# The Role of Deep Learning and Gait Analysis in Parkinson's Disease: A Systematic Review
## 4 Conclusions
In particular, markerless motion capture techniques were employed to compare the gait cycles of patients with PD with those of the HCs and to demonstrate that video-based gait analysis systems can effectively correlate with objective clinical scales. These techniques were also used to investigate specific motor symptoms, such as FoG, and to differentiate between various neurological disorders. It is clear from our systematic review that the markerless motion capture approach integrates smoothly into existing clinical practice, as video capture during the motion analysis of PD patients is already common practice [61; 62; 69; 79]. However, it was not possible to define aunique positioning of the cameras. Indeed, different chamber arrangements were found in our research. In some studies, a single camera was positioned either frontally or laterally to the walkway [61; 64; 65], whereas in studies that used multiple cameras, the cameras were positioned frontally, laterally, and even at 45\({}^{\circ}\) to the subject [63; 65; 66]. In the context of DL approaches for video analysis, CNNs are among the most frequently used methods, particularly when compared with RNNs; see Figure 9. In the field of HPE, OpenPose has been frequently used for the detection of key points of the human body within video footage, as shown in Figure 9. It facilitates the extraction of movements along the sagittal plane, including joint angle movements, such as knee, ankle, and hip flexion-extension, as well as upper limb balance. Additionally, RNNs have been employed to analyze temporal sequences of motion data, thus enabling the detection of subtle changes in gait patterns over time [70; 80]. Furthermore, hybrid neural networks have been used, which combine multiple types of neural architectures to improve the performance of prediction models [54; 59]. The analyzed articles applied various DL techniques to public datasets or their own datasets, which have highly variable dimensions. Moreover, it was found that some of the scientific studies were conducted in collaboration with research institutions and specialized clinics, where motion analysis was not only used for research purposes but was actually integrated as part of clinical trials [49; 54; 61; 62]. In conclusion, gait analysis combined with DL techniques can be helpful in understanding and interpreting gait abnormalities, enabling the prediction of PD and disease severity, and discriminating between different neurodegenerative diseases. However, the choice of the best DNN architecture may depend on the size of the dataset and the specific goals of the research or application. In general, larger and more complex datasets may benefit from more sophisticated architectures, such as deeper or more advanced models that can capture intricate patterns in the data. Conversely, simpler architectures may be sufficient for smaller or less complex datasets, which suggests that researchers should consider factors such as the dataset size, data complexity, and the specific goals of their study when selecting an appropriate DNN architecture. Additionally, it could be essential to compare different DL techniques applied to the same dataset to determine the most effective approach for achieving the desired outcomes. Such comparisons can provide valuable insights into the relative performances of various techniques to help identify the best methods for specific tasks. By evaluating and contrasting these techniques on identical datasets, researchers can make more informed decisions and enhance the overall effectiveness of their DL applications. However, it is also important to recognize that not all studies may justify such comparisons. Some studies might have specific objectives or constraints that limit the applicability of direct comparisons, which makes it crucial to consider these factors when interpreting the results.