Ankle intention detection algorithm using electromyography signal

Abstract

Abstract 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.