Deep learning based ankle–foot movement classification for prosthetic foot

Abstract

The primary motivation behind this study is the aspiration to design a prosthetic foot that demonstrates enhanced functionality, enabling more active and prompt responses, particularly tailored for individuals with below-knee amputations. This goal underscores the intention to create a prosthetic foot with the capability to execute foot movements in a more natural and effective manner. A new 1D-ResCNN model has been proposed for the rapid and accurate classification of foot movements based on user intent in the context of a prosthetic limb. This research introduces an innovative approach by integrating inertial measurement units with deep learning algorithms to advance the development of more functional prosthetic feet, specifically tailored for below-knee amputees. Leveraging wearable technologies, this method allows for the prolonged monitoring of foot movements within the users’ natural environments. The dual benefits of cost reduction and enhanced user experience are achieved through this combination of advanced technologies, providing a promising avenue for the evolution of prosthetic foot design and usage. The results obtained with this model are satisfying both in terms of speed and accuracy with 99.8% compared to other methods in the literature.