Detecting human movement intentions is fundamental to neural control of robotic exoskeletons, as it is essential for achieving seamless transitions between different locomotion modes. In this study, we enhanced a muscle synergy-inspired method of locomotion mode identification by fusing the electromyography data with two types of data from wearable sensors (inertial measurement units), namely linear acceleration and angular velocity. From the finite state machine perspective, the enhanced method was used to systematically identify 2 static modes, 7 dynamic modes, and 27 transitions among them. In addition to the five broadly studied modes (level ground walking, ramps ascent/descent, stairs ascent/descent), we identified the transition between different walking speeds and modes of ramp walking at different inclination angles. Seven combinations of sensor fusion were conducted, on experimental data from 8 able-bodied adult subjects, and their classification accuracy and prediction time were compared. Prediction based on a fusion of electromyography and gyroscope (angular velocity) data predicted transitions earlier and with higher accuracy. All transitions and modes were identified with a total average classification accuracy of 94.5% with fused sensor data. For nearly all transitions, we were able to predict the next locomotion mode 300-500ms prior to the step into that mode.

中文翻译：

---

一种基于可穿戴传感器融合的肌肉协同检测人体运动意图的方法

检测人体运动意图是机器人外骨骼神经控制的基础，因为它对于实现不同运动模式之间的无缝转换至关重要。在这项研究中，我们通过将肌电数据与来自可穿戴传感器（惯性测量单元）的两种类型的数据，即线性加速度和角速度融合，增强了一种受肌肉协同启发的运动模式识别方法。从有限状态机的角度来看，增强方法被用来系统地识别 2 个静态模式，7 个动态模式，以及它们之间的 27 个转换。除了五种广泛研究的模式（平地步行、坡道上升/下降、楼梯上升/下降）之外，我们还确定了不同步行速度和不同倾斜角度下坡道步行模式之间的过渡。对 8 名身体健全的成年受试者的实验数据进行了 7 种传感器融合组合，并比较了它们的分类精度和预测时间。基于肌电图和陀螺仪（角速度）数据融合的预测更早、更准确地预测转换。使用融合传感器数据识别所有转换和模式的总平均分类准确率为 94.5%。对于几乎所有的转换，我们都能够在进入该模式之前 300-500 毫秒预测下一个移动模式。使用融合传感器数据识别所有转换和模式的总平均分类准确率为 94.5%。对于几乎所有的转换，我们都能够在进入该模式之前 300-500 毫秒预测下一个移动模式。使用融合传感器数据识别所有转换和模式的总平均分类准确率为 94.5%。对于几乎所有的转换，我们都能够在进入该模式之前 300-500 毫秒预测下一个移动模式。