The relatively recent incidence of COVID-19 has arguably accelerated production andinterest in E-health applications. In a physically restricted environment in which patientswere less likely to be able to seek the care they need, these apps could act as a bridgebetween doctor and patient with regards to evaluation, diagnosis and prescription-basedcare. Furthermore, within the context of COVID-19 specifically, these applicationsarguably helped alleviate an already stressed live environment by offering preliminaryevaluations and assurances to patients who might have been forced to visit a clinicalsetting for answers a professional might have been able to answer through live video.Within physiotherapy, range of motion (ROM) defines the degree to which an individualis able to move their joints. Specifically, shoulder-ROM is essential for an individualsday-to-day activities and overall well-being. Evaluation of shoulder-ROM could thereforebe considered of utmost importance in maintaining overall well-being, especially consideringthe senior members of a population.Recent developments in Computer Science (CS), particularly within the field of ArtificialIntelligence (AI) offer exciting options within the prospects of E-health. While developmentswithin generative AI have made definitive impacts on the professional and private settingalike, tools such as computer vision and hearing have already been widely adoptedwithin the context of health-care, offering faster analysis of X-ray images for diagnosisas an example. Efficiency within healthcare is critical, but further entanglement betweenhealth-care and CS also stresses the importance of privacy. The amount of data generatedwithin health-care is considerable, and managing this data securely in order for theend-user to be and feel truly secure will only continue to be of greater importancemoving forward.This study presents a mobile application for the evaluation of shoulder-ROM whilepreserving privacy of the end-user. The full scale of the application is designed asa client-server relationship between the app itself operated by the end-user and anindependent server which stores and retrieves data for the client. This applicationfurther hopes to present a solution for a use-case which is general for the domain ofanalysis of ROM through computer vision.The application produced in this thesis hopes to offer an individual the ability tocomfortably evaluate specific movements pertaining to shoulder-ROM by offering real-timeand progressive feedback. Furthermore, with the ability to save data produced bythe end-user in a privacy-securing manner, the application would give professionalsthe ability to quickly evaluate the shoulder-ROM of their patients. Lastly, the studyitself hopes to prove that producing a seemingly complex application for use withinhealth-care can at this point in time be achieved by relatively ease, and that expansionto new use-cases within the context ROM is more than achievable through the use of modern tools like computer-vision.