Parkinson’s Disease (PD) is a severe neurodegenerative disorder that affects the central nervous system of humans. The lack of specific clinical tests to diagnose Parkinson’s Disease accurately poses a challenge. Parkinson’s disease is characterized by motor symptoms such as tremors, rigidity, and bradykinesia. In addition to these cardinal symptoms, Parkinson’s disease patients exhibit additional motor deficiencies, including handwriting problems and speech impairments. A defining characteristic of Parkinson’s disease, frequently present in its early stages, is hypomimia or a decrease in the expressiveness of the face. Early detection of Parkinson’s disease is essential since it is not a contagious disease, and the timely initiation of therapy can prevent further damage to those affected. However, diagnosing Parkinson’s disease in its early stages is difficult, as its symptoms may be subtle and easily mistaken for other conditions. Moreover, there are currently no specific clinical tests to diagnose Parkinson’s disease accurately. Researchers have proposed various techniques to detect Parkinson’s disease, such as machine learning and deep learning approaches using hand-drawn wave and spiral pictures, voice recordings, and face photographs. However, these methods are still in the experimental stage and have limitations in terms of accuracy and feasibility for clinical use. Therefore, accurate clinical diagnosis is critical for the early detection of Parkinson’s disease. Currently, diagnosis relies on clinical examination and evaluation of motor symptoms by neurologists, and the use of levodopa responsiveness tests to confirm the diagnosis. In recent years, there has been increasing interest in using biomarkers, such as imaging and biological markers, to aid in the diagnosis of Parkinson’s disease.

EasyPark system seeks to diagnose Parkinson disease and its severity level, and this Software Design Description (SDD) will thoroughly detail how it does so. There are seven key sections in the paper. The scope, purpose, target audience, citations, and acronyms for this paper are included in the introduction. The system overview that follows describes the system to which this document relates by outlining the project’s goals, timing, and scope. Additionally, the design perspectives that are subsumed under context, composition, logic, pattern use, algorithm, interaction, and interface viewpoints that employ a variety of diagrams, such as use-case diagram and class diagram, to show the system design. The data design portion, which comprises database design, description, and dataset description, is the fourth section. The fifth part covers how the user will interact with the system and provides illustrations of the user interface and screen visuals. The sixth component is the requirement matrix, which displays the functional requirements that have been fulfilled and those that have not.

EasyPark is a program that overcomes a workload of examinations and testing to diagnose a person. The system contains multiple datasets that were in various research papers, but as for the face images dataset it had to be collected due to not finding any. It offers a very easy way to detect it, one of which is by sketching a spiral wave or a normal one and then submitting it, it can be drawn using the phone itself or on paper and then uploading it. Also recording a voice record saying anything can also detect it. Last but not least by simply taking a picture of the face while being disgusted, happy, and surprised. In the end, all these images and records are taken to apply some machine learning method that detects whether a person has Parkinson’s or not. After detecting the disease, the patient will be able to determine the severity level of the disease, contact doctors to know how to cure it and some physical exercise will be available too. Another thing that the system provides is a questionnaire that the patient answers and the doctors check to get more accurate results.