Barriers to sEMG Assessment During Overground Robot-Assisted Gait Training in Subacute Stroke Patients

Michela Goffredo, Francesco Infarinato, Sanaz Pournajaf, Paola Romano, Marco Ottaviani, Leonardo Pellicciari, Daniele Galafate, Debora Gabbani, Annalisa Gison, Marco Franceschini, Michela Goffredo, Francesco Infarinato, Sanaz Pournajaf, Paola Romano, Marco Ottaviani, Leonardo Pellicciari, Daniele Galafate, Debora Gabbani, Annalisa Gison, Marco Franceschini

Abstract

Background: The limitation to the use of ElectroMyoGraphy (sEMG) in rehabilitation services is in contrast with its potential diagnostic capacity for rational planning and monitoring of the rehabilitation treatments, especially the overground Robot-Assisted Gait Training (o-RAGT). Objective: To assess the barriers to the implementation of a sEMG-based assessment protocol in a clinical context for evaluating the effects of o-RAGT in subacute stroke patients. Methods: An observational study was conducted in a rehabilitation hospital. The primary outcome was the success rate of the implementation of the sEMG-based assessment. The number of dropouts and the motivations have been registered. A detailed report on difficulties in implementing the sEMG protocol has been edited for each patient. The educational level and the working status of the staff have been registered. Each member of staff completed a brief survey indicating their level of knowledge of sEMG, using a five-point Likert scale. Results: The sEMG protocol was carried out by a multidisciplinary team composed of Physical Therapists (PTs) and Biomedical Engineers (BEs). Indeed, the educational level and the expertise of the members of staff influenced the fulfillment of the implementation of the study. The PTs involved in the study did not receive any formal education on sEMG during their course of study. The low success rate (22.7%) of the protocol was caused by several factors which could be grouped in: patient-related barriers; cultural barriers; technical barriers; and administrative barriers. Conclusions: Since a series of barriers limited the use of sEMG in the clinical rehabilitative environment, concrete actions are needed for disseminating sEMG in rehabilitation services. The sEMG assessment should be included in health systems regulations and specific education should be part of the rehabilitation professionals' curriculum. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT03395717.

Keywords: clinical applications; overground robot-assisted gait rehabilitation; sEMG barriers; stroke; surface electromyography.

Copyright © 2020 Goffredo, Infarinato, Pournajaf, Romano, Ottaviani, Pellicciari, Galafate, Gabbani, Gison and Franceschini.

Figures

Figure 1
Figure 1
Flowchart of the experimental procedure.
Figure 2
Figure 2
sEMG activation of the affected and the unaffected limb for all patients (N = 8), depicted as mean and standard deviation plot, during ecological overground gait. The red line (mean) and the red band (standard deviation) represent the sEMG envelopes (normalized with respect to the maximum sEMG amplitude level of each side) before o-RAGT (T1). The blue line (mean) and the blue band (standard deviation) represent the sEMG envelopes at the end of o-RAGT (T2). For each subject, five gait cycles have been considered. Shaded rectangular areas indicate when a muscle is active based on normative healthy adult gait, Perry and Burnfield (36).

