Mechanism of pathological recognition based on bioelectrical impedance spectrum of elbow joint

Ping Zhang; Kai Su; GuoDong Gao

doi:10.1117/12.2639610

27 June 2022 Mechanism of pathological recognition based on bioelectrical impedance spectrum of elbow joint

Ping Zhang, Kai Su, GuoDong Gao

Proceedings Volume 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022); 122531L (2022) https://doi.org/10.1117/12.2639610
Event: Second International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 2022, Qingdao, China

Abstract

The elbow joint is prone to stiffness and adhesion after trauma or surgery. High-energy trauma can easily lead to loss of mobility of the elbow joint. Mild trauma can also cause stiffness in the elbow joint. In recent years, despite the remarkable progress made in the treatment of trauma to the elbow joint and surrounding tissues, postoperative elbow joint contractures are still very common. The improved elbow orthosis can provide a portable rehabilitation environment for the elbow joint after the operation, which is not affected by the environment, can be used for rehabilitation exercises. Bioimpedance spectroscopy (BIS) can quickly and accurately obtain mechanism information through the analysis of bioelectric signals, and has the characteristics of highspeed, portability, and non-invasiveness. Therefore, this paper proposes a technical solution for elbow joint orthosis based on the combination of bioelectrical impedance spectroscopy and GRNN neural network. A feedback type elbow joint control system based on GRNN network is proposed, which realizes the control strategy of converting the patient's elbow joint pathological information into elbow joint orthosis control information. The data obtained in the experiment were processed by the improved Cole-Cole model, partial least squares method, and improved VMD-HHT model. As the elbow joint continues to heal, the internal changes are the first to produce a large amount of extracellular fluid. And the conclusion that the wound can be healed through cell division. And input the information into the GRNN network for training, and finally adjust the force applied by the elbow joint orthosis to the patient's elbow joint through the training results, and achieve good results.

Citation Download Citation

Ping Zhang, Kai Su, and GuoDong Gao "Mechanism of pathological recognition based on bioelectrical impedance spectrum of elbow joint", Proc. SPIE 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 122531L (27 June 2022); https://doi.org/10.1117/12.2639610

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KEYWORDS

Signal detection

Control systems

Neural networks

Data modeling

Surgery

Electronic filtering

Filtering (signal processing)

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