Knowledge-Based Deep Learning for Time-Efficient Inverse Dynamics

• URL: http://arxiv.org/abs/2412.05403v1
• Date: Fri, 06 Dec 2024 20:12:52 GMT
• Title: Knowledge-Based Deep Learning for Time-Efficient Inverse Dynamics
• Authors: Shuhao Ma, Yu Cao, Ian D. Robertson, Chaoyang Shi, Jindong Liu, Zhi-Qiang Zhang,
• Abstract summary: We propose a knowledge-based deep learning framework for time-efficient inverse dynamic analysis. The BiGRU neural network is selected as the backbone of our model due to its proficient handling of time-series data. The experimental results have shown that the selected BiGRU architecture outperforms other neural network models when trained using our specifically designed loss function.
• Score: 5.78355428732981
• License:
• Abstract: Accurate understanding of muscle activation and muscle forces plays an essential role in neuro-rehabilitation and musculoskeletal disorder treatments. Computational musculoskeletal modeling has been widely used as a powerful non-invasive tool to estimate them through inverse dynamics using static optimization, but the inherent computational complexity results in time-consuming analysis. In this paper, we propose a knowledge-based deep learning framework for time-efficient inverse dynamic analysis, which can predict muscle activation and muscle forces from joint kinematic data directly while not requiring any label information during model training. The Bidirectional Gated Recurrent Unit (BiGRU) neural network is selected as the backbone of our model due to its proficient handling of time-series data. Prior physical knowledge from forward dynamics and pre-selected inverse dynamics based physiological criteria are integrated into the loss function to guide the training of neural networks. Experimental validations on two datasets, including one benchmark upper limb movement dataset and one self-collected lower limb movement dataset from six healthy subjects, are performed. The experimental results have shown that the selected BiGRU architecture outperforms other neural network models when trained using our specifically designed loss function, which illustrates the effectiveness and robustness of the proposed framework.

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