Effects of robotic gait training after stroke: A meta-analysis

Moucheboeuf, Geoffroy; Griffier, Romain; Gasq, David; Glize, Bertrand; Bouyer, Laurent J.; Dehail, Patrick; Cassoudesalle, Hélène

doi:10.1016/j.rehab.2020.02.008

Cited by 84 publications

(70 citation statements)

References 27 publications

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“…It has been suggested that the effect of RAGT depends on factors such as time after stroke and impairment severity [12]. Indeed, RAGT combined with physiotherapy has been suggested to be especially efficient in improving the function and mobility of the lower limbs in nonambulatory patients in their subacute phase post-stroke [11,13]. It has also been hypothesized that gait function and movement pattern is less likely to change in a chronic phase post-stroke.…”

Section: Methodological Considerationsmentioning

confidence: 99%

“…The devices could be either end-effectors or exoskeletons for treadmill gait training or exoskeletons used for overground gait training [3]. Contemporary evidence and recommendations suggest that RAGT should complement, not replace, existing gait rehabilitation and non-robotic physical therapies [3,13]. We therefore also included studies using a combination of RAGT and other therapies such as conventional physiotherapy training or functional electrical stimulation (FES).…”

Section: Type Of Intervention and Comparator Groupsmentioning

confidence: 99%

“…Earlier reviews revealed that RAGT, together with conventional physiotherapy, might have a slightly better or similar positive effect on gait speed and ambulation when compared with conventional gait training alone [6][7][8][9][10][11][12][13][14][15][16]. However, the need for a broadened perspective in the evaluation of gait ability after RAGT post-stroke has been highlighted [13,15,17,18]. The International Classification of Functioning, Disability and Health (ICF), advocated by the World Health Organization, is a classification system widely used in clinical practice [19].…”

Section: Introductionmentioning

confidence: 99%

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Effect of robotic-assisted gait training on objective biomechanical measures of gait in persons post-stroke: a systematic review and meta-analysis

Nedergård

Arumugam

Sandlund

et al. 2021

J NeuroEngineering Rehabil

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Background Robotic-Assisted Gait Training (RAGT) may enable high-intensive and task-specific gait training post-stroke. The effect of RAGT on gait movement patterns has however not been comprehensively reviewed. The purpose of this review was to summarize the evidence for potentially superior effects of RAGT on biomechanical measures of gait post-stroke when compared with non-robotic gait training alone. Methods Nine databases were searched using database-specific search terms from their inception until January 2021. We included randomized controlled trials investigating the effects of RAGT (e.g., using exoskeletons or end-effectors) on spatiotemporal, kinematic and kinetic parameters among adults suffering from any stage of stroke. Screening, data extraction and judgement of risk of bias (using the Cochrane Risk of bias 2 tool) were performed by 2–3 independent reviewers. The Grading of Recommendations Assessment Development and Evaluation (GRADE) criteria were used to evaluate the certainty of evidence for the biomechanical gait measures of interest. Results Thirteen studies including a total of 412 individuals (mean age: 52–69 years; 264 males) met eligibility criteria and were included. RAGT was employed either as monotherapy or in combination with other therapies in a subacute or chronic phase post-stroke. The included studies showed a high risk of bias (n = 6), some concerns (n = 6) or a low risk of bias (n = 1). Meta-analyses using a random-effects model for gait speed, cadence, step length (non-affected side) and spatial asymmetry revealed no significant differences between the RAGT and comparator groups, while stride length (mean difference [MD] 2.86 cm), step length (affected side; MD 2.67 cm) and temporal asymmetry calculated in ratio-values (MD 0.09) improved slightly more in the RAGT groups. There were serious weaknesses with almost all GRADE domains (risk of bias, consistency, directness, or precision of the findings) for the included outcome measures (spatiotemporal and kinematic gait parameters). Kinetic parameters were not reported at all. Conclusion There were few relevant studies and the review synthesis revealed a very low certainty in current evidence for employing RAGT to improve gait biomechanics post-stroke. Further high-quality, robust clinical trials on RAGT that complement clinical data with biomechanical data are thus warranted to disentangle the potential effects of such interventions on gait biomechanics post-stroke.

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Section: Methodological Considerationsmentioning

confidence: 99%

Section: Type Of Intervention and Comparator Groupsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Effect of robotic-assisted gait training on objective biomechanical measures of gait in persons post-stroke: a systematic review and meta-analysis

Nedergård

Arumugam

Sandlund

et al. 2021

J NeuroEngineering Rehabil

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“…Robotic-assisted exoskeleton and treadmill training systems have been used in clinical rehabilitation for patients with neurological disorders. Exoskeleton ambulation systems can in the lower limb movements during walking training for rehabilitation for improvement and better outcomes including rehabilitation of spinal cord injuries (SCI) (Ungerleider et al, 2002) and stroke (Moucheboeuf et al, 2020). Volitional-controlled assistive rehabilitation devices has been reported to yield better functional recovery among stroke patients (Bundy et al, 2017).…”

