Abstract: Lower Limb Exoskeletons (LLEs) are receiving increasing attention for supporting activities of daily living. In such active systems, an intelligent controller may be indispensable. In this paper, we proposed a locomotion intention recognition system based on time series data sets derived from human motion signals. Composed of input data and Deep Learning (DL) algorithms, this framework enables the detection and prediction of users’ movement patterns. This makes it possible to predict the detection of locomotion modes, allowing the LLEs to provide smooth and seamless assistance. The pre-processed eight subjects were used as input to classify four scenes: Standing/Walking on Level Ground (S/WOLG), Up the Stairs (US), Down the Stairs (DS), and Walking on Grass (WOG). The result showed that the ResNet performed optimally compared to four algorithms (CNN, CNN-LSTM, ResNet, and ResNet-Att) with an approximate evaluation indicator of 100%. It is expected that the proposed locomotion intention system will significantly improve the safety and the effectiveness of LLE due to its high accuracy and predictive performance.

Key words: Lower limb exoskeletons (LLEs) , · Locomotion intention , · ResNet , · Multi-source