Posts Tagged Upper Extremity

[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

 

via A Mechatronic Mirror-Image Motion Device for Symmetric Upper-Limb Rehabilitation | SpringerLink

 

, , , , , , ,

Leave a comment

[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.

 Download Full Text PDF

, , , , , , , ,

Leave a comment

[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

, , , , , , , , , ,

Leave a comment

[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

via A Novel Exoskeleton with Fractional Sliding Mode Control for Upper Limb Rehabilitation | Robotica | Cambridge Core

, , , , , , , , ,

Leave a comment

[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.

via Upper Limb Movement Modelling for Adaptive and Personalised Physical Rehabilitation in Virtual Reality — Ulster University

, , , , , , , , , ,

Leave a comment

[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.

Download Full Text PDF

via Pushing the limits of recovery in chronic stroke survivors: User perceptions of the Queen Square Upper Limb Neurorehabilitation Programme. | medRxiv

, , , , , , , , ,

Leave a comment

[Abstract + References] Game Design Principles Influencing Stroke Survivor Engagement for VR-Based Upper Limb Rehabilitation: A User Experience Case Study – Proceedings

ABSTRACT

Engagement with one’s rehabilitation is crucial for stroke survivors. Serious games utilising desktop Virtual Reality could be used in rehabilitation to increase stroke survivors’ engagement. This paper discusses the results of a user experience case study that was conducted with six stroke survivors to determine which game design principles are or would be important for engaging them with a desktop VR serious games designed for the upper limb rehabilitation. The results of our study showed the game design principles that warrant further investigation are awareness, feedback, interactivity, flow and challenge; and also important to a great extent are attention, involvement, motivation, effort, clear instructions, usability, interest, psychological absorption, purpose and a first-person view.

