Robust and novel attention guided MultiResUnet model for 3D ground reaction force and moment prediction from foot kinematics

• Published in: Neural computing & applications 2024, Vol.36 (3), p.1105-1121
• Main Authors: Faisal, Md. Ahasan Atick, Mahmud, Sakib, Chowdhury, Muhammad E. H., Khandakar, Amith, Ahmed, Mosabber Uddin, Alqahtani, Abdulrahman, Alhatou, Mohammed
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Biomechanical engineering; Biomechanics; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Correlation coefficients; Data Mining and Knowledge Discovery; Datasets; Deep learning; Force plates; Image Processing and Computer Vision; Impact analysis; Kinematics; Motion capture; Original Article; Probability and Statistics in Computer Science; Signal quality; Three dimensional models
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-023-09081-z

Ground reaction force and moment (GRF&M) measurements are vital for biomechanical analysis and significantly impact the clinical domain for early abnormality detection for different neurodegenerative diseases. Force platforms have become the de facto standard for measuring GRF&M signals in recent years. Although the signal quality achieved from these devices is unparalleled, they are expensive and require laboratory setup, making them unsuitable for many clinical applications. For these reasons, predicting GRF&M from cheaper and more feasible alternatives has become a topic of interest. Several works have been done on predicting GRF&M from kinematic data captured from the subject’s body with the help of motion capture cameras. The problem with these solutions is that they rely on markers placed on the whole body to capture the movements, which can be very infeasible in many practical scenarios. This paper proposes a novel deep learning-based approach to predict 3D GRF&M from only 5 markers placed on the shoe. The proposed network “Attention Guided MultiResUNet” can predict the force and moment signals accurately and reliably compared to the techniques relying on full-body markers. The proposed deep learning model is tested on two publicly available datasets containing data from 66 healthy subjects to validate the approach. The framework has achieved an average correlation coefficient of 0.96 for 3D ground reaction force prediction and 0.86 for 3D ground reaction momentum prediction in cross-dataset validation. The framework can provide a cheaper and more feasible alternative for predicting GRF&M in many practical applications.