Soft robotic exosuit augmented high intensity gait training on stroke survivors: a pilot study

Sung Yul Shin, Kristen Hohl, Matt Giffhorn, Louis N Awad, Conor J Walsh, Arun Jayaraman, Sung Yul Shin, Kristen Hohl, Matt Giffhorn, Louis N Awad, Conor J Walsh, Arun Jayaraman

Abstract

Background: Stroke is a leading cause of serious gait impairments and restoring walking ability is a major goal of physical therapy interventions. Soft robotic exosuits are portable, lightweight, and unobtrusive assistive devices designed to improve the mobility of post-stroke individuals through facilitation of more natural paretic limb function during walking training. However, it is unknown whether long-term gait training using soft robotic exosuits will clinically impact gait function and quality of movement post-stroke.

Objective: The objective of this pilot study was to examine the therapeutic effects of soft robotic exosuit-augmented gait training on clinical and biomechanical gait outcomes in chronic post-stroke individuals.

Methods: Five post-stroke individuals received high intensity gait training augmented with a soft robotic exosuit, delivered in 18 sessions over 6-8 weeks. Performance based clinical outcomes and biomechanical gait quality parameters were measured at baseline, midpoint, and completion.

Results: Clinically meaningful improvements were observed in walking speed ([Formula: see text] < 0.05) and endurance ([Formula: see text] < 0.01) together with other traditional gait related outcomes. The gait quality measures including hip ([Formula: see text] < 0.01) and knee ([Formula: see text] < 0.05) flexion/extension exhibited an increase in range of motion in a symmetric manner ([Formula: see text] < 0.05). We also observed an increase in bilateral ankle angular velocities ([Formula: see text] < 0.05), suggesting biomechanical improvements in walking function.

Conclusions: The results in this study offer preliminary evidence that a soft robotic exosuit can be a useful tool to augment high intensity gait training in a clinical setting. This study justifies more expanded research on soft exosuit technology with a larger post-stroke population for more reliable generalization. Trial registration This study is registered with ClinicalTrials.gov (ID: NCT04251091).

Keywords: Clinical outcomes; Exosuit; Gait quality; High intensity gait training; Soft robotics; Stroke.

Conflict of interest statement

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

© 2022. The Author(s).

Figures

Fig. 1
Fig. 1
A The ReWalk ReStore™ (ReWalk Robotics, Israel) soft robotic exosuit designed to assist paretic ankle dorsiflexion and plantarflexion of individuals with post-stroke. B Workflow of intervention, including assessment and training of high intensity gait training augmented with soft robotic exosuit. Gait training sessions consist of initial 10MWT to determine baseline self-selected speed followed by five sets of 6-min walking on treadmill or overground. Pre: before training; Mid: after 9 training sessions; Post: after 18 training sessions; 10MWT: 10-Meter Walk Test
Fig. 2
Fig. 2
Clinical outcome measures before training (Pre), after 9 training sessions (Mid), and after 18 training sessions (Post) time points. 10MWT SSV: 10-Meter Walk Test for self-selected walking velocity; 10MWT FV: 10-Meter Walk Test for fast walking velocity; FGA: functional gait assessment; TUG: Timed-Up-and-Go; 6MWT: 6-min walk test; LE Motor FM: lower extremity subscale of the Fugl-Meyer Assessment; 2MWT OG: 2-min walk test overground, 2MWT TM: 2-min walk test treadmill
Fig. 3
Fig. 3
A Selected gait quality measures with significance before training (Pre), after 9 training sessions (Mid), and after 18 training sessions (Post) time points. B Example of ankle angular velocity profiles across a single gait cycle from a representative subject (top panel) and changes in ankle angular velocity at Pre, Mid and Post time points (bottom panel). US unaffected side, AS affected side, FE flexion/extension, AAV ankle angular velocity

