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[Research on multi-scale convolutional neural network hand muscle strength prediction model improved based on

Yihao Du¹, Mengyu Sun¹, Jingjin Li¹

¹Key Laboratory of Intelligent Rehabilitation and Neuroregulation of Hebei Province, School of Electrical Engineering, Key Laboratory of Intelligent Control and Neural Information Processing, Ministry of Education, Yanshan University, Qinhuangdao, Hebei 066000, P. R. China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi

|February 25, 2025

Summary

A new deep learning model combining multi-scale convolutional neural networks (MSCNN), convolutional block attention module (CBAM), and bidirectional long short-term memory (BiLSTM) enhances muscle strength prediction accuracy for hand rehabilitation. This model effectively captures spatial and temporal data features, improving rehabilitation strategies.

Keywords:

Ablation experiment Convolutional block attention module Hand force Multi-scale convolutional neural network Muscle strength prediction

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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Area of Science:

Biomedical Engineering
Rehabilitation Science
Artificial Intelligence in Healthcare

Context:

Quantitative assessment of muscle strength is crucial for effective hand function rehabilitation.
Current methods may lack the precision to capture complex spatio-temporal muscle activity dynamics.
Developing advanced computational models can optimize rehabilitation training strategies.

Purpose:

To develop and validate a novel deep learning model (MSCNN-CBAM-BiLSTM) for accurate muscle strength prediction in hand rehabilitation.
To explore the model's ability to integrate spatial and temporal features while suppressing irrelevant data.
To compare the proposed model's performance against traditional machine learning and deep learning approaches.

Summary:

A multi-scale convolutional neural network (MSCNN) integrated with a convolutional block attention module (CBAM) and bidirectional long short-term memory network (BiLSTM) was constructed for muscle strength prediction.
The model demonstrated improved accuracy in predicting hand muscle strength across varying force levels (40-60% MVC) compared to SVM, RF, CNN, and other hybrid models.
Ablation studies confirmed the critical role of the CBAM module in enhancing prediction performance, highlighting its ability to refine feature representation.

Impact:

The proposed MSCNN-CBAM-BiLSTM model significantly improves the accuracy of muscle strength prediction, aiding in the formulation of personalized rehabilitation plans.
Deeper understanding of hand muscle activity characteristics and underlying mechanisms can be achieved, facilitating advancements in hand function recovery.
Provides a robust computational tool for objective assessment in clinical settings and research, supporting evidence-based rehabilitation practices.