Focusing on the issue that collecting multi-dimensional numerical sensitive data directly from wearable devices may leak users' privacy information when a data server was untrusted, by introducing a local differential privacy model, a personalized local privacy protection scheme for the numerical sensitive data of wearable devices was proposed. Firstly, by setting the privacy budget threshold interval, a users' privacy budget within the interval was set to meet the individual privacy needs, which also met the definition of personalized local differential privacy. Then, security domain was used to normalize the sensitive data. Finally, the Bernoulli distribution was used to perturb multi-dimensional numerical data by grouping, and attribute security domain was used to restore the disturbance results. The theoretical analysis shows that the proposed algorithm meets the personalized local differential privacy. The experimental results demonstrate that the proposed algorithm has lower Max Relative Error (MRE) than that of Harmony algorithm, thus effectively improving the utility of aggregated data collecting from wearable devices with the untrusted data server as well as protecting users' privacy.