Deep learning-based semantic segmentation of human features in bath scrubbing robots

With the rise in the aging population, an increase in the number of semidisabled elderly individuals has been noted, leading to notable challenges in medical and healthcare, exacerbated by a shortage of nursing staff. This study aims to enhance the human feature recognition capabilities of bath scrubbing robots operating in a water fog environment. The investigation focuses on semantic segmentation of human features using deep learning methodologies. Initially, 3D point cloud data of human bodies with varying sizes are gathered through light detection and ranging to establish human models. Subsequently, a hybrid filtering algorithm was employed to address the impact of the water fog environment on the modeling and extraction of human regions. Finally, the network is refined by integrating the spatial feature extraction module and the channel attention module based on PointNet. The results indicate that the algorithm adeptly identifies feature information for 3D human models of diverse body sizes, achieving an overall accuracy of 95.7%. This represents a 4.5% improvement compared with the PointNet network and a 2.5% enhancement over mean intersection over union. In conclusion, this study substantially augments the human feature segmentation capabilities, facilitating effective collaboration with bath scrubbing robots for caregiving tasks, thereby possessing significant engineering application value.