With a global prevalence ranging from 12% to 19%, constipation exerts a severe negative impact on the quality of life of patients. Alleviating constipation can be effectively achieved through abdominal massage. Nevertheless, this method confronts multiple hurdles. For instance, there is a scarcity of professional therapists, the effectiveness of massage varies significantly, and there is an absence of standardized massage techniques. This research presents the design plan of a six-degree-of-freedom multi-technique abdominal massage robot. This robot aims to ease the workload of nursing staff. Structured with a series–parallel configuration, the robot is capable of carrying out four distinct massage techniques: pressing, vibrating, pushing, and kneading. Combining anatomical principles and the meridian theories of traditional Chinese medicine, it constructs a vital model for massage actions, which allows for accurate targeting of massage areas. The hardware system is designed modularly, which facilitates efficient information collection and motor control. In terms of the control strategy, admittance control is utilized to establish a closed-loop system. This ensures that the desired force curve can be precisely tracked during the planning of the abdominal massage trajectory, thus attaining flexible control. Experimental results indicate that within the complex abdominal environment consisting of diverse soft tissues, the six-degree-of-freedom multi-technique abdominal massage robot can generate stable massage interaction forces. It is anticipated that this robot system will standardize massage techniques, unify the patterns of abdominal massage, and offer novel perspectives for the intelligent and multi-technical application of clinical abdominal massage in the future.

Abdominal massage for constipation has problems such as high labor costs, insufficient standardization, and large differences in efficacy. This study aims to develop a new type of abdominal massage robot to break through the bottlenecks of manual abdominal massage. This paper constructs a new six-degree-of-freedom multi-functional abdominal massage robot system, improves and designs a new series-parallel hybrid mechanical structure, and based on this, establishes an abdominal interaction force control strategy that uses a variable-stiffness physical model to represent the biomechanical properties of the abdomen. Verified by simulation and human experiments, this robot can accurately reproduce a variety of massage techniques, and demonstrate stable force control performance in prosthesis experiments and human abdominal tests, with the interaction force error controlled within 0.8 N, providing an innovative path for the intelligentization and standardization of clinical abdominal massage.

This research focuses on the crucial issue of ascertaining safe and suitable massage forces for abdominal massage robots. It does so by constructing variable stiffness soft tissue physical models and devising a machine learning-based prediction algorithm. Twelve healthy volunteers with diverse body types (underweight, standard, overweight) were enlisted. A self-developed experimental platform was utilized to gather high-precision mechanical data. Two physical models were formulated to depict the nonlinear force-displacement relationship of abdominal soft tissues. Model 1 was based on an exponential function, and Model 2 was based on a power function. Model 1 showed greater accuracy for standard body types (R² > 0.95 at 72.22% of data points) and underweight body types (R² > 0.95 at 100% of data points). In contrast, Model 2 was a better fit for overweight individuals (R² > 0.95 at 47.06% of data points). Furthermore, a transformer-based machine learning algorithm was developed. This algorithm predicts interaction forces using anthropometric and physiological characteristics. It achieved an accuracy of 92.60% within a pressing depth of 0–15 mm. In most situations, especially in full-range predictions (with an accuracy of 58.60%), the algorithm outperformed both physical models. This study presents a dual framework that combines biomechanical modeling and data-driven algorithms. It lays the theoretical and practical groundwork for safe and adaptable force control in abdominal massage robotics.