A comparison of the effects and usability of two exoskeletal robots with and without robotic actuation for upper extremity rehabilitation among patients with stroke: a single-blinded randomised controlled pilot study

Jin Ho Park, Gyulee Park, Ha Yeon Kim, Ji-Yeong Lee, Yeajin Ham, Donghwan Hwang, Suncheol Kwon, Joon-Ho Shin, Jin Ho Park, Gyulee Park, Ha Yeon Kim, Ji-Yeong Lee, Yeajin Ham, Donghwan Hwang, Suncheol Kwon, Joon-Ho Shin

Abstract

Background: Robotic rehabilitation of stroke survivors with upper extremity dysfunction may yield different outcomes depending on the robot type. Considering that excessive dependence on assistive force by robotic actuators may interfere with the patient's active learning and participation, we hypothesised that the use of an active-assistive robot with robotic actuators does not lead to a more meaningful difference with respect to upper extremity rehabilitation than the use of a passive robot without robotic actuators. Accordingly, we aimed to evaluate the differences in the clinical and kinematic outcomes between active-assistive and passive robotic rehabilitation among stroke survivors.

Methods: In this single-blinded randomised controlled pilot trial, we assigned 20 stroke survivors with upper extremity dysfunction (Medical Research Council scale score, 3 or 4) to the active-assistive robotic intervention (ACT) and passive robotic intervention (PSV) groups in a 1:1 ratio and administered 20 sessions of 30-min robotic intervention (5 days/week, 4 weeks). The primary (Wolf Motor Function Test [WMFT]-score and -time: measures activity), and secondary (Fugl-Meyer Assessment [FMA] and Stroke Impact Scale [SIS] scores: measure impairment and participation, respectively; kinematic outcomes) outcome measures were determined at baseline, after 2 and 4 weeks of the intervention, and 4 weeks after the end of the intervention. Furthermore, we evaluated the usability of the robots through interviews with patients, therapists, and physiatrists.

Results: In both the groups, the WMFT-score and -time improved over the course of the intervention. Time had a significant effect on the WMFT-score and -time, FMA-UE, FMA-prox, and SIS-strength; group × time interaction had a significant effect on SIS-function and SIS-social participation (all, p < 0.05). The PSV group showed better improvement in participation and smoothness than the ACT group. In contrast, the ACT group exhibited better improvement in mean speed.

Conclusions: There were no differences between the two groups regarding the impairment and activity domains. However, the PSV robots were more beneficial than ACT robots regarding participation and smoothness. Considering the high cost and complexity of ACT robots, PSV robots might be more suitable for rehabilitation in stroke survivors capable of voluntary movement. Trial registration The trial was registered retrospectively on 14 March 2018 at ClinicalTrials.gov (NCT03465267).

Keywords: Exoskeleton devices; Motivations; Neurological rehabilitation; Quality of life; Rehabilitation; Robot; Robotic rehabilitation; Stroke; Stroke rehabilitation; Upper extremity.

Conflict of interest statement

The authors report no conflicts of interest.

Figures

Fig. 1
Fig. 1
Two types of rehabilitation robots used for the robotic rehabilitation. a Armeo® Power for the ACT group and b Armeo® Spring for the PSV group. ACT active-assistive robotic intervention, PSV passive robotic intervention
Fig. 2
Fig. 2
a A picture of the experimental setup for kinematic measurements. b Illustration of placement of the base button and target buttons
Fig. 3
Fig. 3
Flow chart showing the study design
Fig. 4
Fig. 4
a WMFT-score, b WMFT-time, c FMA-UE, d FMA-prox. Values are presented as mean ± standard error. ACT active-assistive robotic intervention, PSV passive robotic intervention
Fig. 5
Fig. 5
Examples of reaching trajectories across time from a patient with stroke in a the ACT group and in b the PSV group. ACT active-assistive robotic intervention, PSV passive robotic intervention
Fig. 6
Fig. 6
a Spectral arc length and b mean speed. Values are presented as mean ± standard error. ACT active-assistive robotic intervention, PSV passive robotic intervention

