# The Role of Deep Learning and Gait Analysis in Parkinson's Disease: A Systematic Review
The neurological conditions of patients were assessed across various stages of the disease: initial, intermediate, and advanced stages. Additionally, they examined the resilience of the proposed approach by incorporating speech signals from three distinct languages: Spanish, German, and Czech. The authors performed three main experiments: classification of PD patients and HC subjects, classification of PD patients in different stages of the disease according to the total MDS-UPDDS-III score, and classification of PD patients in different stages of the disease according to specific impairments in the lower and upper limbs and in speech by considering sub-scores of the MDS-UPDRS Part III scale. Individual CNNs were trained for each modality on a cohort of 84 subjects (44 PD patients); afterward, multimodal assessment was performed by combining the three bio-signals. In 2022, [PERSON] et al. acquired gait data from 134 subjects (68 PD and 66 HC subjects) and considered two subgroups of HCs, namely, elderly and young [54]. This was the first study in gait analysis where temporal and spectral information was combined in an architecture of DL. They employed segments of 3 s of raw time series data to train three different DL architectures: CNN, gated recurrent unit (GRU), and CNN + GRU. The authors validated that the combination of CNN and GRU methods yielded results comparable with those obtained with GRUs alone. However, due to the small dataset employed in this study, it was not possible to demonstrate the authors' starting assumption that the combination of CNN and GRU would lead to greater accuracy. The authors of the previous articles thus trained their neural networks on the same cohort of subjects with the shared goal of predicting PD. However, [PERSON] et al. achieved superior results, where they reached an accuracy of 97.6% and an AUC of 98.8% by merging the three analyzed signals [53]. [PERSON] et al., [PERSON] et al., [PERSON] et al., [PERSON] et al., [PERSON] et al., and [PERSON] et al. used the public dataset Physionet Gait in PD (93 PD patients and 73 HC subjects) in their studies, which contains gait signals collected via sixteen pressure sensors placed on the left and right soles of the subjects' shoes, with eight on each foot [55; 56; 57; 58; 59; 60]. In 2023, [PERSON] et al. proposed an explainable learning architecture to address the challenge of early PD diagnosis by analyzing differences between PD patients and healthy individuals. The study incorporated three domains (force, peak, and abnormality) to extract twenty-two features for analysis [55]. Subsequently, the ANOVA with recursive reduction (ARR) was first used to determine which features should be removed; then two neural networks, XGBoost and CNN, were trained. They found that the learning model increased the accuracy of the performance analysis. In the same year, [PERSON] et al. proposed a robust frequency-domain-based graph adaptive network (RFdGAD) for PD detection from gait information. Previously existing DL methods for PD detection did not take into account automatic feature extraction in the frequency domain [56]. The RFdGAD utilized initially learns frequency-domain features of signals from individual foot sensors using a frequency representation learning (FRL) block. Subsequently, it employs a graph adaptive network block (GAD) that takes frequency-domain features as the input to dynamically learn and leverage the connections between various sensor signals, and thus, enhance the PD detection accuracy. [PERSON] et al., [PERSON] et al., [PERSON] et al., and [PERSON] et al., in addition to the prediction of PD, also considered the severity of symptoms as an objective [57; 58; 59; 60]. In 2022, [PERSON] et al. subjected the signal data from the sensors to feature extraction. These features were then input into a hybrid DL architecture that combined CNNs with locally weighted random forests [57]. The study of [PERSON] et al.