References

    1. Virani SS, et al. Heart disease and stroke statistics—2020 update: a report from the American Heart Association. Circulation. 2020;141:e139–e596.
    1. Olney SJ, Richards C. Hemiparetic gait following stroke. Part I: characteristics. Gait Posture. 1996;4:136–148. doi: 10.1016/0966-6362(96)01063-6.
    1. Bohannon RW, Andrews AW, Smith MB. Rehabilitation goals of patients with hemiplegia. Int J Rehabil Res. 1988;11:181–184. doi: 10.1097/00004356-198806000-00012.
    1. Moore JL, et al. Implementation of high-intensity stepping training during inpatient stroke rehabilitation improves functional outcomes. Stroke. 2020;51:563–570. doi: 10.1161/STROKEAHA.119.027450.
    1. Holleran CL, Straube DD, Kinnaird CR, Leddy AL, Hornby TG. Feasibility and potential efficacy of high-intensity stepping training in variable contexts in subacute and chronic stroke. Neurorehabil Neural Repair. 2014;28:643–651. doi: 10.1177/1545968314521001.
    1. Shin SY, Lee RK, Spicer P, Sulzer J. Does kinematic gait quality improve with functional gait recovery? A longitudinal pilot study on early post-stroke individuals. J Biomech. 2020;105:109761. doi: 10.1016/j.jbiomech.2020.109761.
    1. Levin MF, Kleim JA, Wolf SL. What do motor “recovery” and “compensation” mean in patients following stroke? Neurorehabil Neural Repair. 2009;23:313–319. doi: 10.1177/1545968308328727.
    1. Patterson KK, et al. Gait asymmetry in community-ambulating stroke survivors. Arch Phys Med Rehabil. 2008;89:304–310. doi: 10.1016/j.apmr.2007.08.142.
    1. Finley JM, Bastian AJ, Gottschall JS. Learning to be economical: the energy cost of walking tracks motor adaptation. J Physiol. 2013;591:1081–1095. doi: 10.1113/jphysiol.2012.245506.
    1. Weerdesteijn VGM, Niet Md, Van Duijnhoven HJ, Geurts AC. Falls in individuals with stroke. 2008.
    1. Sridar S, Qiao Z, Muthukrishnan N, Zhang W, Polygerinos P. A soft-inflatable exosuit for knee rehabilitation: assisting swing phase during walking. Front Robot AI. 2018;5:44. doi: 10.3389/frobt.2018.00044.
    1. Bae J, et al. Biomechanical mechanisms underlying exosuit-induced improvements in walking economy after stroke. J Exp Biol. 2018;221:jeb168815. doi: 10.1242/jeb.168815.
    1. Awad LN, et al. A soft robotic exosuit improves walking in patients after stroke. Sci Transl Med. 2017;9:eaai9084. doi: 10.1126/scitranslmed.aai9084.
    1. Awad LN, et al. Reducing circumduction and hip hiking during hemiparetic walking through targeted assistance of the paretic limb using a soft wearable robot. Am J Phys Med Rehabil. 2017;96:S157. doi: 10.1097/PHM.0000000000000800.
    1. Awad LN, Kudzia P, Revi DA, Ellis TD, Walsh CJ. Walking faster and farther with a soft robotic exosuit: implications for post-stroke gait assistance and rehabilitation. IEEE Open J Eng Med Biol. 2020;1:108–115. doi: 10.1109/OJEMB.2020.2984429.
    1. Porciuncula F, et al. Targeting paretic propulsion and walking speed with a soft robotic exosuit: a consideration-of-concept trial. Front Neurorobot. 2021;15:689577.
    1. Awad LN, Esquenazi A, Francisco GE, Nolan KJ, Jayaraman A. The ReWalk ReStore™ soft robotic exosuit: a multi-site clinical trial of the safety, reliability, and feasibility of exosuit-augmented post-stroke gait rehabilitation. J Neuroeng Rehabil. 2020;17:1–11. doi: 10.1186/s12984-019-0634-5.
    1. Handlery R, et al. Stepping after stroke: walking characteristics in people with chronic stroke differ on the basis of walking speed, walking endurance, and daily steps. Phys Ther. 2020;100:807–817. doi: 10.1093/ptj/pzaa020.
    1. Catapani LB, Dos Santos TP, Toffano GC, Souza HCD, de Araujo JE. Aerobic exercise after left-sided stroke improves gait speed and endurance: a prospective cohort study. Am J Phys Med Rehabil. 2021;100:576–583. doi: 10.1097/PHM.0000000000001596.
    1. Adams HP, et al. Baseline NIH Stroke Scale score strongly predicts outcome after stroke: a report of the Trial of Org 10172 in Acute Stroke Treatment (TOAST) Neurology. 1999;53:126–126. doi: 10.1212/WNL.53.1.126.
    1. Bae J et al. A lightweight and efficient portable soft exosuit for paretic ankle assistance in walking after stroke. In: 2018 IEEE international conference on robotics and automation (ICRA) 2820–2827 (IEEE, 2018).
