Journal of Bionic Engineering ›› 2024, Vol. 21 ›› Issue (1): 224-235.doi: 10.1007/s42235-023-00435-w
A Comparison of Four Neural Networks Algorithms on Locomotion Intention Recognition of Lower Limb Exoskeleton Based on Multi‑source Information
Duojin Wang1,2； Xiaoping Gu1；Hongliu Yu1,2
- 1 Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, China 2 Shanghai Engineering Research Center of Assistive Devices, 516 Jungong Road, Shanghai 200093, China
A Comparison of Four Neural Networks Algorithms on Locomotion Intention Recognition of Lower Limb Exoskeleton Based on Multi‑source Information
Duojin Wang1,2； Xiaoping Gu1；Hongliu Yu1,2
- 1 Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, China 2 Shanghai Engineering Research Center of Assistive Devices, 516 Jungong Road, Shanghai 200093, China
摘要： 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.