0000000000596003
AUTHOR
Sahand Johansen
Deep Learning for Classifying Physical Activities from Accelerometer Data
Physical inactivity increases the risk of many adverse health conditions, including the world’s major non-communicable diseases, such as coronary heart disease, type 2 diabetes, and breast and colon cancers, shortening life expectancy. There are minimal medical care and personal trainers’ methods to monitor a patient’s actual physical activity types. To improve activity monitoring, we propose an artificial-intelligence-based approach to classify the physical movement activity patterns. In more detail, we employ two deep learning (DL) methods, namely a deep feed-forward neural network (DNN) and a deep recurrent neural network (RNN) for this purpose. We evaluate the proposed models on two phy…
A Deep Learning Approach for Recognizing Daily Movement Patterns through Accelerometer Data
Master's thesis Information- and communication technology IKT590 - University of Agder 2018 Physical activity is a key factor in the treatment of chronic diseases such asdiabetes, cardiovascular disease, and depression. Doctors and personal trainershave limited methods to accurately monitor and classify a patients actual activi-ties based on training diaries and logs that are commonly used today. In this thesis,we apply a tri-axial accelerometer carried by a patient to collect data associated todifferent activities of daily life (ADL) and utilize deep learning (DL) algorithmsfor classifying distinct activities based on the data obtained from the accelerome-ter. Among various DL methods and …