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

Open Access
Conference Proceedings
Authors: Sergio StaabLudger Martin

Abstract: 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. Thus, smartwatches provide an unobtrusive way to measure data.These devices have the following functions: global positioning system, accelerometer, inclinometer, gyroscope, magnetometer, heart rate monitor, oximetry sensor, skin conductivity sensor, and skin temperature sensor.The goal of this project is to gain knowledge about locating, providing, and documenting motor skills during the course of dementia.Caregivers will document patient activities while wearing the watch. Data from the aforementioned sensors are sent to the database at 20 data packets per second via a socket.DecisionTreeClassifier, KNeighborsClassifier, logistic regression, GaussianNB, RandomForestClassifier, Support Vector Machine, and Multilayer Perceptron classification algorithms are used. The test and training data are generated from different subjects to eliminate possible overfitting.The system transmits the labeled data on a six-second frequency (beams), in which 120 data sets are compressed from the previously mentioned sensors. Thus, it is possible to detect minute changes in arm positions. This methodology, after a six-month series of training and testing runs, shows results of over 90% probability for arm positions and over 80% probability for very fine granular activities (reading, playing games, eating), depending on the classification algorithm.

Keywords: Health Informatics, Human Motion Analysis, Machine Learning

DOI: 10.54941/ahfe1001024

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