AI Article Synopsis

  • Deep neural networks (DNNs) and transfer learning (TL) are used to enhance force estimation from surface electromyograms (sEMG), but previous research mainly focused on a single joint, limiting data diversity.
  • This study explored cross-joint TL between two upper-limb joints using four DNN models, finding that 400 ms is sufficient to capture important sEMG-force relationships, suggesting simpler models may work effectively.
  • Fine-tuning a model pre-trained on elbow data with hand-wrist data improved accuracy and reduced training data needs, with a convolutional neural network achieving a significant lower error rate compared to other models.

Article Abstract

Deep neural networks (DNNs) and transfer learning (TL) have been used to improve surface electromyogram (sEMG)-based force estimation. However, prior studies focused mostly on applying TL within one joint, which limits dataset size and diversity. Herein, we investigated cross-joint TL between two upper-limb joints with four DNN architectures using sliding windows. We used two feedforward and two recurrent DNN models with feature engineering and feature learning, respectively. We found that the dependencies between sEMG and force are short-term (<400 ms) and that sliding windows are sufficient to capture them, suggesting that more complicated recurrent structures may not be necessary. Also, using DNN architectures reduced the required sliding window length. A model pre-trained on elbow data was fine-tuned on hand-wrist data, improving force estimation accuracy and reducing the required training data amount. A convolutional neural network with a 391 ms sliding window fine-tuned using 20 s of training data had an error of 6.03 ± 0.49% maximum voluntary torque, which is statistically lower than both our multilayer perceptron model with TL and a linear regression model using 40 s of training data. The success of TL between two distinct joints could help enrich the data available for future deep learning-related studies.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11598622PMC
http://dx.doi.org/10.3390/s24227301DOI Listing

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