Preliminary study on rehabilitation effects of the A3 robot-assisted gait training on patients with chronic stroke_West China Medical Journal

Authors：

CHENG Xue , ZHANG Tao , BAI Dingqun , PENG Xiaohua ,  YU Heping

Department of Rehabilitation Medicine, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, P. R. China;

Corresponding?author：

YU Heping, Email: lyshqiao@126.com

Keywords：

Chronic stroke; A3 lower limb rehabilitation robot; Robot-assisted gait training; Rehabilitation effect; Preliminary study

DOI：

10.7507/1002-0179.202003181

Video：

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Objective To explore the rehabilitation effect of a domestic lower limb rehabilitation robot on patients with chronic stroke.Methods Chronic stroke patients who were hospitalized in the Department of Rehabilitation Medicine, the First Affiliated Hospital of Chongqing Medical University from September 2017 to August 2019 were collected. These patients underwent A3 robot-assisted gait training for 6 weeks. The differences of gait parameters, spatiotemporal asymmetries, total score and score of each item of Barthel Index were analyzed before and after 6 weeks training.Results A total of 15 patients were included, and 12 patients finally completed the trial. After the training, the gait parameters of patients with chronic stroke were significantly improved. Comparing with the baseline data, the cadence, stride length, velocity, step length of the affected leg, and step length of the healthy leg significantly increased (P<0.05) after the training; the stride time and the double-support time were significantly shorter (P<0.05); the stance phase of the affected leg was shortened (P<0.05); the swing phase of the affected leg was prolonged (P<0.05); While no significant difference in the stance phase or swing phase of the healthy leg was found (P>0.05). The spatiotemporal asymmetries had no significant change compared with the baseline data, including the ratio of step length [(1.26±0.23) vs. (1.13±0.10); t=1.816, P=0.097] and the ratio of swing phase of both lower limbs [1.14 (0.23) vs. 1.10 (0.38); Z=?0.153, P=0.878]. The activities of daily living were improved after the training, and the total score of Barthel Index [(72.92± 13.05) vs. (85.42±14.38); t=?6.966, P<0.001] was significantly higher than that before the training. Among the items, the scores of bathing [0.00 (3.75) vs. 5.00 (5.00); Z=?2.000, P=0.046], walking on the flat ground [10.00 (3.75) vs. 15.00 (5.00); Z=?3.000, P=0.003], and going up and down stairs [5.00 (5.00) vs. 7.50 (5.00), Z=?3.000, P=0.003] were higher than the baseline data, and the differences were statistically significant.Conclusions A3 robot-assisted gait training can effectively improve the walking ability and activities of daily living of patients with chronic stroke but not the spatiotemporal asymmetries. Whether the spatiotemporal asymmetries can be improved by adjusting the robot equipment parameters needs to be further studied.

Citation： CHENG Xue, ZHANG Tao, BAI Dingqun, PENG Xiaohua, YU Heping. Preliminary study on rehabilitation effects of the A3 robot-assisted gait training on patients with chronic stroke. West China Medical Journal, 2020, 35(5): 579-584. doi: 10.7507/1002-0179.202003181 Copy

