外骨骼
计算机科学
联动装置(软件)
步态
机制(生物学)
人工神经网络
人工智能
弹道
脚踝
物理医学与康复
模拟
医学
哲学
物理
认识论
病理
生物化学
化学
天文
基因
作者
Akim Kapsalyamov,Shahid Hussain,Nicholas Brown,Roland Goecke,Munawar Hayat,Prashant K. Jamwal
标识
DOI:10.1016/j.engappai.2022.105500
摘要
Robotic exoskeletons have demonstrated their effectiveness in post-stroke gait rehabilitation therapy. Nevertheless, further research is being conducted to improve existing rehabilitation exoskeletons in terms of ease-of-use and innovative design. Previously, the adaptation of linkage-based mechanisms for rehabilitation exoskeletons has been considered an option. However, finding linkage parameters that will produce the required gait trajectories using a linkage-based exoskeleton, is quite challenging. It is furthermore challenging to obtain parameters of a linkage-based mechanism designed for a gait rehabilitation task that has to produce two trajectories (for knee and ankle joints) simultaneously. In this work, we propose Deep Generative Neural Networks (DGNN) to obtain a set of optimal dimensions and parameters for the Stephenson III six-bar linkage-based gait exoskeleton. The proposed methodology demonstrates high efficacy in determining the linkage parameters for various target trajectories. The proposed framework, once trained, can accurately predict mechanism parameters to achieve two joint trajectories simultaneously. Subsequent to developing the model, walking trajectories from healthy human subjects are given to the model to determine the optimal linkage dimensions of the gait rehabilitation exoskeleton. The proposed model can be used to assist designers in quickly determining the optimized linkage dimensions of linkage-based mechanisms that can provide various target trajectories.
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