References

    1. Hogrel J-Y. Clinical applications of surface electromyography in neuromuscular disorders. Clin. Neurophysiol. (2005) 35:59–71. 10.1016/j.neucli.2005.03.001
    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) 10:CD006185 10.1002/14651858.CD006185.pub4
    1. Yan T, Cempini M, Oddo CM, Vitiello N. Review of assistive strategies in powered lower-limb orthoses and exoskeletons. Rob. Auton. Syst. (2015) 64:120–36. 10.1016/j.robot.2014.09.032
    1. Barbeau H. Locomotor training in neurorehabilitation: emerging rehabilitation concepts. Neurorehabil. Neural Repair. (2003) 17:3–11. 10.1177/0888439002250442
    1. Morone G, Paolucci S, Cherubini A, De Angelis D, Venturiero V, Coiro P, et al. . Robot-assisted gait training for stroke patients: current state of the art and perspectives of robotics. Neuropsychiatr. Dis. Treat. (2017) 13:1303–11. 10.2147/NDT.S114102
    1. Masiero S, Poli P, Rosati G, Zanotto D, Iosa M, Paolucci S, et al. . The value of robotic systems in stroke rehabilitation. Expert Rev. Med. Devices. (2014) 11:187–98. 10.1586/17434440.2014.882766
    1. Chen G, Chan CK, Guo Z, Yu H. A review of lower extremity assistive robotic exoskeletons in rehabilitation therapy. Crit. Rev. Biomed. Eng. (2013) 41:343–63. 10.1615/CritRevBiomedEng.2014010453
    1. Louie DR, Eng JJ. Powered robotic exoskeletons in post-stroke rehabilitation of gait: a scoping review. J. Neuroengineering Rehabil. (2016) 13:53. 10.1186/s12984-016-0162-5
    1. Molteni F, Gasperini G, Cannaviello G, Guanziroli E. Exoskeleton and end-effector robots for upper and lower limbs rehabilitation: narrative review. PM&R. (2018) 10:S174–88. 10.1016/j.pmrj.2018.06.005
    1. Tedla JS, Dixit S, Gular K, Abohashrh M. Robotic-assisted gait training effect on function and gait speed in subacute and chronic stroke population: a systematic review and meta-analysis of randomized controlled trials. Eur. Neurol. (2019) 81:1–9. 10.1159/000500747
    1. Peters S, Handy TC, Lakhani B, Boyd LA, Garland SJ. Motor and visuospatial attention and motor planning after stroke: considerations for the rehabilitation of standing balance and gait. Phys. Ther. (2015) 95:1423–32. 10.2522/ptj.20140492
    1. Buesing C, Fisch G, O'Donnell M, Shahidi I, Thomas L, Mummidisetty CK, et al. Effects of a wearable exoskeleton stride management assist system (SMA®) on spatiotemporal gait characteristics in individuals after stroke: a randomized controlled trial. J. NeuroEngineering Rehabil. (2015) 20:12–69. 10.1186/s12984-015-0062-0
    1. Li L, Ding L, Chen N, Mao Y, Huang D, Li L. Improved walking ability with wearable robot-assisted training in patients suffering chronic stroke. Biomed. Mater. Eng. (2015) 26:S329–40. 10.3233/BME-151320
    1. Gasperini G, Gaffuri M, Guanziroli E, Goffredo M, Pournajaf S, Galafate D, et al. Recovery of gait function with a wearable powered exoskeleton in sub-acute stroke patients using SEMG for fine tuning: preliminary results. Ann. Phys. Rehabil. Med. (2018) 61:e93 10.1016/j.rehab.2018.05.198
    1. Goffredo M, Guanziroli E, Pournajaf S, Gaffuri M, Gasperini G, Filoni S, et al. . Overground wearable powered exoskeleton for gait training in subacute stroke subjects: clinical and gait assessments. Eur. J. Phys. Rehabil. Med. (2019) 55:710–21. 10.23736/S1973-9087.19.05574-6
    1. Goffredo M, Iacovelli C, Russo E, Pournajaf S, Di Blasi C, Galafate D, et al. Stroke gait rehabilitation: a comparison of end-effector, overground exoskeleton, and conventional gait training. Appl. Sci. (2019) 9:2627 10.3390/app9132627
    1. Pournajaf S, Goffredo M, Agosti M, Massucci M, Ferro S, Franceschini M. Community ambulation of stroke survivors at 6 months follow-up: an observational study on sociodemographic and sub-acute clinical indicators. Eur. J. Phys. Rehabil. Med. (2019) 55:433–41. 10.23736/S1973-9087.18.05489-8
    1. Lee HJ, Lee SH, Seo K, Lee M, Chang WH, Choi BO, et al. . Training for walking efficiency with a wearable hip-assist robot in patients with stroke: a pilot randomized controlled trial. Stroke. (2019) 50:3545–52. 10.1161/STROKEAHA.119.025950
    1. Sylos-Labini F, La Scaleia V, d'Avella A, Pisotta I, Tamburella F, Scivoletto G, et al. . EMG patterns during assisted walking in the exoskeleton. Front. Hum. Neurosci. (2014) 8:423. 10.3389/fnhum.2014.00423
    1. Androwis GJ, Pilkar R, Ramanujam A, Nolan KJ. Electromyography assessment during gait in a robotic exoskeleton for acute stroke. Front. Neurol. (2018) 9:630. 10.3389/fneur.2018.00630
    1. Swank C, Sikka S, Driver S, Bennett M, Callender L. Feasibility of integrating robotic exoskeleton gait training in inpatient rehabilitation. Disabil. Rehabil. (2020) 15:409–17. 10.1080/17483107.2019.1587014
    1. Calabrò RS, Naro A, Russo M, Bramanti P, Carioti L, Balletta T, et al. . Shaping neuroplasticity by using powered exoskeletons in patients with stroke: a randomized clinical trial. J. NeuroEngineering Rehabil. (2018) 15:35. 10.1186/s12984-018-0377-8