1.	Liu W. A narrative review of gait training after stroke anda proposal for developing a novel gait training device that provides minimal assistance. Top Stroke Rehabil, 2018, 25(5): 375-383.
2.	Zhang X, Elnady AM, Randhawa BK, et al. Combining mental training and physical training with goal-oriented protocols in stroke rehabilitation: a feasibility case study. Front Hum Neurosci, 2018, 12: 125.
3.	Taveggia G, Borboni A, Mulé C, et al. Conflicting results of robot-assisted versus usual gait training during postacute rehabilitation of stroke patients: a randomized clinical trial. Int J Rehabil Res, 2016, 39(1): 29-35.
4.	Duncan PW, Sullivan KJ, Behrman AL, et al. Body-weight-supported treadmill rehabilitation after stroke. N Engl J Med, 2011, 364(21): 2026-2036.
5.	Mustafaoglu R, Erhan B, Yeldan I, et al. Does robot-assisted gait training improve mobility, activities of daily living and quality of life in stroke? A single-blinded, randomized controlled trial. Acta Neurol Belg, 2020, 120(2): 335-344.
6.	王俊, 楊振輝, 劉海兵, 等. 下肢康復機器人在腦卒中患者步行障礙中的應用和研究進展. 中國康復醫學雜志, 2014, 29(8): 784-788.
7.	李冰, 卞立, 黃澎. A3 機器人對腦卒中患者步行功能的影響. 中國康復醫學雜志, 2014, 29(12): 1142-1145.
8.	林海丹, 張韜, 陳青, 等. 康復機器人輔助步行訓練對不完全性脊髓損傷患者步行能力的影響. 自動化學報, 2016, 42(12): 1832-1838.
9.	中華神經科學會, 中華神經外科學會. 各類腦血管疾病診斷要點. 中華神經科雜志, 1996, 29(6): 379-380.
10.	Buesing C, Fisch G, O′Donnell M, 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 Neuroeng Rehabil, 2015, 12: 69.
11.	張雅靜, 張小蘭, 馬延愛, 等. Barthel 指數量表應用于急性腦卒中患者生活能力測量的信度研究. 中國護理管理, 2007, 7(5): 30-32.
12.	Stinear CM. Prediction of motor recovery after stroke: advances in biomarkers. Lancet Neurol, 2017, 16(10): 826-836.
13.	勵建安, 孟殿懷. 步態分析的臨床應用. 中華物理醫學與康復雜志, 2006, 28(7): 500-503.
14.	Morone G, Paolucci S, Cherubini A, et al. Robot-assisted gait training for stroke patients: current state of the art and perspectives of robotics. Neuropsychiatr Dis Treat, 2017, 13: 1303-1311.
15.	王麗萍. 康復治療時機對腦卒中患者預后的影響. 河北醫學, 2014, 20(11): 1798-1800, 1801.
16.	van Kammen K, Boonstra AM, van der Woude LHV, et al. Differences in muscle activity and temporal step parameters between lokomat guided walking and treadmill walking in post-stroke hemiparetic patients and healthy walkers. J Neuroeng Rehabil, 2017, 14(1): 32.
17.	Calabrò RS, Naro A, Russo M, et al. Shaping neuroplasticity by using powered exoskeletons in patients with stroke: a randomized clinical trial. J Neuroeng Rehabil, 2018, 15(1): 35.
18.	Yoshimoto T, Shimizu I, Hiroi Y. Sustained effects of once-a-week gait training with hybrid assistive limb for rehabilitation in chronic stroke: case study. J Phys Ther Sci, 2016, 28(9): 2684-2687.
19.	Iosa M, Morone G, Bragoni M, et al. Driving electromechanically assisted gait trainer for people with stroke. J Rehabil Res Dev, 2011, 48(2): 135-146.
20.	Shi D, Zhang W, Ding X, et al. Parametric generation of three-dimensional gait for robot-assisted rehabilitation. Biol Open, 2020, 9(3): pii: bio047332.
21.	Lefeber N, De Buyzer S, Dassen N, et al. Energy consumption and cost during walking with different modalities of assistance after stroke: a systematic review and meta-analysis. Disabil Rehabil, 2019: 1-17.
22.	Erbil D, Tugba G, Murat TH, et al. Effects of robot-assisted gait training in chronic stroke patients treated by botulinum toxin-a: a pivotal study. Physiother Res Int, 2018, 23(3): e1718.
23.	Bustamante Valles K, Montes S, Madrigal Mde J, et al. Technology-assisted stroke rehabilitation in Mexico: a pilot randomized trial comparing traditional therapy to circuit training in a Robot/technology-assisted therapy gym. J Neuroeng Rehabil, 2016, 13(1): 83.
24.	Wernig A. “Ineffectiveness” of automated locomotor training. Arch Phys Med Rehabil, 2005, 86(12): 2385-236.
25.	Hebenstreit F, Leibold A, Krinner S, et al. Effect of walking speed on gait sub phase durations. Hum Mov Sci, 2015, 43: 118-124.
26.	張利. 不同步行速度的下肢康復機器人訓練對腦卒中患者步行能力的影響. 重慶: 重慶醫科大學, 2018.
27.	Yoshimoto T, Shimizu I, Hiroi Y, et al. Feasibility and efficacy of high-speed gait training with a voluntary driven exoskeleton robot for gait and balance dysfunction in patients with chronic stroke: nonrandomized pilot study with concurrent control. Int J Rehabil Res, 2015, 38(4): 338-343.
28.	Lam T, Anderschitz M, Dietz V. Contribution of feedback and feedforward strategies to locomotor adaptations. J Neurophysiol, 2006, 95(2): 766-773.
29.	Reisman DS, Wityk R, Silver K, et al. Locomotor adaptation ona split-belt treadmill can improve walking symmetry post-stroke. Brain, 2007, 130(Pt 7): 1861-1872.
30.	Ohura T, Hase K, Nakajima Y, et al. Validity and reliability ofa performance evaluation tool based on the modified Barthel Index for stroke patients. BMC Med Res Methodol, 2017, 17(1): 131.
31.	Nascimento LR, de Oliveira CQ, Ada L, et al. Walking training with cueing of cadence improves walking speed and stride length after stroke more than walking training alone: a systematic review. J Physiother, 2015, 61(1): 10-15.

