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.

中文翻译：

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处理用于外骨骼运动控制的表面肌电信号。

表面肌电图（sEMG）信号已用于机器人辅助设备的自愿控制。在提高系统性能精度和信号处理以消除系统噪声方面仍然存在挑战。这项研究介绍了肌电图驱动外骨骼和集成跑步机的速度控制的程序并进行了初步验证，目的是提供用户与系统之间的更好交互。使用自相关算法和贝叶斯融合算法从sEMG信号中提取步态周期持续时间（GCD）。然后对各种步行速度的GCD进行编程，以控制外骨骼机器人系统的运动速度。在6名健康志愿者中测试并验证了该sEMG控制的机器人辅助移动系统的性能和效率。结果表明，自相关算法从单个肌肉收缩中提取了GCD。在指定的步行速度下，各个肌肉的GCD在不同的步行步骤之间具有差异性。贝叶斯融合算法处理了多条肌肉的GCD，从而产生了具有最小方差的最终GCD。融合的GCD有效地控制了外骨骼和跑步机的运动速度。在更快的步行速度中发现具有较短GCD的EMG信号具有较高的幅度。使用融合的GCD和步态步长的算法产生了正弦曲线波形形状的轨迹关节运动轨迹。通过安装在臀部上的解码器测量的外骨骼的关节角度证明是正弦波形。外骨骼的髋关节运动轨迹与由基于高度一致的融合GCD的计算机算法生成的轨迹曲线投影的角度相匹配。由EMG驱动的速度控制提供了人机间的协调机制，可根据用户的意图对外骨骼-跑步机系统进行直观的速度控制。潜在地，整个系统可用于不完全性脊髓半球性卒中患者的步态康复，作为目标导向和任务导向的训练工具。