References

  1. B. Ploderer, J. Stuart, V. Tran, T. Green, and J. Muller, “The transition of stroke survivors from hospital to home: understanding work and design opportunities,” in OZCHI, Brisbane, Australia, 2017, pp. 1–9.Google Scholar
  2. G. A. MacDonald, N. M. Kayes, and F. Bright, “Barriers and facilitators to engagement in rehabilitation for people with stroke: a review of the literature,” New Zealand Journal of Physiotherapy, vol. 41, p. 112, 2013.Google Scholar
  3. J. W. Burke, M. D. J. McNeill, D. K. Charles, P. J. Morrow, J. H. Crosbie, and S. M. McDonough, “Optimising engagement for stroke rehabilitation using serious games,” The Visual Computer, vol. 25, pp. 1085–1099, 2009.Google ScholarDigital Library
  4. G. C. Peron, L. I. B. dos Santos, L. M. Brasil, R. C. Silva, F. Bombonato, P. S. de Lima, et al., “Serious games in cognitive rehabilitation,” 2011, pp. 94–95.Google Scholar
  5. D. Jack, R. Boian, A. S. Merians, M. Tremaine, G. C. Burdea, S. V. Adamovich, et al., “Virtual reality-enhanced stroke rehabilitation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 9, pp. 308–318, 2001.Google ScholarCross Ref
  6. D. Webster and O. Celik, “Systematic review of Kinect applications in elderly care and stroke rehabilitation,” Journal of neuroengineering and rehabilitation, vol. 11, pp. 108–108, 2014.Google ScholarCross Ref
  7. A. Henderson, N. Korner-Bitensky, and M. Levin, “Virtual reality in stroke rehabilitation: a systematic review of its effectiveness for upper limb motor recovery,” Topics in stroke rehabilitation, vol. 14, pp. 52–61, 2007.Google ScholarCross Ref
  8. D. Sadihov, B. Migge, R. Gassert, and Y. Kim, “Prototype of a VR upper-limb rehabilitation system enhanced with motion-based tactile feedback,” pp. 449–454.Google Scholar
  9. O. Dele-Ajayi, J. Sanderson, R. Strachan, and A. Pickard, “Learning mathematics through serious games: An engagement framework,” 2016, pp. 1–5.Google Scholar
  10. H. Jokinen, S. Melkas, R. Ylikoski, T. Pohjasvaara, M. Kaste, T. Erkinjuntti, et al., “Post-stroke cognitive impairment is common even after successful clinical recovery,” European Journal of Neurology, vol. 22, pp. 1288–1294, 2015.Google ScholarCross Ref
  11. K. Lohse, N. Shirzad, A. Verster, N. Hodges, and H. F. M. Van der Loos, “Video Games and Rehabilitation: Using Design Principles to Enhance Engagement in Physical Therapy,” Journal of Neurologic Physical Therapy, vol. 37, pp. 166–175, 2013.Google ScholarCross Ref
  12. T. M. Connolly, T. Hainley, E. Boyle, G. Baxter, and P. Moreno-Ger, Psychology, Pedagogy, and Assessment in Serious Games. Hershey, Pennsylvania: Information Science Reference, 2014.Google Scholar
  13. A. K. Przybylski, C. S. Rigby, and R. M. Ryan, “A Motivational Model of Video Game Engagement,” Review of General Psychology, vol. 14, pp. 154–166, 2010.Google ScholarCross Ref
  14. M. A. Bruno and L. Griffiths, “Serious games: supporting occupational engagement of people aged 50+ based on intelligent tutoring systems/Juegos serios: apoyo a la participación ocupacional de personas mayores de 50 años basado en sistemas de tutoría inteligente,” Ingeniare: Revista Chilena de Ingenieria, vol. 22, p. 125, 2014.Google ScholarCross Ref
  15. S. Arnab, I. Dunwell, and K. Debattista, Serious games for healthcare: applications and implications. Hershey, PA: Medical Information Science Reference, 2013.Google ScholarCross Ref
  16. R. S. Kalawsky, The Science of Virtual Reality and Virtual Environments: A Technical, Scientific and Engineering Reference on Virtual Environments. Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc., 1993.Google Scholar
  17. K. Pimentel and K. Teixeira, Virtual Reality: Through the New Looking Glass, 2nd ed. New York: Intel/McGraw-Hill, 1995.Google Scholar
  18. S. Rabin, Introduction to game development, 2nd ed. Boston, MA: Course Technology, Cengage Learning, 2010.Google Scholar
  19. A. Oxarart, J. Weaver, A. Al-Bataineh, and T. A. B. Mohamed, “Game Design Principles and Motivation,” International Journal of Arts & Sciences, vol. 7, p. 347, 2014.Google Scholar
  20. H. Desurvire and D. Wixon, “Game principles: choice, change & creativity: making better games,” pp. 1065–1070.Google Scholar
  21. R. McDaniel, S. Fiore, M., and D. Nicholson, “Serious Storytelling: Narrative Considerations for Serious Games Researchers and Developers,” in Serious Game Design and Development: Technologies for Training and Learning, ed Hershey, PA, USA: IGI Global, 2010, pp. 13–30.Google Scholar
  22. R. M. Martey, K. Kenski, J. Folkestad, L. Feldman, E. Gordis, A. Shaw, et al., “Measuring Game Engagement: Multiple Methods and Construct Complexity,” Simulation & Gaming, vol. 45, pp. 528–547, 2014.Google ScholarDigital Library
  23. H. L. O’Brien and E. G. Toms, “What is user engagement? A conceptual framework for defining user engagement with technology,” Journal of the American Society for Information Science and Technology, vol. 59, pp. 938–955, 2008.Google ScholarDigital Library
  24. J. H. Brockmyer, C. M. Fox, K. A. Curtiss, E. McBroom, K. M. Burkhart, and J. N. Pidruzny, “The development of the Game Engagement Questionnaire: A measure of engagement in video game-playing,” Journal of Experimental Social Psychology, vol. 45, pp. 624–634, 2009.Google ScholarCross Ref
  25. N. Whitton, “Game Engagement Theory and Adult Learning,” Simulation & Gaming, vol. 42, pp. 596–609, 2011.Google ScholarCross Ref
  26. B. Bongers and S. Smith, “Interactivated rehabilitation device,” in OZCHI, Brisbane, Australia, 2010, pp. 410–411.Google Scholar
  27. E. V. Ekusheva and I. V. Damulin, “Post-Stroke Rehabilitation: Importance of Neuroplasticity and Sensorimotor Integration Processes,” Neuroscience and Behavioral Physiology, vol. 45, pp. 594–599, 2015.Google ScholarCross Ref
  28. Y. Sagi, I. Tavor, S. Hofstetter, S. Tzur-Moryosef, T. Blumenfeld-Katzir, and Y. Assaf, “Learning in the Fast Lane: New Insights into Neuroplasticity,” Neuron, vol. 73, pp. 1195–1203, 2012.Google ScholarCross Ref
  29. S. Hofstetter, I. Tavor, S. Tzur Moryosef, and Y. Assaf, “Short-term learning induces white matter plasticity in the fornix,” The Journal of neuroscience: the official journal of the Society for Neuroscience, vol. 33, p. 12844, 2013.Google ScholarCross Ref
  30. I. Tavor, S. Hofstetter, and Y. Assaf, “Micro-structural assessment of short term plasticity dynamics,” NeuroImage, vol. 81, pp. 1–7, 2013.Google ScholarCross Ref
  31. Murdoch University. (2015, 6/3/2019). Virtual Reality software brings hope to stroke survivors. Available: http://web.archive.org/web/20180331035704/http://media.murdoch.edu.au/virtualreality-software-brings-hope-to-stroke-survivorsGoogle Scholar
  32. S. Brown. (2010). Likert Scale Examples for Surveys. Available: http://www.extension.iastate.edu/Documents/ANR/LikertScaleExamplesforSurveys.pdfGoogle Scholar
  33. Oxford Dictionary of English, 3rd ed. New York, NY: Oxford University Press, 2010.Google Scholar

