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.

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In this study, an ankle intention detection algorithm was developed to calculate the torque the user wants to exert from the ankle starting from the user’s EMG signal. Since the subtalar joint axis of the ankle is very important for stability, the intent detection algorithm also calculates the torque of the eversion motion of the subtalar joint axis. A dry EMG sensor was used to measure the EMG signal, and an ankle biaxial torque measurement device was manufactured to measure the ankle torque to perform the experiment. The experiment was conducted on four healthy subjects (mean ± SD: height, 177.6 ± 7.3 cm; weight 70.2 ± 8.9 kg; and age, 27 ± 2 years), and the EMG signals and ankle torque were measured. Using the experimental results and a neural network, we developed an intention detection algorithm. When you input an EMG signal, the algorithm estimates the torque of eversion, dorsiflexion, and plantar flexion. The error of the algorithm is 0.37 Nm (subtalar) and 0.57 Nm (talocrural), which is 0.5% (subtalar) and 1.5% (talocrural) of the torque required for walking. Using the algorithm of this study, more accurate and stable exoskeleton robot control becomes possible.

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