Abdominal mechanics experiments and twin model data

Research Hypothesis: This study identified 18 key acupoints on the human abdomen that aid in relieving constipation, including Zhongwan (CV12), Tianshu (ST25), Daxiong (ST21), and Zusanli (ST36). In the experiment, a mechanical data acquisition platform was used to conduct quasi-static, vertical pressure tests on these acupoints. Experimental data were collected from four subjects, whose abdominal soft tissue layer thicknesses were measured via CT imaging.
Pressure measurements were performed using an SBT-674 sensor attached to the contact end of a push-rod motor. This sensor featured a radius of 7.5 mm, a measurement range of 0–20 N, and a sensitivity of 2.0 ± 10% mV/V. During the experiment, subjects maintained a supine position on an empty stomach while the motor applied vertical downward pressure at 5 mm/s, simultaneously recording pressure data.
Based on the abdominal CT-derived soft tissue 3D model, a finite element model of the abdomen was established. This finite element model comprised 147,851 tetrahedral elements. A cylindrical object applied vertical pressure at a constant speed of 5 mm/s, with a circular contact area of 7.5 mm radius.
Finally, based on the validated finite element model, finite element calculations were performed on 18 key action points and their surrounding auxiliary points. This yielded corresponding massage force values and intra-abdominal wall deformation data at different pressing depths.
The data presented: The first column is the subject number, the second column is the number of the massage points, the third and fourth columns represent the thickness of the skin layer and the muscle layer respectively, the fifth column is the massage depth, the sixth column is the massage force corresponding to the massage depth in the mechanical experiment, and the seventh column is the medial variable of the abdominal wall corresponding to the massage depth calculated by the finite element model of the abdomen.
Data interpretation and application: This study generated a multi-modal dataset on the biomechanical characteristics of key acupoints in the human abdomen through systematic experimental measurements and finite element simulations. The core value of this data lies in the first time converting the empirical operation of traditional massage into quantifiable mechanical parameters, establishing an accurate mapping relationship between "massage depth" and "tissue mechanical response". Specifically, the dataset comprehensively records the massage force required and the medial abdominal wall deformation triggered by different subjects and acupoints at a specific pressing depth.