via Game Design Principles Influencing Stroke Survivor Engagement for VR-Based Upper Limb Rehabilitation | Proceedings of the 31st Australian Conference on Human-Computer-Interaction

, , , , , , , ,

Leave a comment

[Abstract] Timing-dependent interaction effects of tDCS with mirror therapy on upper extremity motor recovery in patients with chronic stroke: A randomized controlled pilot study

Highlights

  • The priming effect of dual tDCS was important to facilitate motor recovery in combination with mirror therapy in stroke.

Abstract

This study was a randomized, controlled pilot trial to investigate the timing-dependent interaction effects of dual transcranial direct current stimulation (tDCS) in mirror therapy (MT) for hemiplegic upper extremity in patients with chronic stroke. Thirty patients with chronic stroke were randomly assigned to three groups: tDCS applied before MT (prior-tDCS group), tDCS applied during MT (concurrent-tDCS group), and sham tDCS applied randomly prior to or concurrent with MT (sham-tDCS group). Dual tDCS at 1 mA was applied bilaterally over the ipsilesional M1 (anodal electrode) and the contralesional M1 (cathodal electrode) for 30 min. The intervention was delivered five days per week for two weeks. Upper extremity motor performance was measured using the Fugl-Meyer Assessment-Upper Extremity (FMA-UE), the Action Research Arm Test (ARAT), and the Box and Block Test (BBT). Assessments were administered at baseline, post-intervention, and two weeks follow-up. The results indicated that concurrent-tDCS group showed significant improvements in the ARAT in relation to the prior-tDCS group and sham-tDCS group at post-intervention. Besides, a trend toward greater improvement was also found in the FMA-UE for the concurrent-tDCS group. However, no statistically significant difference in the FMA-UE and BBT was identified among the three groups at either post-intervention or follow-up. The concurrent-tDCS seems to be more advantageous and time-efficient in the context of clinical trials combining with MT. The timing-dependent interaction factor of tDCS to facilitate motor recovery should be considered in future clinical application.

via Timing-dependent interaction effects of tDCS with mirror therapy on upper extremity motor recovery in patients with chronic stroke: A randomized controlled pilot study – Journal of the Neurological Sciences

, , , , , , , , , ,

Leave a comment

[WEB SITE] Vagal Nerve Stimulation Improves Arm Function After Stroke

By

HOUSTON, Texas — An implanted device that stimulates the vagus nerve has shown promising improvement of arm function in stroke patients in a second small clinical study.

While the primary endpoint — change in functional score after 6 weeks of therapy — was not significantly different between treatment groups, the improvement did appear to become significant after a further 60 days of treatment, as did responder rates.

Lead investigator, Jesse Dawson, MD, University of Glasgow, United Kingdom, reported that the group receiving active stimulation with the device showed a 9-point improvement in upper-limb Fugl-Meyer (UEFM) score at this time point.

Dr Jesse Dawson

“All in all, we feel this is quite promising,” Dr Dawson said. “A 9-point change in this scale is highly likely to be clinically significant.”

This magnitude of change would mean different things for different patients, depending on where they start, he said. “If they start at 20 — which is not much function at all — they might regain some grasp ability so they might be able to carry a plate, for example. If they were in the 30s to start with, they would probably already have the grasp function but they would be able to get back to do more specific tasks.”

The results were presented here at the International Stroke Conference (ISC) 2017.

