A lower-limb exoskeleton robot identifies the wearer′s walking intention and assists the walking movement through mechanical force; thus, it is important to be able to identify the wearer′s movement in real-time. Measurement of the angle of the knee and ankle can be difficult in the case of patients who cannot move the lower-limb joint properly. Therefore, in this study, the knee angle as well as the angles of the talocrural and subtalar joints of the ankle were estimated during walking by applying the neural network to two inertial measurement unit (IMU) sensors attached to the thigh and shank. First, for angle estimation, the gyroscope and accelerometer data of the IMU sensor were obtained while walking at a treadmill speed of 1 to 2.5 km/h while wearing an exoskeleton robot. The weights according to each walking speed were calculated using a neural network algorithm programmed in MATLAB software. Second, an appropriate weight was selected according to the walking speed through the IMU data, and the knee angle and the angles of the talocrural and subtalar joints of the ankle were estimated in real-time during walking through a feedforward neural network using the IMU data received in real-time. We confirmed that the angle estimation error was accurately estimated as 1.69° ± 1.43 (mean absolute error (MAE) ± standard deviation (SD)) for the knee joint, 1.29° ± 1.01 for the talocrural joint, and 0.82° ± 0.69 for the subtalar joint. Therefore, the proposed algorithm has potential for gait rehabilitation as it addresses the difficulty of estimating angles of lower extremity patients using torque and EMG sensors.

中文翻译：

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神经网络刺激下肢外骨骼机器人的膝盖和踝关节（足关节，距下关节）连续行走角度

下肢外骨骼机器人识别佩戴者的步行意图，并通过机械力辅助步行运动。因此，重要的是能够实时识别佩戴者的运动。对于无法正确移动下肢关节的患者，可能难以测量膝盖和踝关节的角度。因此，在这项研究中，通过将神经网络应用于连接到大腿和小腿的两个惯性测量单元（IMU）传感器，估计了步行过程中的膝盖角度以及踝关节的距骨和距下关节的角度。首先，为了进行角度估计，在佩戴外骨骼机器人的情况下，以1至2.5 km / h的跑步机速度行走时，获得了IMU传感器的陀螺仪和加速度计数据。使用在MATLAB软件中编程的神经网络算法来计算根据每个步行速度的权重。其次，通过IMU数据根据步行速度选择合适的重量，并使用IMU数据实时估算步行通过前馈神经网络时的膝盖角度以及脚踝和距下踝关节的角度。实时接收。我们确认，角度估计误差准确地估计为膝关节为1.69°±1.43（平均绝对误差（MAE）±标准偏差（SD）），滑膜关节为1.29°±1.01，而角膜关节为0.82°±0.69。距下关节。因此，提出的算法具有步态康复的潜力，因为它解决了使用扭矩和EMG传感器估算下肢患者角度的困难。