One potential limitation of using head motion parameters for respiratory variation estimation is the presence of artifacts or noise in the motion data that may interfere with accurate estimation. To address this, preprocessing techniques such as motion correction algorithms can be applied to clean the motion data and remove any irrelevant or erroneous signals. Additionally, incorporating advanced signal processing methods or filters specifically designed to enhance the quality of head motion parameters can help mitigate the impact of noise and artifacts on the estimation process. Moreover, utilizing a multi-modal approach that combines head motion parameters with other physiological signals related to respiration, such as heart rate variability, can provide a more comprehensive and robust estimation of respiratory variations.