Locomotion mode prediction in real-life walking with and without ankle–foot exoskeleton assistance

Abstract

Exoskeletons can assist human locomotion in real-life scenarios, but existing tools for decoding locomotion modes (LMs) focus on recognition rather than prediction, which can lead to delayed assistance. This study proposes a long short-term memory (LSTM) neural network to predict five LMs (level-walking, ramp ascent/descent, stair ascent/descent) with greater lead time compared to state-of-the-art methods. We examined the optimal sequence length (SL) for LSTM-based LM prediction, using data from inertial sensors placed on the lower limbs and the lower back, along with a waist-mounted infrared laser. Ten subjects walked in real-life scenarios, both with and without an ankle–foot exoskeleton. Results show that a 1-s SL provides the most advanced and accurate LM prediction, outperforming SLs of 0.6, 0.8, and 1.2 s. The proposed LSTM model achieved an accuracy of 98 ± 0.31%, predicting LMs 0.66 s in advance (for an average stride time of 1.98 ± 0.83 s). Level-walking presented more misclassifications, and the model primarily relied on inertial data over laser input. Overall, these findings demonstrate the LSTM’s strong predictive capability for both assisted and non-assisted walking and independent of which limb executes the transition, supporting its applicability for exoskeleton-assisted locomotion.