# The Role of Deep Learning and Gait Analysis in Parkinson's Disease: A Systematic Review
## 4 Conclusions  
PD has a significant impact on one's quality of life by affecting various aspects of daily life, such as mobility, communication, autonomy, and mental health. Motor impairments that occur during the disease can be identified through an objective and quantitative gait analysis. This approach can improve clinical practice by aiding in diagnosis, symptom monitoring, rehabilitation, and fall prevention. Traditional methods of diagnosis and evaluation rely on clinical assessment, which can be subjective and imprecise, especially in the early stages of the disease. To overcome these limitations, numerous studies integrated gait analysis with ML and DL approaches to detect and classify PD and its severity [17; 73; 74; 75; 76; 77]. Although various ML algorithms have been used to predict PD, a recent review by [PERSON] et al. discussed and compared these approaches [33]. However, there remains a gap in the literature specifically addressing a comparison of DL algorithms used for similar purposes. For instance, [PERSON] et al. conducted a comprehensive review on DL models for the automated identification of PD that covered a broad range of applications, including brain analysis and motor symptoms, such as handwriting and speech [78]. However, their review did not focus specifically on the integration of DL algorithms with tools for movement assessment. Indeed, the aim of the present review was to present novel DL-based solutions for future research efforts focused on the study of gait patterns in patients with PD using different measurement tools. This includes applications for early disease detection, assessment of disease progression, and the implementation of therapeutic interventions. By using DL models, it was possible to thoroughly analyze motion data from wearable sensors or markerless techniques to identify characteristic patterns associated with PD. These models can process both temporal and spatial motion information, which allows for the detection of subtle changes in motor patterns over time. The topic on which this review focused is emerging, as there has been an increasing production of scientific publications on it from 2018 to 2023, and thus, this represents an initial phase of interest and development of the theme. Considering that 2018 was the first publication year, this confirmed the recent relevance and growing interest in the topic. The authors of the included papers frequently opted to utilize CNNs in their studies. Among the various available architectures, the use of AlexNet proved suitable when the features were time-frequency spectrograms. This choice was guided by AlexNet's reputation for processing complex data through its deep structure and extracting relevant features from images. Although time-frequency spectrograms are not traditional images, they provide a visual representation of spatio-temporal data that can be effectively processed by CNN architectures, like AlexNet. Exploiting an AlexNet pre-trained on a large set of images, the authors achieved significant results by transferring knowledge from the network [51; 52; 58]. Moreover, ResNet has been particularly useful for the long-term analysis of gait signals. In this context, the presence of numerous residual structures has helped to capture complex relationships between motion signals and PD characteristics [58; 70]. In the reviewed papers, we found a widespread utilization of wearables to acquire gait data. Among them, the most commonly utilized were ground force sensors, which are force-sensitive resistors mounted on an insole inside the shoe and powered by a battery. Accelerometers and gyroscopes mounted on shoes or even on the tibia, wrist, or lower back were also employed. In addition, we observed a greater utilization of private datasets in the context of video capture compared with wearables. Overall, the reviewed papers extensively explored the use of video systems, such as a single or multiple cameras in combination with markerless pose estimation techniques for analyzing gait patterns in PD.