    1. Hornby TG, et al. Clinical practice guideline to improve locomotor function following chronic stroke, incomplete spinal cord injury, and brain injury. J Neurol Phys Ther. 2020;44:49–100. doi: 10.1097/NPT.0000000000000303.
    1. Abdi H, Williams LJ. Tukey’s honestly significant difference (HSD) test. Encycl Res Des. 2010;3:1–5.
    1. Perera S, Mody SH, Woodman RC, Studenski SA. Meaningful change and responsiveness in common physical performance measures in older adults. J Am Geriatr Soc. 2006;54:743–749. doi: 10.1111/j.1532-5415.2006.00701.x.
    1. Tang A, Eng J, Rand D. Relationship between perceived and measured changes in walking after stroke. J Neurol Phys Ther. 2012;36:115. doi: 10.1097/NPT.0b013e318262dbd0.
    1. Bushnell C, et al. Chronic stroke outcome measures for motor function intervention trials: expert panel recommendations. Circ Cardiovasc Qual Outcomes. 2015;8:S163–S169. doi: 10.1161/CIRCOUTCOMES.115.002098.
    1. Lin J-H, Hsu M-J, Hsu H-W, Wu H-C, Hsieh C-L. Psychometric comparisons of 3 functional ambulation measures for patients with stroke. Stroke. 2010;41:2021–2025. doi: 10.1161/STROKEAHA.110.589739.
    1. Flansbjer U-B, Holmbäck AM, Downham D, Patten C, Lexell J. Reliability of gait performance tests in men and women with hemiparesis after stroke. J Rehabil Med. 2005;37:75–82. doi: 10.1080/16501970410017215.
    1. Jayaraman A, et al. Stride management assist exoskeleton vs functional gait training in stroke: a randomized trial. Neurology. 2019;92:e263–e273. doi: 10.1212/WNL.0000000000006782.
    1. Peterson CL, Cheng J, Kautz SA, Neptune RR. Leg extension is an important predictor of paretic leg propulsion in hemiparetic walking. Gait Posture. 2010;32:451–456. doi: 10.1016/j.gaitpost.2010.06.014.
    1. Mentiplay BF, Banky M, Clark RA, Kahn MB, Williams G. Lower limb angular velocity during walking at various speeds. Gait Posture. 2018;65:190–196. doi: 10.1016/j.gaitpost.2018.06.162.
    1. Schmidt RA, Young DE, Swinnen S, Shapiro DC. Summary knowledge of results for skill acquisition: support for the guidance hypothesis. J Exp Psychol Learn Mem Cogn. 1989;15:352. doi: 10.1037/0278-7393.15.2.352.
    1. Winstein CJ, Pohl PS, Lewthwaite R. Effects of physical guidance and knowledge of results on motor learning: support for the guidance hypothesis. Res Q Exerc Sport. 1994;65:316–323. doi: 10.1080/02701367.1994.10607635.
    1. Schmid A, et al. Improvements in speed-based gait classifications are meaningful. Stroke. 2007;38:2096–2100. doi: 10.1161/STROKEAHA.106.475921.
    1. Ardestani MM, Henderson CE, Mahtani G, Connolly M, Hornby TG. Locomotor kinematics and kinetics following high-intensity stepping training in variable contexts poststroke. Neurorehabil Neural Repair. 2020;34:652–660. doi: 10.1177/1545968320929675.
    1. Mahtani GB, et al. Altered sagittal-and frontal-plane kinematics following high-intensity stepping training versus conventional interventions in subacute stroke. Phys Ther. 2017;97:320–329. doi: 10.2522/ptj.20160281.
    1. De Vries WHK, Veeger HEJ, Baten CTM, Van Der Helm FCT. Magnetic distortion in motion labs, implications for validating inertial magnetic sensors. Gait Posture. 2009;29:535–541. doi: 10.1016/j.gaitpost.2008.12.004.
    1. Lee J, Shin SY, Ghorpade G, Akbas T, Sulzer J. Sensitivity comparison of inertial to optical motion capture during gait: implications for tracking recovery. In: 2019 IEEE 16th international conference on rehabilitation robotics (ICORR), pp 139–144 (IEEE, 2019).
    1. Zhang J-T, Novak AC, Brouwer B, Li Q. Concurrent validation of Xsens MVN measurement of lower limb joint angular kinematics. Physiol Meas. 2013;34:N63. doi: 10.1088/0967-3334/34/8/N63.
    1. Paulich M, Schepers M, Rudigkeit N, Bellusci G. Xsens MTw Awinda: miniature wireless inertial-magnetic motion tracker for highly accurate 3D kinematic applications. Xsens Enschede Neth. 2018:1–9.
    1. Shin SY, Kim Y, Jayaraman A, Park H-S. Relationship between gait quality measures and modular neuromuscular control parameters in chronic post-stroke individuals. J Neuroeng Rehabil. 2021;18:1–12. doi: 10.1186/s12984-021-00860-0.

Source: PubMed

3
Subskrybuj