Published Paper Details:

Parkinson's disease (PD) is a common and disabling pathology that is characterized by both motor and non-motor symptoms and affects millions of people worldwide. The disease significantly affects quality of life of those affected. Many works in literature discuss the effects of the disease. The most promising trends involve sensor devices, which are low cost, low power, unobtrusive, and accurate in the measurements, for monitoring and managing the pathology. Objectives: This review focuses on wearable devices for PD applications and identifies five main fields: early diagnosis, tremor, body motion analysis, motor fluctuations (ON-OFF phases), and home and long-term monitoring. The concept is to obtain an overview of the pathology at each stage of development, from the beginning of the disease to consider early symptoms, during disease progression with analysis of the most common disorders, and including management of the most complicated situations. Parkinson's Disease is a nervous system disorder and is progressive in nature. It affects body movements. Symptoms begin with a hardly noticeable tremor in body parts. The disorder results in stiffness and slowing down of movements. It's a debilitating neurodegenerative disease and cannot be diagnosed through blood tests. Parkinson' s disease mostly affects the people above 60 and is one of the common diseases among war veterans. We use ML algorithm XG BOOST to diagnose Parkinson's Disease with maximum accuracy using a Sensor dataset. Machine learning algorithms XG BOOST help to generate useful content from it. To increase the lifespan of elderly people the machine learning algorithms are used to detect diseases in the early stages