This paper evaluates a local differential privacy (LDP) approach designed to address the challenges inherent in smartwatch datasets, particularly those dominated by numerical data. Traditional methods such as Lopub and Locop have demonstrated limitations in accurately estimating joint probability distributions (JPD) within these contexts. The Castell2D approach leverages a refined mechanism for perturbation and aggregation that enhances the precision of numerical data handling while maintaining robust privacy guarantees. In smartwatch datasets, the predominance of numerical data poses significant hurdles for existing LDP techniques, which often work with categorical data but fail with continuous values. By incorporating statistical techniques and noise mechanisms, Castell2D achieves a more accurate estimation of JPD. Comparative analyses with established approaches such as Lopub, Locop, and others show that Castell2D consistently outperforms them in terms of estimation accuracy and privacy preservation. The effectiveness of Castell2D is demonstrated through extensive experiments on real-world open smartwatch datasets from three of the largest companies-Fitbit, Apple Watch, and Garmin-and demonstrates its ability to maintain high utility of the data while ensuring privacy standards. This work represents a significant advancement in the field of LDP, providing a practical and effective solution for privacy-preserving data analysis in environments dominated by numerical data.