# The Role of Deep Learning and Gait Analysis in Parkinson's Disease: A Systematic Review
#### 3.3.2 Vision-Based Motion Capture System
Figure 10 shows an example of an HPE technique during a single walking test. In 2021, [PERSON] et al. utilized videos captured with commercially available mobile phones as part of routine gait assessments to evaluate motor performance in PD patients [62]. They introduced a markerless pose estimation system designed to extract quantitative gait features relevant to PD. Key body points were extracted from each frame using the OpenPose library, from which six gait features were derived. These features encompassed critical aspects of movement, such as speed, arm swing, postural control, and  
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movement smoothness. The study found a high correlation of 0.80 between the manually labeled and automatically estimated step frequencies. Similarly, [PERSON] et al. introduced a smart system that utilized gait videos from 10 subjects, which consisted of 5 individuals with PD and 5 HCs. The system employed a stereoscopic device equipped with a pair of cameras mounted with co-planar optical planes and co-linear sensor bases, along with pose estimation techniques, to evaluate gait impairments and assess the disease burden [63]. In this preliminary study, the authors proposed a system that would be able to correctly recognize and qualitatively highlight the same alterations assessed by the MDS-UPDRS scores [63]. Additionally, [PERSON] et al. compared peak-to-peak (P-P) data of left and right arm swings in PD patients using a gait analysis system based on OpenPose and video captured by a smartphone camera. They analyzed arm swing asymmetry (ASA) and P-P data from both arms and compared these metrics with the MDS-UPDRS scores. The system demonstrated an accuracy that ranged from 72.73% to 82.35% [64]. Similarly, [PERSON] et al. proposed a markerless strategy for analyzing body segment kinematics to characterize PD during walking, which was captured in sagittal video sequences that used a single camera. They utilized DensePose to generate dynamic pose masks of specific body segments, such as the head, trunk, and limbs. Kinematic patterns were densely captured within each segmented region, and motion trajectories were computed to represent pixel-by-pixel displacement. The approach was validated using various pretrained classification models and evaluated on a dataset of 11 control subjects and 11 PD patients, where it achieved an average accuracy of 99.6% for the lower limbs and head regions [65]. [PERSON] et al. presented a novel method for recognizing the gait of PD patients by recording gait videos using the cameras of two smartphones [66]. Skeletal features were first extracted from the videos using OpenPose. They then applied a weighted adjacency matrix with virtual connection and multi-scale temporal convolution in a spatio-temporal graph convolution network graph convolutional neural network (WM-STGCN), which offers an efficient mechanism for directly learning joint trajectories. The experimental results demonstrated that the proposed method achieved a best accuracy of 87.1%. Similarly, [PERSON] and co-workers [67] assessed the feasibility of extracting movement trajectories from videos of PD patients for detecting and estimating the severity of levodopa-induced dyskinesia by using the convolutional pose machine (CPM), which is a DL-based pose estimation algorithm. The CPM generated a 14-point skeleton, which included key joints, such as the head, neck, shoulders, elbows, wrists, hips, knees, and ankles, with joint  
Figure 10: On the left, an example of the human pose estimation technique during video recording of an individual walking is shown; on the right, landmark positioning based on the skeleton approach is shown. trajectories extracted independently for each frame. They focused on 15 gait kinematic features to train an ML model, which achieved the best results, with an AUC of 0.930 for detection and a correlation coefficient of 0.661 for the severity estimation.