Deep Learning-Based Mapping of Textile Stretch Sensors to Surface Electromyography Signals: Multilayer Perceptron, Convolutional Neural Network, and Residual Network Models

Gyubin Lee,Sangun Kim, Ji-seon Kim,Jooyong Kim

Processes（2025）

Cited 0|Views1

Abstract

This study evaluates the mapping accuracy between textile stretch sensor data and surface electromyography (sEMG) signals using Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Residual Network (ResNet) models. Data from the forearm, biceps brachii, and triceps brachii were analyzed using Root Mean Square Error (RMSE) and R2 as performance metrics. ResNet achieved the lowest RMSE (e.g., 0.1285 for biceps brachii) and highest R2 (0.8372), outperforming CNN (RMSE: 0.1455; R2: 0.7639) and MLP (RMSE: 0.1789; R2: 0.6722). The residual learning framework of ResNet effectively handles nonlinear patterns and noise, enabling more accurate predictions even for low-variability datasets like the triceps brachii. CNN showed moderate improvement over MLP by learning temporal features but struggled with low-variability datasets. MLP, as the baseline model, demonstrated the highest RMSE and lowest R2, highlighting its limitations in capturing complex relationships. These results suggest the potential reliability of ResNet for mapping textile stretch sensor data to sEMG signals, showing promising performance within the scope of this study. Future research could explore broader applications across different sensor configurations and activities to further validate these findings.

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Key words

textile stretch sensor,surface electromyography (sEMG),deep neural networks,multilayer perceptron (MLP),convolutional neural network (CNN),residual network (ResNet),mapping accuracy,wearable sensor applications

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
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【要点】：本研究通过对比多层感知机（MLP）、卷积神经网络（CNN）和残差网络（ResNet）三种模型，评估了将纺织拉伸传感器数据映射到表面肌电图（sEMG）信号的准确性，发现ResNet模型在准确性和预测能力上表现最优。

【方法】：研究采用了MLP、CNN和ResNet三种深度学习模型，利用均方根误差（RMSE）和R2作为性能评价指标，对比分析了不同模型的映射效果。

【实验】：通过对前臂、肱二头肌和肱三头肌的数据进行分析，实验结果显示ResNet模型在肱二头肌数据上达到了最低的RMSE（0.1285）和最高的R2（0.8372），优于CNN（RMSE: 0.1455; R2: 0.7639）和MLP（RMSE: 0.1789; R2: 0.6722）。数据集名称未在文本中明确提及。