“Spectacular” Results

Commenting on the study, American Heart Association/American Stroke Association spokesperson, Philip Gorelick, MD, MPH, medical director, Hauenstein Neuroscience Center, Grand Rapids, Michigan, described the results as “pretty spectacular.”

Dr Philip Gorelick

“It is always difficult to know what you are getting with these scales, but when you see jumps like this I think it’s safe to conclude that there is clinical significance. There is probably something real going on,” Dr Gorelick said.

“You must remember that these are chronic patients with moderate to severe arm weakness at 18 months down the line from their stroke,” he added. “We think these patients are finished — they are not going to be doing much with that arm. Obviously this study is exploratory, but this raises a lot of hope.”

A larger trial in 120 patients is now planned.

, , , , , ,

Leave a comment

[ARTICLE] Upper Extremity Function Assessment Using a Glove Orthosis and Virtual Reality System – Full Text

Abstract

Hand motor control deficits following stroke can diminish the ability of patients to participate in daily activities. This study investigated the criterion validity of upper extremity (UE) performance measures automatically derived from sensor data during manual practice of simulated instrumental activities of daily living (IADLs) within a virtual environment. A commercial glove orthosis was specially instrumented with motion tracking sensors to enable patients to interact, through functional UE movements, with a computer-generated virtual world using the SaeboVR software system. Fifteen stroke patients completed four virtual IADL practice sessions, as well as a battery of gold-standard assessments of UE motor and hand function. Statistical analysis using the nonparametric Spearman rank correlation reveals high and significant correlation between virtual world-derived measures and the gold-standard assessments. The results provide evidence that performance measures generated during manual interactions with a virtual environment can provide a valid indicator of UE motor status.

Introduction

Virtual world-based games, when combined with human motion sensing, can enable a neurorehabilitation patient to engage in realistic occupations that involve repetitive practice of functional tasks (). An important component of such a system is the ability to automatically track patient movements and use those data to produce indices related to movement quality (). Before these technology-derived measures can be considered relevant to clinical outcomes, criterion validity must be established. If validated, measures of virtual task performance may reasonably be interpreted as reflective of real-world functional status.

The objective of the study described in this article was to investigate the criterion validity of upper extremity (UE) performance measures automatically derived from sensor data collected during practice of simulated instrumental activities of daily living (IADLs) in a virtual environment. A commercially available SaeboGlove orthosis () was specially instrumented to enable tracking of finger and thumb movements. This instrumented glove was employed with an enhanced version of the Kinect sensor-based SaeboVR software system () to enable employment of the hand, elbow, and shoulder in functional interactions with a virtual world. Performance measures were automatically generated during patient use through a combination of arm tracking data from the Kinect and the glove’s finger and thumb sensors. The primary investigational objective was to determine whether performance indices produced by this system for practice of virtual IADLs are valid indicators of a stroke patient’s UE motor status.

Previous investigations into combining hand tracking with video games to animate UE therapy have produced evidence for the efficacy of such interventions. A recent study compared a 15-session hand therapy intervention using a smart glove system and video games with a usual care regimen (). Stroke patients using the smart glove system realized greater gains in Wolf Motor Function Test (WMFT) score compared with dosage-balanced conventional therapy. Another study investigating a similar glove-based device found significantly greater improvements in Fugl-Meyer and Box and Blocks test results for stroke patients who performed 15 sessions that included the technology-aided therapy compared with subjects receiving traditional therapy only (). An instrumented glove has also been used to support video game therapy that incorporates gripping-like movements and thumb-finger opposition ().

Past research into the use of human motion tracking (sometimes referred to as motion capture) technologies for assessment of UE function has produced encouraging results. One group of researchers compared naturalistic point-to-point reaching movements with standardized reaching movements embedded in a virtual reality system, and established concurrent validity between the two (). An investigation involving a device that incorporates handgrip strength and pinch force measurement into virtual reality exercises provided support for system use as an objective evaluation of hand function, and for the potential of replacing conventional goniometry and dynamometry (). In another study, researchers employed a Kinect sensor in a software system that attempts to emulate a subset of the Fugl-Meyer Upper Extremity (FMUE) assessment (). Pearson correlation analysis between the Kinect-derived scores and traditionally administered FMUE test results for 41 hemiparetic stroke patients revealed a high correlation. Previous research involving the SaeboVR system established a moderate and statistically significant correlation between virtual IADL performance scores and the WMFT (). Due to limitations of the Kinect optical tracking system, this previous work involving the SaeboVR system did not include tracking of grasp-release manual interactions with virtual objects (). The present research addresses this limitation by fusing data from the Kinect sensor with data from finger- and wrist-mounted sensors on the SaeboGlove orthosis to reconstruct the kinematic pose of the patient’s UE.