Section: Clinical Relevancementioning

confidence: 99%

Processing Surface EMG Signals for Exoskeleton Motion Control

et al. 2020

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The surface electromyography (sEMG) signal has been used for volitional control of robotic assistive devices. There are still challenges in improving system performance accuracy and signal processing to remove systematic noise. This study presents procedures and a pilot validation of the EMG-driven speed-control of exoskeleton and integrated treadmill with a goal to provide better interaction between a user and the system. The gait cycle duration (GCD) was extracted from sEMG signals using the autocorrelation algorithm and Bayesian fusion algorithm. GCDs of various walking speeds were then programmed to control the motion speed of exoskeleton robotic system. The performance and efficiency of this sEMG-controlled robotic assistive ambulation system was tested and validated among 6 healthy volunteers. The results demonstrated that the autocorrelation algorithm extracted the GCD from individual muscle contraction. The GCDs of individual muscles had variability between different walking steps under a designated walking speed. Bayesian fusion algorithms processed the GCDs of multiple muscles yielding a final GCD with the least variance. The fused GCD effectively controlled the motion speeds of exoskeleton and treadmill. The higher amplitude of EMG signals with shorter GCD was found during a faster walking speed. The algorithms using fused GCDs and gait stride length yielded trajectory joint motion tracks in a shape of sine curve waveform. The joint angles of the exoskeleton measured by a decoder mounted on the hip turned out to be in sine waveforms. The hip joint motion track of the exoskeleton matched the angles projected by trajectory curve generated by computer algorithms based on the fused GCDs with high agreement. The EMG-driven speed-control provided the human-machine inter-limb coordination mechanisms for an intuitive speed control of the exoskeleton-treadmill system at the user's intents. Potentially the whole system can be used for gait rehabilitation of incomplete spinal cord hemispheric stroke patients as goal-directed and task-oriented training tool.

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“…Несмотря на растущее число мобильных экзоскелетов в рутинной клинической практике чаще используют стационарные экзоскелетные устройства «Lokomat», «ReoGo» и др. На сегодняшний день для восстановления ходьбы после инсульта доказана клиническая эффективность как экзоскелетных, так и эндеффекторных типов устройств, и недостаточно данных о преимуществах воздействия на биомеханику движений какого-либо из них [17,18]. Исследования показывают, что при тренировках на системах эндеффекторного типа основные изменения происходят в силовых и кинетических характеристиках голеностопного сустава, а также объеме движений коленного сустава и ассоциированы с улучшениями степени мобильности, скорости и симметрии ходьбы [19,20].…”

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Vliyaniye robotizirovannykh trenirovok na biomekhaniku golenostopnogo sustava u patsiyentov s postinsul'tnym parezom

Клочков¹,

Зимин²,

Khizhnikova³

et al. 2020

Вестник РГМУ

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Klyuchevym faktorom narusheniy pokhodki posle ostrykh narusheniy mozgovogo krovoobrashcheniya (ONMK) yavlyayetsya narusheniye dvizheniy v golenostopnom sustave (GS), privodyashcheye k formirovaniyu patologicheskoy sinergii. V nastoyashcheye vremya pri robotizirovannykh trenirovkakh khod'by ispol'zuyut privody tazobedrennogo i kolennogo sustavov. Odnako po dannym literatury net yedinogo mneniya ob ikh vliyanii na dvizheniya v GS. Tsel'yu issledovaniya bylo izuchit' vliyaniye robotizirovannykh trenirovok khod'by na dvizheniya GS u patsiyentov s postinsul'tnym parezom. V issledovanii prinyalo uchastiye 22 patsiyenta s ONMK polusharnoy lokalizatsii. Dlya otsenki dvigatel'noy funktsii primenyali klinicheskiye shkaly i videoanaliz khod'by. Vsem patsiyentam provodili kurs iz 11 robotizirovannykh trenirovok khod'by. Na fone trenirovok vyyavleno uvelicheniye obshchego balla po shkale Fugl-Mayyera so 146,5 do 152 ballov (p < 0,05) i otdel'no dlya nizhney konechnosti s 18 do 20,5 ballov (p < 0,05), a takzhe snizheniye myshechnogo tonusa razgibateley GS s 2,5 do 2,0 ballov po modifitsirovannoy shkale Eshvorta (p < 0,05). Na fone robotizirovannykh trenirovok otmecheno uvelicheniye dlitel'nosti fazy opory patsiyentov s 28,0 do 33,5% ot tsikla shaga (TSSH). Krome togo, osnovnym razlichiyem struktury TSSH do i posle kursa trenirovok stalo nalichiye trekh chastey shaga vmesto pyati, chto podtverzhdayet konsolidatsiyu goniogramm patsiyentov v periode ot 1 do 61% TSSH. Sravneniye znacheniy uglov do i posle pokazalo dostovernyye razlichiya tol'ko po mezhkvartil'nomu intervalu (r < 0,05). Avtory prishli k vyvodu, chto robotizirovannyye trenirovki s primeneniyem aktivnykh privodov dlya tazobedrennogo i kolennogo sustavov oposredovanno sposobstvuyut izmeneniyam kinematicheskikh parametrov GS za schet priblizheniya pokazateley patterna k nekoyemu usrednennomu shablonu dvizheniy.

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Effects of robotic gait training after stroke: A meta-analysis

Cited by 84 publications

References 27 publications

Effect of robotic-assisted gait training on objective biomechanical measures of gait in persons post-stroke: a systematic review and meta-analysis

Effect of robotic-assisted gait training on objective biomechanical measures of gait in persons post-stroke: a systematic review and meta-analysis

Processing Surface EMG Signals for Exoskeleton Motion Control

Vliyaniye robotizirovannykh trenirovok na biomekhaniku golenostopnogo sustava u patsiyentov s postinsul'tnym parezom

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