Construction and Application of a Tactile Somatosensory Comfort Model for Scrubbing Tasks.

Tactile somatosensory comfort is a critical factor in ergonomics research, particularly in designing assistive robots for geriatric care. Despite its importance, existing studies lack comprehensive comfort models tailored for optimizing system control in such applications. This study addresses this gap by introducing the first derivation of a tactile somatosensory comfort model that integrates Stevens' law with the energy transfer function, establishing a link between physical stimuli and psychological responses. Through experimental data collection and parameter fitting, a quantitative relationship between comfort and psychological responses was established, facilitating the development of a novel optimal control model. The model parameters were fitted using the Physics-Informed Neural Networks (PINNs) algorithm, while the optimal scrubbing parameters for force (1.68 N) and velocity (36.47 mm/s) were determined via the Particle Swarm Optimization (PSO) algorithm. Validation experiments involving 20 participants, which monitored physiological parameters such as heart rate variability (HRV), confirmed the model's effectiveness in enhancing comfort while ensuring robustness and generalizability. These findings contribute a novel theoretical framework for modelling and applying tactile somatosensory comfort, providing valuable insights for future research and development.