Parkinson’s disease is a chronic, progressive process caused by the degeneration of the substantia nigra, leading to reduced dopamine levels and the appearance of motor symptoms—the most common and well known by the public. But neurodegeneration begins before these symptoms manifest, so understanding non-motor symptoms and other signs that, because the disease affects both the central and peripheral nervous systems, may precede or accompany the more typical symptoms, becomes increasingly important. The diagnosis of Parkinson’s is eminently clinical, i.e., it is made by the neurologist from the symptoms and their evolution, since supplementary tests help but do not provide a diagnosis. Objective monitoring of fluctuations and of motor and non-motor symptoms in Parkinson’s disease would be a notable advance in clinical practice, enabling personalized follow-up and adjustment of medication to individual patient needs. It would also open the possibility of new improvements in treatment, diagnosis, and the generation of reports and automatic alarms. The project therefore proposes a system based primarily on artificial vision capable of automatically detecting different involuntary facial expressions and motor features that evidence the disease or changes in its progression.