Artificial Intelligence to Locomotion in Movement Disorders

Parkinson’s disease is a chronic and progressive neurodegenerative disease characterized by a reduction in the number of dopaminergic neurons in the par’s compacta area of the substantia nigra. In this disease, non-motor symptoms manifest initially, and as the disease advances, motor symptoms emerge. However, motor symptoms are a hallmark of the disease. Unfortunately, Parkinson’s disease has no cure and early diagnosis remains a significant challenge. In fact, it is only possible to make a diagnosis when motor symptoms appear, at a stage equivalent to a loss of around 60–70% of dopaminergic neurons. This diagnosis is essentially based on clinical observation and qualitative gait assessment, which may contribute to the existence of inconsistent diagnoses. In this sense, artificial intelligence (AI) offers potential solutions to overcome these limitations, allowing objective and accurate analysis of patients’ movements. More specifically, machine learning, a subset of AI, has gained recognition for its effectiveness in analyzing locomotion in populations affected by movement disorders. Thus, this chapter aims to explore the application of machine learning concepts in the study of movement disorders, with a particular focus on Parkinson’s disease.