Chinese Journal of Tissue Engineering Research ›› 2026, Vol. 30 ›› Issue (16): 4125-4136.doi: 10.12307/2026.685

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A Transformer-based convolutional neural network fusion approach for single inertial recognition of lumbar rehabilitation exercises

Yu Shenghan1, 2, Cheng Xiankai1, 2, Zheng Yue1, 2, Yang Ying2, 3   

  1. 1School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Suzhou 215163, Jiangsu Province, China; 2Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, Jiangsu Province, China; 3The People’s Hospital of Suzhou New District, Suzhou 215129, Jiangsu Province, China
  • Received:2025-07-01 Accepted:2025-08-22 Online:2026-06-08 Published:2025-11-27
  • Contact: Cheng Xiankai, MS, Associate researcher, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Suzhou 215163, Jiangsu Province, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, Jiangsu Province, China
  • About author:Yu Shenghan, MS candidate, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Suzhou 215163, Jiangsu Province, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, Jiangsu Province, China
  • Supported by:
    National Key Research and Development Program of China, No. 2023YFC3604804; Key Research and Development Program Project of Jiangsu Province, No. BE2022064-2  (to CXK)

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Abstract: BACKGROUND: Inertial measurement units are widely used for human posture perception and dynamic capture. Deep learning has gradually replaced traditional rules and feature engineering, and is commonly used in action recognition tasks. Convolutional neural networks perform well in extracting local dynamic features, while Transformer-based convolutional neural networks approach demonstrates strong capabilities in modeling long-term dependencies.
OBJECTIVE: To develop a recognition method based on a Transformer-convolutional neural networks-based fusion model to classify lumbar rehabilitation exercises using data from a single inertial measurement unit.
METHODS: A dataset was constructed by collecting tri-axial accelerometer and gyroscope signals from six healthy participants performing standardized lumbar rehabilitation movements with a single waist-mounted inertial measurement unit. Each trial was labeled according to the exercise type. Transformer-based convolutional neural network fusion model was trained using this dataset to construct a motion classification system. Model performance was evaluated using leave-one-out cross-validation and compared against baseline models, including  linear discriminant analysis, support vector machine, multi-layer perception, and the standard Transformer. 
RESULTS AND CONCLUSION: Experimental results show that the proposed Transformer-based convolutional neural network fusion model achieved a classification accuracy of 96.67% and an F1-score of 0.966 9 across five exercise categories. Compared with conventional algorithms, it demonstrated superior accuracy and generalizability under the single-sensor constraint. These findings validate the practicality of deep learning models using single inertial measurement unit data for lumbar rehabilitation monitoring and provide a foundation for developing lightweight, highly deployable home-based rehabilitation systems.


Key words: chronic back pain, rehabilitation training, deep learning, Transformer, single inertial sensor, action classification

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