References

    1. Kwakkel G, Kollen BJ, van der Grond J, Prevo AJ. Probability of regaining dexterity in the flaccid upper limb: impact of severity of paresis and time since onset in acute stroke. Stroke. 2003;34(9):2181–2186. doi: 10.1161/.
    1. Klamroth-Marganska V, Blanco J, Campen K, Curt A, Dietz V, Ettlin T, Felder M, Fellinghauer B, Guidali M, Kollmar A. Three-dimensional, task-specific robot therapy of the arm after stroke: a multicentre, parallel-group randomised trial. Lancet Neurol. 2014;13(2):159–166. doi: 10.1016/S1474-4422(13)70305-3.
    1. Aprile I, Cruciani A, Germanotta M, Gower V, Pecchioli C, Cattaneo D, Vannetti F, Padua L, Gramatica F. Upper limb robotics in rehabilitation: an approach to select the devices, based on rehabilitation aims, and their evaluation in a feasibility study. Appl Sci. 2019;9(18):3920. doi: 10.3390/app9183920.
    1. Mehrholz J, Pohl M, Platz T, Kugler J, Elsner B. Electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength after stroke. Cochrane Database Syst Rev. 2018;9:8.
    1. Lo AC, Guarino PD, Richards LG, Haselkorn JK, Wittenberg GF, Federman DG, Ringer RJ, Wagner TH, Krebs HI, Volpe BT. Robot-assisted therapy for long-term upper-limb impairment after stroke. N Engl J Med. 2010;362(19):1772–1783. doi: 10.1056/NEJMoa0911341.
    1. Veerbeek JM, Langbroek-Amersfoort AC, van Wegen EEH, Meskers CGM, Kwakkel G. Effects of robot-assisted therapy for the upper limb after stroke: a systematic review and meta-analysis. Neurorehabil Neural Repair. 2016;31(2):107–121. doi: 10.1177/1545968316666957.
    1. Lee SH, Park G, Cho DY, Kim HY, Lee J-Y, Kim S, Park S-B, Shin J-H. Comparisons between end-effector and exoskeleton rehabilitation robots regarding upper extremity function among chronic stroke patients with moderate-to-severe upper limb impairment. Scientific Rep. 2020;10(1):1–8. doi: 10.1038/s41598-019-56847-4.
    1. Denève A, Moughamir S, Afilal L, Zaytoon J. Control system design of a 3-DOF upper limbs rehabilitation robot. Comput Methods Programs Biomed. 2008;89(2):202–214. doi: 10.1016/j.cmpb.2007.07.006.
    1. Masia L, Xiloyannis M, Khanh DB, Wilson AC, Contu S, Yongtae KG. Chapter 4 - Actuation for robot-aided rehabilitation: Design and control strategies. In: Colombo R, Sanguineti V, editors. Rehabilitation Robotics. New York: Academic Press; 2018. pp. 47–61.
    1. Lo HS, Xie SQ. Exoskeleton robots for upper-limb rehabilitation: State of the art and future prospects. Med Eng Phys. 2012;34(3):261–268. doi: 10.1016/j.medengphy.2011.10.004.
    1. Ochoa Luna C, Habibur Rahman M, Saad M, Archambault PS, Bruce Ferrer S. Admittance-based upper limb robotic active and active-assistive movements. Int J Adv Rob Syst. 2015;12(9):117. doi: 10.5772/60784.
    1. Maciejasz P, Eschweiler J, Gerlach-Hahn K, Jansen-Troy A, Leonhardt S. A survey on robotic devices for upper limb rehabilitation. J Neuroeng Rehabil. 2014;11:3. doi: 10.1186/1743-0003-11-3.
    1. Sivan M, O'Connor RJ, Makower S, Levesley M, Bhakta B. Systematic review of outcome measures used in the evaluation of robot-assisted upper limb exercise in stroke. J Rehabil Med. 2011;43(3):181–189. doi: 10.2340/16501977-0674.
    1. Wolf SL, Catlin PA, Ellis M, Archer AL, Morgan B, Piacentino A. Assessing wolf motor function test as outcome measure for research in patients after stroke. Stroke. 2001;32(7):1635–1639. doi: 10.1161/01.STR.32.7.1635.
    1. Lang CE, Edwards DF, Birkenmeier RL, Dromerick AW. Estimating minimal clinically important differences of upper-extremity measures early after stroke. Arch Phys Med Rehabil. 2008;89(9):1693–1700. doi: 10.1016/j.apmr.2008.02.022.
    1. Thompson-Butel AG, Lin G, Shiner CT, McNulty PA. Comparison of three tools to measure improvements in upper-limb function with poststroke therapy. Neurorehabil Neural Repair. 2015;29(4):341–348. doi: 10.1177/1545968314547766.
    1. Gladstone DJ, Danells CJ, Black SE. The Fugl-Meyer assessment of motor recovery after stroke: a critical review of its measurement properties. Neurorehabil Neural Repair. 2002;16(3):232–240. doi: 10.1177/154596802401105171.
    1. Carod-Artal FJ, Coral LF, Trizotto DS, Moreira CM. The Stroke Impact Scale 3.0. Stroke. 2008;39(9):2477–2484. doi: 10.1161/STROKEAHA.107.513671.
    1. Choi SU, Lee HS, Shin JH, Ho SH, Koo MJ, Park KH, Yoon JA, Kim DM, Oh JE, Yu SH, et al. Stroke Impact Scale 30: reliability and validity evaluation of the korean version. Ann Rehabil Med. 2017;41(3):387–393. doi: 10.5535/arm.2017.41.3.387.
