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

Yu Shenghan; Cheng Xiankai; Zheng Yue; Yang Ying

doi:10.12307/2026.685

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.