: Converging Clinical and Engineering Research on Neurorehabilitation II (IbáñezJ.González-VargasJ.AzornJ. M.AkayM. and PonsJ. L., eds.) (Springer International PublishingCham2017) pp. 501505.Google Scholar
29.SutapunA. and SangveraphunsiriV., “A 4-DOF upper limb exoskeleton for stroke rehabilitation: Kinematics mechanics and control,” Int. J. Mech. Eng. Rob. Res. 4(3), 269272 (2015).Google Scholar
30.TangZ.ZhangK.SunS.GaoZ.ZhangL. and YangZ., “An upper-limb power-assist exoskeleton using proportional myoelectric control,” Sens. (Basel, Switzerland) 14(4), 66776694 (2014).CrossRef | Google Scholar | PubMed
31.XiaoF.GaoY.WangY.ZhuY. and ZhaoJ., “Design of a wearable cable-driven upper limb exoskeleton based on epicyclic gear trains structure,” Technol. Health Care. 25(S1), 311 (2017).CrossRef | Google Scholar | PubMed
32.GopuraR. A. R. C.BandaraD. S. V.KiguchiK. and MannG. K. I., “Developments in hardware systems of active upper-limb exoskeleton robots: A review,” Rob. Auton. Syst. 75203220 (2016).CrossRef | Google Scholar
33.IslamM.SpiewakC.RahmanM. and FarehR., “A brief review on robotic exoskeletons for upper extremity rehabilitation to find the gap between research porotype and commercial type,” Adv. Robot Autom. 6(3), (2017) pp. 112.CrossRef | Google Scholar
34.JarrasséN.ProiettiT.CrocherV.RobertsonJ.SahbaniA.MorelG. and Roby-BramiA., “Robotic exoskeletons: A perspective for the rehabilitation of arm coordination in stroke patients,” Front. Hum. Neurosci. 8(947), (2014) pp. 113.Google Scholar | PubMed
35.MaciejaszP.EschweilerJ.Gerlach-HahnK.Jansen-TroyA. and LeonhardtS., “A survey on robotic devices for upper limb rehabilitation,” J. NeuroEng. Rehabil. 11(1), 3 (2014).CrossRef | Google Scholar | PubMed
36.ChenY.LiG.ZhuY.ZhaoJ. and CaiH., “Design of a 6-DOF upper limb rehabilitation exoskeleton with parallel actuated joints,” Bio-Med. Mater. Eng. 24(6), 25272535 (2014).CrossRef | Google Scholar | PubMed
37.MadaniT.DaachiB. and DjouaniK., “Modular-controller-design-based fast terminal sliding mode for articulated exoskeleton systems,” IEEE Trans. Control Syst. Technol. 25(3), 11331140 (2017).CrossRef | Google Scholar
38.RahmanM. H.SaadM.KennéJ.-P. and ArchambaultP. S., “Control of an exoskeleton robot arm with sliding mode exponential reaching law,” Int. J. Control Autom. Syst. 11(1), 92104 (2013).CrossRef | Google Scholar
39.GopuraR. A. R. C.KiguchiK. and LiY., “SUEFUL-7: A 7DOF Upper-limb Exoskeleton Robot with Muscle-model-oriented EMG-based Control,” 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (2009) pp. 11261131.Google Scholar
40.ZeiaeeA.Soltani-ZarrinR.LangariR. and TafreshiR., “Design and kinematic analysis of a novel upper limb exoskeleton for rehabilitation of stroke patients,” 2017 International Conference on Rehabilitation Robotics (ICORR) (2017) pp. 759764.Google Scholar
41.FellagR.BenyahiaT.DriasM.GuiatniM. and HamerlainM., “Sliding Mode Control of a 5 Dofs Upper Limb Exoskeleton Robot,” 2017 5th International Conference on Electrical Engineering – Boumerdes (ICEE-B) (2017) pp. 16.Google Scholar
42.BabaiaslM.GoldarS. N.BarhaghtalabM. H. and MeigoliV., “Sliding Mode Control of an Exoskeleton Robot for use in Upper-limb Rehabilitation,” 2015 3rd RSI International Conference on Robotics and Mechatronics (ICROM) (2015) pp. 694701.Google Scholar