The use of an assistive glove orthosis in the present work fills an important clinical need. Inability to bring the hand and wrist into a neutral position due to weakness and/or lack of finger extension can prevent participation in occupation-oriented functional practice (). A common technique to enable stroke patients to achieve a functional hand position (and thus participate in rehabilitation) is a dynamic splint that supports finger and/or wrist extension. When larger forces are necessary (e.g., to overcome abnormal muscle tone), an outrigger-type splint may be employed. For patients with no more than mild hypertonicity, a lower-profile device such as the SaeboGlove orthosis () can be used. Employment of an assistive glove orthosis in the context of virtual IADLs practice thus addresses some of the leading causes of hand motor control deficits following stroke and their adverse impact on ability to participate in daily activities ().

Method

Participants

Candidates were recruited from a population of stroke patients receiving in-patient rehabilitation care, outpatient rehabilitation, or who had been previously discharged from rehabilitative care at the UVA Encompass Health Rehabilitation Hospital (Charlottesville, VA, USA). Table 1 includes the study characteristics. Of 17 patients enrolled in the study, 15 completed the protocol. One subject dropped out due to unrelated illness. A second subject was disenrolled due to an inability to adequately express an understanding of consent during re-verification at the beginning of the first post-consent study session.

Table 1.

Patient Characteristics (n = 17).

Age, years, median (range) 67 (25-83)
Time since stroke onset in months, median (range) 12 (1-72)
Sex, M/F, n (%) 10 (59)/7 (41)
Race category, Black/White, n (%) 3 (18)/14 (82)
Ethnic category, Hispanic/non-Hispanic, n (%) 0 (0)/17 (100)
Side of hemiplegia, L/R, n (%) 10 (59)/7 (41)
Affected side dominance, dominant/nondominant, n (%) 9 (53)/8 (47)

All study activities were conducted under the auspices of the University of Virginia Institutional Review Board for Health Sciences Research (IRB-HSR). All study sessions took place in a private room within the UVA Encompass Health outpatient clinic between October 20, 2017, and February 9, 2018. Licensed Occupational Therapists trained in study procedures and registered with the IRB-HSR were responsible for all patient contact, recruitment, consent, and protocol administration.

Verification of inclusion/exclusion criteria was through a three-step process including an initial medical record review prior to recruitment, verbal confirmation prior to administration of consent, and an evaluation screen conducted by a study therapist following consent. Inclusion criteria included history of stroke with hemiplegia, ongoing stroke-related hand impairment, sufficient active finger flexion at the metacarpal phalangeal joint in at least one finger to be detected by visual observation by a study therapist, antigravity strength at the elbow to at least 45° of active flexion, antigravity shoulder strength to at least 30° each in active flexion and abduction/adduction, and 15° in active shoulder rotation from an upright seated position. Participants had visual acuity with corrective lenses of 20/50 or better and were able to understand and follow verbal directions. The study excluded patients with visual field deficit in either eye that would impair ability to view the computer monitor and/or with hemispatial neglect that would impair an individual’s ability to process and perceive visual stimuli. The study also excluded individuals with motor limb apraxia, significant muscle spasticity, or contractures of the muscles, joints, tendons, ligaments, or skin that would restrict normal UE movement.

Materials

A commercial SaeboGlove orthosis was fitted with wrist and finger motion sensors to permit tracking of finger joint angles during grasp-release interactions with a virtual environment. The instrumented glove orthosis is shown in Figure 1. The sensors were attached to the existing tensioner band hooks on the dorsal side of each glove finger. An electronics enclosure mounted to the palmar side of the SaeboGlove’s plastic wrist splint processes the sensor data and transmits information to a personal computer (PC) that hosts the modified SaeboVR software. Data from both the SaeboGlove-integrated sensors and from a Kinect sensor were used by a custom motion capture algorithm, which employs a human UE kinematics model to produce real-time estimates of arm, wrist, and finger joint angles.

An external file that holds a picture, illustration, etc.Object name is 10.1177_1539449219829862-fig1.jpg

Figure 1.
SaeboGlove orthosis with sensors to track grasp interactions.

[…]

 

Continue —->  Upper Extremity Function Assessment Using a Glove Orthosis and Virtual Reality System

, , , , , , , , , ,

Leave a comment

%d bloggers like this: