A detailed analysis of the inertial signals input is required when using deep learning models for Parkinson's Disease detection. This work explores the possibility of reducing the input size of the models studying the most appropriate frequency range and determines if it is feasible to evaluate subjects with different sensor locations than those used during training. For experimentation, 3.2-second windows are used to classify signals between Parkinson's patients and control subjects, applying Fast Fourier Transform to the inertial signals and following a Leave-One-Subject-Out Cross-Validation methodology over the PD-BioStampRC21 dataset. It has been observed that the frequency range of 0 to 5 Hz offers a classification accuracy rate of 75.75 ± 0.62% using the five available sensors for training and evaluation, which is close to the model's performance over the entire frequency range, from 0 to 15,625 Hz, which is 77.46 ± 0.60%. Regarding the information transfer between sensors located in different body parts, it was observed that training and evaluating the model using data from the right forearm resulted in an accuracy of 65.17 ± 0.69%. When the model was trained with data from the opposite forearm, the accuracy was similar, at 63.57 ± 0.69%. Likewise, comparable results were found when using data from the other forearm and when training and evaluating with opposite thighs, with accuracy reductions not exceeding 3%.