DL-Enhanced GAIT Analysis for Rehabilitation: A Comprehensive Survey

International Journal of Electrical and Electronics Engineering

© 2024 by SSRG - IJEEE Journal

Volume 11 Issue 11

Year of Publication : 2024

Authors : O. Pushpalatha, R. Premkumar

10.14445/23488379/IJEEE-V11I11P121

How to Cite?

O. Pushpalatha, R. Premkumar, "DL-Enhanced GAIT Analysis for Rehabilitation: A Comprehensive Survey," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 11, pp. 203-221, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I11P121

Abstract:

Integrating DL techniques has revolutionized gait analysis, enhancing the accuracy and efficiency of detecting and characterizing gait abnormalities. This paper surveys recent studies employing Deep Learning algorithms (DL), such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to analyze gait patterns from diverse data sources with wearable sensors, video footage, and motion capture systems. The advantages of DL in handling complex, high-dimensional gait data and its potential to uncover subtle patterns indicative of disease or recovery status are discussed. Furthermore, the clinical applications of DL-based gait analysis, emphasizing its role in personalized rehabilitation programs and real-time monitoring, are explored. The paper also addresses the challenges of implementing DL in clinical settings, such as the need for large, annotated datasets, computational resources, and interdisciplinary collaboration. In conclusion, this survey highlights the transformative potential of DL methods in gait analysis for fracture and Parkinson's disease patients. By providing a detailed overview of current research and identifying key trends and challenges, this work seems to inform and inspire further advancements in this field, ultimately enhancing rehabilitation outcomes and quality of life for affected individuals.

Keywords:

CNN, DL techniques, Gait analysis, RNN.

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