Department of Rehabilitation Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
Graduate School of Science and Technology, Keio University, Tokyo, Kanagawa, Japan.
BMC Neurol. 2024 May 9;24(1):144. doi: 10.1186/s12883-024-03651-x.
Restoring shoulder function is critical for upper-extremity rehabilitation following a stroke. The complex musculoskeletal anatomy of the shoulder presents a challenge for safely assisting elevation movements through robotic interventions. The level of shoulder elevation assistance in rehabilitation is often based on clinical judgment. There is no standardized method for deriving an optimal level of assistance, underscoring the importance of addressing abnormal movements during shoulder elevation, such as abnormal synergies and compensatory actions. This study aimed to investigate the effectiveness and safety of a newly developed shoulder elevation exoskeleton robot by applying a novel optimization technique derived from the muscle synergy index.
Twelve chronic stroke participants underwent an intervention consisting of 100 robot-assisted shoulder elevation exercises (10 × 10 times, approximately 40 min) for 10 days (4-5 times/week). The optimal robot assist rate was derived by detecting the change points using the co-contraction index, calculated from electromyogram (EMG) data obtained from the anterior deltoid and biceps brachii muscles during shoulder elevation at the initial evaluation. The primary outcomes were the Fugl-Meyer assessment-upper extremity (FMA-UE) shoulder/elbow/forearm score, kinematic outcomes (maximum angle of voluntary shoulder flexion and elbow flexion ratio during shoulder elevation), and shoulder pain outcomes (pain-free passive shoulder flexion range of motion [ROM] and visual analogue scale for pain severity during shoulder flexion). The effectiveness and safety of robotic therapy were examined using the Wilcoxon signed-rank sum test.
All 12 patients completed the procedure without any adverse events. Two participants were excluded from the analysis because the EMG of the biceps brachii was not obtained. Ten participants (five men and five women; mean age: 57.0 [5.5] years; mean FMA-UE total score: 18.7 [10.5] points) showed significant improvement in the FMA-UE shoulder/elbow/forearm score, kinematic outcomes, and pain-free passive shoulder flexion ROM (P < 0.05). The shoulder pain outcomes remained unchanged or improved in all patients.
The study presents a method for deriving the optimal robotic assist rate. Rehabilitation using a shoulder robot based on this derived optimal assist rate showed the possibility of safely improving the upper-extremity function in patients with severe stroke in the chronic phase.
恢复肩部功能对于中风后的上肢康复至关重要。肩部复杂的肌肉骨骼解剖结构给通过机器人干预安全协助抬高运动带来了挑战。康复中肩部抬高辅助的程度通常基于临床判断。没有标准化的方法来确定最佳辅助水平,这凸显了在肩部抬高过程中解决异常运动(如异常协同作用和代偿动作)的重要性。本研究旨在通过应用源自肌肉协同指数的新型优化技术,研究一种新开发的肩部抬高外骨骼机器人的有效性和安全性。
12 名慢性中风患者接受了一项干预措施,包括 100 次机器人辅助肩部抬高运动(每次 10 次,约 40 分钟),共 10 天(每周 4-5 次)。在初始评估中,通过计算三角肌和肱二头肌的肌电图(EMG)数据得到的协同收缩指数来检测变化点,从而得出最佳机器人辅助率。主要结果是 Fugl-Meyer 上肢评估(FMA-UE)肩部/肘部/前臂评分、运动学结果(肩部主动最大屈曲角度和肩部抬高时肘部屈曲比)和肩部疼痛结果(肩部无痛被动屈曲活动范围和肩部屈曲时疼痛严重程度的视觉模拟评分)。使用 Wilcoxon 符号秩和检验检查机器人治疗的有效性和安全性。
所有 12 名患者均顺利完成了治疗,没有发生任何不良事件。由于肱二头肌的 EMG 未获得,有两名患者被排除在分析之外。10 名患者(5 名男性和 5 名女性;平均年龄:57.0[5.5]岁;平均 FMA-UE 总分:18.7[10.5]分)在 FMA-UE 肩部/肘部/前臂评分、运动学结果和肩部无痛被动屈曲活动范围方面显示出显著改善(P<0.05)。所有患者的肩部疼痛结果保持不变或改善。
本研究提出了一种确定最佳机器人辅助率的方法。基于该方法得出的最佳辅助率进行肩部机器人康复可能安全地改善慢性期严重中风患者的上肢功能。