Prediction of Massage Force and Intra-abdominal Wall Deformation During Massage by a Digital Twin Model Based on an Abdominal Finite Element Model.
Authors
Affiliations (9)
Affiliations (9)
- Institute of Intelligent Rehabilitation Engineering , University of Shanghai for Science and Technology, Shanghai, 200093, China.
- Shanghai Engineering Research Center of Assistive Devices, Shanghai, 200093, China.
- Key Laboratory of Neural-Functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai, 200093, China.
- Institute of Intelligent Rehabilitation Engineering , University of Shanghai for Science and Technology, Shanghai, 200093, China. [email protected].
- Shanghai Engineering Research Center of Assistive Devices, Shanghai, 200093, China. [email protected].
- Key Laboratory of Neural-Functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai, 200093, China. [email protected].
- Institute of Intelligent Rehabilitation Engineering , University of Shanghai for Science and Technology, Shanghai, 200093, China. [email protected].
- Shanghai Engineering Research Center of Assistive Devices, Shanghai, 200093, China. [email protected].
- Key Laboratory of Neural-Functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai, 200093, China. [email protected].
Abstract
Constipation is a prevalent disorder of the digestive system, and abdominal massage is commonly employed to alleviate its symptoms due to its non-invasive nature and minimal side effects. This study aims to develop an accurate correlation model that links massage force (MF), massage depth (MD), and intra-abdominal wall deformation (IAWD) based on patient-specific data, thereby providing quantitative standards and personalized treatment options for the use of clinical massage devices. The study utilized finite element method (FEM) and machine learning (ML) techniques to construct a digital twin model of the abdomen. Initially, a high-fidelity skin-muscle two-layer abdominal finite element model was developed using computed tomography (CT) images and mechanical experimental data. Subsequently, four individual learning models were trained, and stacked models were constructed by combining the advantages of these individual models through random forest and K-nearest neighbor algorithms to predict MF and IAWD. The prediction accuracy rates for MF in the ascending colon, transverse colon, and descending colon regions were 84.80%, 84.00%, and 85.60%, respectively. The prediction accuracy rates for IAWD were 96.00%, 87.00%, and 95.20%, respectively. Compared to traditional finite element simulations, this abdominal digital twin model is capable of delivering predictions within 3 seconds, offering valuable insights for the digital transformation of healthcare.