1. Liu W. A narrative review of gait training after stroke anda proposal for developing a novel gait training device that provides minimal assistance. Top Stroke Rehabil, 2018, 25(5): 375-383.
2. Zhang X, Elnady AM, Randhawa BK, et al. Combining mental training and physical training with goal-oriented protocols in stroke rehabilitation: a feasibility case study. Front Hum Neurosci, 2018, 12: 125.
3. Taveggia G, Borboni A, Mulé C, et al. Conflicting results of robot-assisted versus usual gait training during postacute rehabilitation of stroke patients: a randomized clinical trial. Int J Rehabil Res, 2016, 39(1): 29-35.
4. Duncan PW, Sullivan KJ, Behrman AL, et al. Body-weight-supported treadmill rehabilitation after stroke. N Engl J Med, 2011, 364(21): 2026-2036.
5. Mustafaoglu R, Erhan B, Yeldan I, et al. Does robot-assisted gait training improve mobility, activities of daily living and quality of life in stroke? A single-blinded, randomized controlled trial. Acta Neurol Belg, 2020, 120(2): 335-344.
6. 王俊, 楊振輝, 劉海兵, 等. 下肢康復機器人在腦卒中患者步行障礙中的應用和研究進展. 中國康復醫學雜志, 2014, 29(8): 784-788.
7. 李冰, 卞立, 黃澎. A3 機器人對腦卒中患者步行功能的影響. 中國康復醫學雜志, 2014, 29(12): 1142-1145.
8. 林海丹, 張韜, 陳青, 等. 康復機器人輔助步行訓練對不完全性脊髓損傷患者步行能力的影響. 自動化學報, 2016, 42(12): 1832-1838.
9. 中華神經科學會, 中華神經外科學會. 各類腦血管疾病診斷要點. 中華神經科雜志, 1996, 29(6): 379-380.
10. Buesing C, Fisch G, O′Donnell M, 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 Neuroeng Rehabil, 2015, 12: 69.
11. 張雅靜, 張小蘭, 馬延愛, 等. Barthel 指數量表應用于急性腦卒中患者生活能力測量的信度研究. 中國護理管理, 2007, 7(5): 30-32.
12. Stinear CM. Prediction of motor recovery after stroke: advances in biomarkers. Lancet Neurol, 2017, 16(10): 826-836.
13. 勵建安, 孟殿懷. 步態分析的臨床應用. 中華物理醫學與康復雜志, 2006, 28(7): 500-503.
14. Morone G, Paolucci S, Cherubini A, et al. Robot-assisted gait training for stroke patients: current state of the art and perspectives of robotics. Neuropsychiatr Dis Treat, 2017, 13: 1303-1311.
15. 王麗萍. 康復治療時機對腦卒中患者預后的影響. 河北醫學, 2014, 20(11): 1798-1800, 1801.
16. van Kammen K, Boonstra AM, van der Woude LHV, et al. Differences in muscle activity and temporal step parameters between lokomat guided walking and treadmill walking in post-stroke hemiparetic patients and healthy walkers. J Neuroeng Rehabil, 2017, 14(1): 32.
17. Calabrò RS, Naro A, Russo M, et al. Shaping neuroplasticity by using powered exoskeletons in patients with stroke: a randomized clinical trial. J Neuroeng Rehabil, 2018, 15(1): 35.
18. Yoshimoto T, Shimizu I, Hiroi Y. Sustained effects of once-a-week gait training with hybrid assistive limb for rehabilitation in chronic stroke: case study. J Phys Ther Sci, 2016, 28(9): 2684-2687.