    1. Balasubramanian S, Melendez-Calderon A, Burdet E. A robust and sensitive metric for quantifying movement smoothness. IEEE Trans Biomed Eng. 2011;59(8):2126–2136. doi: 10.1109/TBME.2011.2179545.
    1. Beck Y, Herman T, Brozgol M, Giladi N, Mirelman A, Hausdorff JM. SPARC: a new approach to quantifying gait smoothness in patients with Parkinson’s disease. J NeuroEng Rehabil. 2018;15(1):49. doi: 10.1186/s12984-018-0398-3.
    1. Krebs HI, Hogan N, Aisen ML, Volpe BT. Robot-aided neurorehabilitation. IEEE Trans Rehab Eng. 1998;6(1):75–87. doi: 10.1109/86.662623.
    1. Kahn L, Zygman M, Rymer WZ, Reinkensmeyer D: Effect of robot-assisted and unassisted exercise on functional reaching in chronic hemiparesis. In: 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society: 2001: IEEE; 2001. p. 1344–1347.
    1. Raghavan P, Bilaloglu S, Ali SZ, Jin X, Aluru V, Buckley MC, Tang A, Yousefi A, Stone J, Agrawal SK, et al. The Role of Robotic Path Assistance and Weight Support in Facilitating 3D Movements in Individuals With Poststroke Hemiparesis. Neurorehab Neural Repair. 2020;34(2):134–147. doi: 10.1177/1545968319887685.
    1. Marchal-Crespo L, Reinkensmeyer DJ. Review of control strategies for robotic movement training after neurologic injury. J Neuroeng Rehab. 2009;6(1):20. doi: 10.1186/1743-0003-6-20.
    1. Guo C, Guo S, Ji J, Xi F. Iterative learning impedance for lower limb rehabilitation robot. J Healthcare Eng. 2017;2017:6732459. doi: 10.1155/2017/6732459.
    1. Pan L, Song A, Duan S, Yu Z. Patient-centered robot-aided passive neurorehabilitation exercise based on safety-motion decision-making mechanism. BioMed Res Int. 2017;2017:89.
    1. Weber LM, Stein J. The use of robots in stroke rehabilitation: A narrative review. NeuroRehabilitation. 2018;43(1):99–110. doi: 10.3233/NRE-172408.
    1. Hussain N, Sunnerhagen KS, Murphy MA. End-point kinematics using virtual reality explaining upper limb impairment and activity capacity in stroke. J Neuroeng Rehab. 2019;16(1):82. doi: 10.1186/s12984-019-0551-7.
    1. Washabaugh EP, Treadway E, Gillespie RB, Remy CD, Krishnan C. Self-powered robots to reduce motor slacking during upper-extremity rehabilitation: a proof of concept study. Restor Neurol Neurosci. 2018;36(6):693–708.
    1. Lotze M, Braun C, Birbaumer N, Anders S, Cohen LG. Motor learning elicited by voluntary drive. Brain. 2003;126(4):866–872. doi: 10.1093/brain/awg079.
    1. Alexoulis-Chrysovergis AC. Investigation of novel control strategies for promoting motor learning in the upper limb with a haptic computer exercise system in able-bodied adults and those with motor impairments. Manchester: Metropolitan University; 2017.
    1. Stein J, Krebs HI, Frontera WR, Fasoli SE, Hughes R, Hogan N. Comparison of two techniques of robot-aided upper limb exercise training after stroke. Am J Phys Med Rehabil. 2004;83(9):720–728. doi: 10.1097/01.PHM.0000137313.14480.CE.
    1. Gassert R, Dietz V. Rehabilitation robots for the treatment of sensorimotor deficits: a neurophysiological perspective. J Neuroeng Rehab. 2018;15(1):1–15. doi: 10.1186/s12984-018-0383-x.
    1. Bernhardt J, Hayward KS, Kwakkel G, Ward NS, Wolf SL, Borschmann K, Krakauer JW, Boyd LA, Carmichael ST, Corbett D. Agreed definitions and a shared vision for new standards in stroke recovery research: the stroke recovery and rehabilitation roundtable taskforce. Int J Stroke. 2017;12(5):444–450. doi: 10.1177/1747493017711816.
    1. Nudo R. Recovery after brain injury: mechanisms and principles. Front Hum Neurosci. 2013;7(887):222.
    1. Cirstea CM. Are wearable robots effective for gait recovery after stroke? Stroke. 2019;50(12):3337–3338. doi: 10.1161/STROKEAHA.119.026548.
    1. Cassidy JM, Cramer SC. Spontaneous and therapeutic-induced mechanisms of functional recovery after stroke. Transl Stroke Res. 2017;8(1):33–46. doi: 10.1007/s12975-016-0467-5.
    1. Mehrholz J, Thomas S, Werner C, Kugler J, Pohl M, Elsner B. Electromechanical-assisted training for walking after stroke. Cochrane Database Syst Rev. 2017;8:5.
    1. Lee SH, Park G, Cho DY, Kim HY, Lee J-Y, Kim S, Park S-B, Shin J-H. Comparisons between end-effector and exoskeleton rehabilitation robots regarding upper extremity function among chronic stroke patients with moderate-to-severe upper limb impairment. Scientific Rep. 2020;10(1):1806. doi: 10.1038/s41598-020-58630-2.
    1. Pollock A, Farmer SE, Brady MC, Langhorne P, Mead GE, Mehrholz J, van Wijck F. Interventions for improving upper limb function after stroke. Cochrane Database Syst Rev. 2014;2014(11):010820.

Source: PubMed

Sponsorit ja yhteistyökumppanit

Sairaudet

Huumeiden interventiot

3
Tilaa