# Pose-Based Gait Analysis for Diagnosis of Parkinson's Disease
## 1 Introduction  
The motor skills required to walk without difficulty in different environments are vital for good quality of life. As with gait in general, these motor skills take many years to develop. Parkinson's disease (PD) is a neurodegenerative disorder that is more common in elderly people and affects motor control, flexibility, and adaptability [1]. PD is caused by a decline in the production of dopamine, which negatively affects patients' speech coherence and motor control. However, as is the case with most neurodegenerative disorders, the root cause of PD is unknown [2]. PD is progressive in nature, and if undetected and untreated, symptoms grow worse over time. PD has a stage in which the symptoms required for correct diagnosis are absent or difficult to detect [3]. The main visible signs of PD are reduced movement (hypokinesia) and loss of movement (akinsieia) [1]. These symptoms can be observed as reduced range of motion, tremors in the upper limbs, body rigidity, difficulty in walking, reduced step length, and slower walking speed [4]. Early prediction could aid in treatment and therapy to slow down the degenerative process. This could prevent degradation to a clinical stage, thereby improving quality of life [3]. PD affects the central nervous system by reducing its control of motor neurons. This results in the condition referred to as dysphonia, which is the inability to form words coherently. Hence, PD can be detected using voice features. However, speech features obtained in unconstrained environments are often noisy [2]. Fortunately, PD can be detectedearly using gait features since the loss of motor control results in gait impairment. Early signs of PD shown in gait include reduced arm swing amplitude and symmetry, reduced gait speed, reduced step length, and increased time spent in the double-support phase [5]. Predicting PD from gait requires an appropriate gait capture technique. In general, techniques for capturing gait can be categorized as vision-based or sensor-based [6]. Sensor-based techniques are mostly used in clinical gait analysis and are regarded as the gold standard for PD detection. The sensors used can be wearable, such as inertial measurement units (IMUs) [7]; or non-wearable, such as floor sensors [5]. The main limitation of using sensor-based gait capture is the associated high cost and the technical expertise required for setup. In addition, subjects' consciousness of worn sensors and being actively monitored may further impact their motor function. The studies focusing on PD prediction can be grouped into two main categories based on the features used: studies using non-perceivable features, and those using perceivable features. Examples of non-perceivable features are those extracted from magnetic resonance imaging (MRI) scans of the brain. Such features could be analysed by experts, or prediction could be performed using deep neural networks [8; 9]. PD prediction has also been performed using brain electroencephalogram (EEG) signals [10]. The limitations of these features include the requirement for expert knowledge and the level of interpretability of results obtained. Perceivable features for PD prediction include voice, handwriting, face, and gait. [PERSON] and [PERSON] [2] proposed a neural network approach to predict PD based on voice features. However, speech features obtained in unconstrained environments are often noisy. Handwriting features have also been used in PD prediction [11; 12]. In recent research [13; 14], the face has been shown to contain PD-related features. Of all perceivable features, gait can be obtained unobtrusively in videos, and it has also been used for PD prediction [15; 16; 17], including a computer-vision approach to classifying slowness of movement based on arm motion captured by a smartphone camera [18]. Recent advances in computer vision have enabled the tracking of body parts in a markerless motion capture scenario via human pose estimation (HPE) algorithms such as AlphaPose [19] and OpenPose [20].