19. Iosa M, Morone G, Bragoni M, et al. Driving electromechanically assisted gait trainer for people with stroke. J Rehabil Res Dev, 2011, 48(2): 135-146.
20. Shi D, Zhang W, Ding X, et al. Parametric generation of three-dimensional gait for robot-assisted rehabilitation. Biol Open, 2020, 9(3): pii: bio047332.
21. Lefeber N, De Buyzer S, Dassen N, et al. Energy consumption and cost during walking with different modalities of assistance after stroke: a systematic review and meta-analysis. Disabil Rehabil, 2019: 1-17.
22. Erbil D, Tugba G, Murat TH, et al. Effects of robot-assisted gait training in chronic stroke patients treated by botulinum toxin-a: a pivotal study. Physiother Res Int, 2018, 23(3): e1718.
23. Bustamante Valles K, Montes S, Madrigal Mde J, et al. Technology-assisted stroke rehabilitation in Mexico: a pilot randomized trial comparing traditional therapy to circuit training in a Robot/technology-assisted therapy gym. J Neuroeng Rehabil, 2016, 13(1): 83.
24. Wernig A. “Ineffectiveness” of automated locomotor training. Arch Phys Med Rehabil, 2005, 86(12): 2385-236.
25. Hebenstreit F, Leibold A, Krinner S, et al. Effect of walking speed on gait sub phase durations. Hum Mov Sci, 2015, 43: 118-124.
26. 張利. 不同步行速度的下肢康復機器人訓練對腦卒中患者步行能力的影響. 重慶: 重慶醫科大學, 2018.
27. Yoshimoto T, Shimizu I, Hiroi Y, et al. Feasibility and efficacy of high-speed gait training with a voluntary driven exoskeleton robot for gait and balance dysfunction in patients with chronic stroke: nonrandomized pilot study with concurrent control. Int J Rehabil Res, 2015, 38(4): 338-343.
28. Lam T, Anderschitz M, Dietz V. Contribution of feedback and feedforward strategies to locomotor adaptations. J Neurophysiol, 2006, 95(2): 766-773.
29. Reisman DS, Wityk R, Silver K, et al. Locomotor adaptation ona split-belt treadmill can improve walking symmetry post-stroke. Brain, 2007, 130(Pt 7): 1861-1872.
30. Ohura T, Hase K, Nakajima Y, et al. Validity and reliability ofa performance evaluation tool based on the modified Barthel Index for stroke patients. BMC Med Res Methodol, 2017, 17(1): 131.
31. Nascimento LR, de Oliveira CQ, Ada L, et al. Walking training with cueing of cadence improves walking speed and stride length after stroke more than walking training alone: a systematic review. J Physiother, 2015, 61(1): 10-15.

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Preliminary study on rehabilitation effects of the A3 robot-assisted gait training on patients with chronic stroke

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