# The Role of Deep Learning and Gait Analysis in Parkinson's Disease: A Systematic Review
#### 3.3.2 Vision-Based Motion Capture System
All these papers collectively explored the application of markerless pose estimation techniques for analyzing gait patterns in PD patients. They emphasized the use of video-based motion capture methods by employing markerless pose estimation systems, such as OpenPose, DensePose, and CPM to extract movement features. In the studies [62; 63; 64; 65], videos served as the primary data source for assessing motor performance in PD patients. The authors demonstrated the effectiveness of computer vision techniques in capturing the nuanced movement patterns associated with PD achieving accuracy values of over 90% in identifying kinematic measurements of the lower limbs with DensePose and analyzing upper limb sway with OpenPose. In addition, several studies demonstrated that video-based gait analysis systems can effectively correlate with MDS-UPDRS scores, which provides valuable insight into motor symptom severity in PD patients, as shown by the results of [61; 63; 67]. Markerless motion capture systems were also employed for a common focus to analyze gait patterns, specifically in PD patients that experienced FoG symptoms. [PERSON] et al. proposed a method for quantifying gait features and detecting FoG events by extracting the cadence from normal and parkinsonian gait movies recorded with a home video camera. Sequential gait features were obtained from the movies by extracting body joint coordinates using the OpenPose network [68]. In a similar way, [PERSON] et al. proposed a promising novel graph convolutional neural network (GCNN) FoG detection method that involved collecting more than 100 videos from 45 PD patients in a frontal view with an accuracy over 89% [69]. Our systematic research revealed a common crucial point: understanding the distinctive characteristics of walking patterns associated with various neurodegenerative pathologies. Through the application of videos and DL algorithms, these studies delved into the analysis of walking videos to extract pertinent information for the quantification and automatic differentiation of these diseases. In particular, [PERSON] et al. investigated the effectiveness of a vision-based model for predicting gait dysfunction in several neurodegenerative diseases, such as PD and multiple sclerosis, during single and dual trials. The study used two digital cameras, which were positioned to capture the subjects' lower half and foot movements from the front and right side at a frame rate of 30 frames per second [70]. The OpenPose network was first used to locate 2D joint points for each subject, followed by a pose transformation into 3D space. By segmenting the gait steps and identifying heel strikes, several DL algorithms, such as a CNN and recurrent neural network (RNN), were trained and tested to classify the two neurodegenerative diseases. In the single task, the RNN produced the highest accuracy and AUC of 78.1% and 0.87, and the CNN had the highest accuracy of 75% in the dual trials. Similarly, [PERSON] et al. proposed a hybrid system based on a CNN and using videos of 28 patients taken from the front, back, and both sides during walking in order to distinguish PD from multiple sclerosis (MS) [71]. In the study, the data were analyzed using ML techniques and the best accuracy score was obtained as 87.5%. In the same year, [PERSON] et al. studied the gaits of patients with PD and other neurological diseases while walking on a circular path approximately 1 m in diameter [72]. Participants were asked to walk clockwise for two laps and counterclockwise for two laps at their comfortable speed. The Three-Dimensional Pose Tracker for Gait Test (TDPT-GT) application, which is a markerless motion capture system based on ML, was used in combination with an iPhone camera to record and analyze each participant's gait. Gait data were collected using a LightGBM model, which combines a gradient-boosting decision tree with gradient-based one-sided sampling and exclusive feature bundling.