Movement Recognition to Analyze Disease-Related Changes in Motor Skills of Dementia Patients

Currently, about 46.8 million people worldwide have dementia. More than 7.7 million new cases occur every year. Causes and triggers of the disease are currently unknown and a cure is not available. This makes dementia, along with cancer, one of the most dangerous diseases in the world. In the field of dementia care, this work attempts to use machine learning to classify the activities of individuals with dementia in order to track and analyze disease progression and detect disease-related changes as early as possible. In collaboration with several care communities, exercise data is measured using the Apple Watch Series 6. Consultation with several care teams that work with dementia patients on a daily basis revealed that many dementia patients wear watches. In this project data from the aforementioned sensors is sent to the database at 20 data packets per second (20 Hz) via a socket. Fast Forest, Logistic Regression and Support Vector Machine classification algorithms are used to gain knowledge about locating, providing, and documenting motor skills during the course of dementia.