43.BrahmiB.SaadM.LunaC. O.ArchambaultP. S. and RahmanM. H., “Sliding Mode Control of an Exoskeleton Robot Based on Time Delay Estimation,” 2017 International Conference on Virtual Rehabilitation (ICVR) (2017) pp. 12.Google Scholar
44.ZhuS.JinX.YaoB.ChenQ.PeiX. and PanZ., “Non-linear sliding mode control of the lower extremity exoskeleton based on human–robot cooperation,” Int. J. Adv. Rob. Syst. 13(5), 1729881416662788 (2016).Google Scholar
45.ChenH.ChenH. and YangF., “Fractional-order Sliding-mode Stabilization of Nonholonomic Mobile Robots Based on Dynamic Feedback Linearization,” 2016 35th Chinese Control Conference (CCC) (2016) pp. 58745878.Google Scholar
46.ChengZ.MaZ.SunG. and DongH., “Fractional Order Sliding Mode Control for Attitude and Altitude Stabilization of a Quadrotor UAV,” 2017 Chinese Automation Congress (CAC) (2017) pp. 26512656.Google Scholar
47.TianyiZ.XuemeiR. and YaoZ., “A Fractional Order Sliding Mode Controller Design for Spacecraft Attitude Control System,” 2015 34th Chinese Control Conference (CCC) (2015) pp. 33793382.Google Scholar
48.TianJ.ChenN.YangJ. and WangL., “Fractional Order Sliding Model Control of Active Four-wheel Steering Vehicles,” ICFDA’14 International Conference on Fractional Differentiation and Its Applications 2014 (2014) pp. 15.Google Scholar
49.BouroubaB. and LadaciS., “Stabilization of Class of Fractional-order Chaotic System Via New Sliding Mode Control,” 2017 6th International Conference on Systems and Control (ICSC) (2017) pp. 470475.Google Scholar
50.KangJ.ZhuZ. H.WangW.LiA. and WangC., “Fractional order sliding mode control for tethered satellite deployment with disturbances,” Adv. Space Res. 59(1), 263273 (2017).CrossRef | Google Scholar
51.IslamM. R.Assad-Uz-ZamanM. and RahmanM. H., “Design and control of an ergonomic robotic shoulder for wearable exoskeleton robot for rehabilitation,” Int. J. Dyn. Control (2019) pp. 114.Google Scholar
52.CraboluM.PaniD.RaffoL.ContiM.CrivelliP. and CereattiA., “In vivo estimation of the shoulder joint center of rotation using magneto-inertial sensors: MRI-based accuracy and repeatability assessment,” Biomed. Eng. Online 16(1), 3434 (2017).CrossRef | Google Scholar | PubMed
53.HalderA. M.ItoiE. and AnK.-N., “Anatomy and biomechanics of the shoulder,” Orthop. Clinic. 31(2), 159176 (2000).Google Scholar
54.Soltani-ZarrinR.ZeiaeeA.LangariR. and TafreshiR., “A Computational Approach for Human-like Motion Generation in Upper Limb Exoskeletons Supporting Scapulohumeral Rhythms,” IEEE International Symposium on Wearable & Rehabilitation Robotics (WeRob2017) (Houston, Texas, USA, 2017) pp. 12.CrossRef | Google Scholar
55.CraigJ. J.Introduction to Robotics: Mechanics and Control (PearsonUpper saddle river, New Jersy2017) p. 448.Google Scholar
56.DenavitJ. and HartenbergR. S., “A kinematic notation for lower-pair mechanisms based on matrices,” Trans. of the ASME. J. Appl. Mech. 22215221 (1955).Google Scholar
57.RahmaniM. and RahmanM. H., “Novel robust control of a 7-DOF exoskeleton robot,” PLoS One 13(9), e0203440 (2018).CrossRef | Google Scholar | PubMed
58.RahmaniM.RahmanM. H. and GhommamJ., “A 7-DoF upper limb exoskeleton robot control using a new robust hybrid controller,” Int. J. Control Autom. Syst. 17(4), 986994 (2019).CrossRef | Google Scholar
59.WinterD. A., “Anthropometry,” In: Biomechanics and Motor Control of Human Movement (WinterD.A., eds.) (John Wiley & SonsNew York2009) p. 370.CrossRef | Google Scholar

