Analyzing a person's gait is important in determining their physical and neurological health. However, typical motion analysis laboratories are only in urban specialty care facilities and can be expensive due to the specialized personnel and technology needed for these examinations. Many patients, especially those who reside in underdeveloped or isolated locations, find it impractical to go to such facilities. With the help of recent developments in high-performance computing and artificial intelligence models, it is now feasible to evaluate human movement using digital video. Over the past 20 years, various visual gait analysis tools and scales have been developed. A study of the literature and discussions with physicians who are domain experts revealed that the Edinburgh Visual Gait Score (EVGS) is one of the most effective scales currently available. Clinical implementations of EVGS currently rely on human scoring of videos. In this thesis, an algorithmic implementation of EVGS scoring based on hand-held smart phone video was implemented. Walking gait was recorded using a handheld smartphone at 60Hz as participants walked along a hallway. Body keypoints representing joints and limb segments were then identified using the OpenPose - Body 25 pose estimation model. A new algorithm was developed to identify foot events and strides from the keypoints and determine EVGS parameters at relevant strides. The stride identification results were compared with ground truth foot events that were manually labeled through direct observation, and the EVGS results were compared with evaluations by human scorers. Stride detection was accurate within 2 to 5 frames. The level of agreement between the scorers and the algorithmic EVGS score was strong for 14 of 17 parameters. The algorithm EVGS results were highly correlated to scorers' scores (r>0.80) for eight of the 17 factors. Smartphone-based remote motion analysis with automated implementation of the EVGS may be employed in a patient's neighborhood, eliminating the need to travel. These results demonstrated the viability of automated EVGS for remote human motion analysis.