    1. Tan CK, Kadone H, Watanabe H, Marushima A, Yamazaki M, Sankai Y, et al. . Lateral symmetry of synergies in lower limb muscles of acute post-stroke patients after robotic intervention. Front. Neurosci. (2018) 12:276. 10.3389/fnins.2018.00276
    1. Sutherland DH. The evolution of clinical gait analysis part l: kinesiological EMG. Gait Posture. (2001) 14:61–70. 10.1016/S0966-6362(01)00100-X
    1. Zwarts MJ, Stegeman DF. Multichannel surface EMG: basic aspects and clinical utility. Muscle Nerve. (2003) 28:1–17. 10.1002/mus.10358
    1. World Health Organization Neurological Disorders: Public Health Challenges. Geneva: World Health Organization; (2006).
    1. Frigo C, Crenna P. Multichannel SEMG in clinical gait analysis: a review and state-of-the-art. Clin. Biomech. (2009) 24:236–45. 10.1016/j.clinbiomech.2008.07.012
    1. Feldner HA, Howell D, Kelly VE, McCoy SW, Steele KM. “Look, your muscles are firing!”: a qualitative study of clinician perspectives on the use of surface electromyography in neurorehabilitation. Arch. Phys. Med. Rehabil. (2019) 100:663–75. 10.1016/j.apmr.2018.09.120
    1. Swinkels RAHM, Van Peppen RPPS, Wittink H, Custers JWH, Beurskens AJHM. Current use and barriers and facilitators for implementation of standardised measures in physical therapy in the Netherlands. BMC Musculoskelet Disord. (2011) 12:106. 10.1186/1471-2474-12-106
    1. Braun T, Rieckmann A, Weber F, Grüneberg C. Current use of measurement instruments by physiotherapists working in Germany: a cross-sectional online survey. BMC Health Serv. Res. (2018) 18:1–16. 10.1186/s12913-018-3563-2
    1. Hermens HJ, Freriks B, Merletti R, Stegeman D, Blok J, Rau G, et al. . European recommendations for surface electromyography. Roessingh Res. Dev. (1999) 8:13–54.
    1. Paoloni M, Mangone M, Scettri P, Procaccianti R, Cometa A, Santilli V. Segmental muscle vibration improves walking in chronic stroke patients with foot drop: a randomized controlled trial. Neurorehabil. Neural Repair. (2010) 24:254–62. 10.1177/1545968309349940
    1. Wren TA, Do KP, Rethlefsen SA, Healy B. Cross-correlation as a method for comparing dynamic electromyography signals during gait. J. Biomech. (2006) 39:2714–18. 10.1016/j.jbiomech.2005.09.006
    1. Di Nardo F, Mengarelli A, Burattini L, Maranesi E, Agostini V, et al. . Normative EMG patterns of ankle muscle co-contractions in school-age children during gait. Gait Posture. (2016) 46:161–66. 10.1016/j.gaitpost.2016.03.002
    1. Bailey CA, Corona F, Pilloni G, Porta M, Fastame MC, Hitchcott PK, et al. . Sex-dependent and sex-independent muscle activation patterns in adult gait as a function of age. Exp. Gerontol. (2018) 110:1–8. 10.1016/j.exger.2018.05.005
    1. Perry J, Burnfield JM. Gait Analysis: Normal and Pathological Function. 2nd ed Thorofare, NJ: Slack Inc; (2010).
    1. Fiori L, Ranavolo A, Varrecchia T, Tatarelli A, Conte C, Draicchio F, et al. . Impairment of global lower limb muscle coactivation during walking in cerebellar ataxias. Cerebellum. (2020) 19:583–96. 10.1007/s12311-020-01142-6
    1. Tatarelli A, Serrao M, Varrecchia T, Fiori L, Draicchio F, Silvetti A, et al. . A. global muscle coactivation of the sound limb in gait of people with transfemoral and transtibial amputation. Sensors. (2020) 20:2543. 10.3390/s20092543
    1. Varrecchia T, Rinaldi M, Serrao M, Draicchio F, Conte C, Conforto S, et al. . Global lower limb muscle coactivation during walking at different speeds: relationship between spatio-temporal, kinematic, kinetic, and energetic parameters. J. Electromyogr. kinesiol. (2018) 43:148–57. 10.1016/j.jelekin.2018.09.012
    1. Campanini I, Disselhorst-Klug C, Rymer WZ, Merletti R. Surface EMG in clinical assessment and neurorehabilitation: barriers limiting its use. Front. Neurol. (2020) 11:934. 10.3389/fneur.2020.00934
    1. Campanini I, Merlo A, Degola P, Merletti R, Vezzosi G, Farina D. Effect of electrode location on EMG signal envelope in leg muscles during gait. J. Electromyogr. Kinesiol. (2007) 17:515–26. 10.1016/j.jelekin.2006.06.001
    1. Mesin L, Merletti R, Rainoldi A. Surface EMG: the issue of electrode location. J. Electromyogr. Kinesiol. (2009) 19:719–26. 10.1016/j.jelekin.2008.07.006
    1. Besomi M, Hodges PW, Van Dieën J, Carson RG, Clancy EA, Disselhorst-Klug C, et al. . Consensus for experimental design in electromyography (CEDE) project: electrode selection matrix. J. Electromyogr. Kinesiol. (2019) 48:128–44. 10.1016/j.jelekin.2019.07.008
    1. Merletti R, Muceli S. Tutorial. Surface EMG detection in space and time: best practices. J. Electromyogr. Kinesiol. (2019) 49:102363. 10.1016/j.jelekin.2019.102363
    1. Groenier M, Pieters JM, Miedema HA. Technical medicine: designing medical technological solutions for improved health care. Med. Sci. Educator. (2017) 27:621–31. 10.1007/s40670-017-0443-z
    1. De la Fuente C, Machado ÁS, Kunzler MR, Carpes FP. Winter school on sEMG signal processing: an initiative to reduce educational gaps and to promote the engagement of physiotherapists and movement scientists with science. Front. Neurol. (2020) 11:509. 10.3389/fneur.2020.00509

Source: PubMed

Sponsoren und Mitarbeiter

Krankheiten

Drogeninterventionen

3
Abonnieren