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[Abstract] Upper Limb Movement Modelling for Adaptive and Personalised Physical Rehabilitation in Virtual Reality – Thesis

Abstract

Stroke is one of the leading causes of disability with over three-quarters of patients experiencing an upper limb impairment varying in severity. Early, intense, and frequent physical rehabilitation is important for quicker recovery of the upper limbs and the prevention of further deterioration of their upper limb impairment. Rehabilitation begins almost immediately at the hospital. Once released from the hospital it is intended that patients continue their rehabilitation program at home supported by a community stroke team. However, there are two main barriers to rehabilitation continuing effectively at this stage. The first is limited contact with a physiotherapist or occupational therapist to guide and support an intensive rehabilitation programme. The second is that conventional rehabilitation is tough to maintain immediately after stroke due to fatigue, lack of concentration, depression and other effects. Stroke patients can find exercises monotonous and tiring, and a lack of motivation can result in patients failing to engage fully with their treatment. Lack of participation in prescribed rehabilitation exercises may affect recovery or cause deterioration of mobility.

This thesis examines the hypothesis that upper limb stroke rehabilitation can be made more accessible and enjoyable through the use of modern commercial virtual reality (VR) hardware, with personalised models of user hand motion adapted to user capability over time, and VR games with tasks that utilise natural hand gestures as input controls to execute personalised physical rehabilitation exercises. To support the investigation of this hypothesis a novel adaptive, gamebased, virtual reality (VR) rehabilitation system has been designed and developed for self-managed rehabilitation. Hands are tracked using a Leap Motion Controller, with hand movements and gestures used as in input controller for VR tasks. A user-centred design methodology was adopted, and the final version of the system was evolved through several versions and iterative testing and feedback through trials with able-bodied testers, stroke survivor volunteers, and practising clinicians.

A key finding of the research was that an adapted form of Fitts’s law, that models difficulty of reaching and touching objects in 3D interaction spaces, could be used to profile movement capability for able-bodied people and stroke patients vii in upper arm VR stroke rehabilitation. It was also found that even when Fitts’s law was less effective, that the statistics of the regression quality were still informative in profiling users. Fitts law regression statistics along with information on task performance (such as percentage of hits) could be used to adapt task difficulty or advising rest. Further, it was found that multiple regression could provide better movement capability profiles with a modified form of Fitts law to account for varying degrees of difficulty due to the angles of motion in 3D space. In addition, a novel approach was developed which profiled sectors of the 3D VR interaction space separately, rather than treat movement through the whole space as being equally difficult. This approach accounts for some stroke patients having more difficulty moving in some directions than others, e.g. up and left. Results demonstrate that this has potential but may need to be investigated further with stroke patients and with larger numbers of people.

The VR system that utilised the movement capability model was evolved over time with a user-centred design methodology, with input from able-bodied people, stroke patients, and clinicians. A final longitudinal study investigated the suitability of three bespoke games, the usability of the system over a longer time, and the effectiveness of the movement profiler and adaptive system. Throughout this experiment, the system provided informative user movement profile variations that could identify unique movement behaviour traits in individuals. Results showed that user performance varied over time and the adaptive system proved effective in changing the difficulty of the tasks for individuals over multiple sessions. The VR rehabilitation games incorporated enhanced gameplay and feedback, and users expressed enjoyment with the interactive experience. Throughout all of the experiments, users enjoyed wearing a VR headset, preferring it over a standard PC monitor. Most users subjectively felt that they were more effective in completing tasks within VR, and results from experiments provided empirical evidence to support this view. Results within this thesis support the proposal that an appropriately designed, adaptive gamebased VR system can provide an accessible, personalised and enjoyable rehabilitation system that can motivate more regular rehabilitation participation and promote improved motor function.

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[Abstract] Pushing the limits of recovery in chronic stroke survivors: User perceptions of the Queen Square Upper Limb Neurorehabilitation Programme – Full Text PDF

Abstract

Introduction: The Queen Square Upper Limb (QSUL) Neurorehabilitation Programme is a clinical service within the National Health Service in the United Kingdom that provides 90 hours of therapy over three weeks to stroke survivors with persistent upper limb impairment. This study aimed to explore the perceptions of participants of this programme, including clinicians, stroke survivors and carers.

Design: Descriptive qualitative.

Setting: Clinical outpatient neurorehabilitation service.

Participants: Clinicians (physiotherapists, occupational therapists, rehabilitation assistants) involved in the delivery of the QSUL Programme, as well as stroke survivors and carers who had participated in the programme were purposively sampled. Each focus group followed a series of semi-structured, open questions that were tailored to the clinical or stroke group. One independent researcher facilitated all focus groups, which were audio-recorded, transcribed verbatim and analysed by four researchers using a thematic approach to identify main themes.

Results: Four focus groups were completed: three including stroke survivors (n = 16) and carers (n = 2), and one including clinicians (n = 11). The main stroke survivor themes related to psychosocial aspects of the programme (″ you feel valued as an individual ″), as well as the behavioural training provided (″ gruelling, yet rewarding& [Prime]). The main clinician themes also included psychosocial aspects of the programme (″ patient driven ethos − no barriers, no rules ″), and knowledge, skills and resources of clinicians (″ it is more than intensity, it is complex ″).

Conclusions: As an intervention, the QSUL Programme is both comprehensive and complex. The impact of participation in the programme spans psychosocial and behavioural domains from the perspectives of both the stroke survivor and clinician.

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