Nunavath, V.; Johansen, S.; Johannessen, T.S.; Jiao, L.; Hansen, B.H.; Berntsen, S.; Goodwin, M. Deep Learning for Classifying Physical Activities from Accelerometer Data. Sensors2021, 21, 5564.
Nunavath, V.; Johansen, S.; Johannessen, T.S.; Jiao, L.; Hansen, B.H.; Berntsen, S.; Goodwin, M. Deep Learning for Classifying Physical Activities from Accelerometer Data. Sensors 2021, 21, 5564.
Nunavath, V.; Johansen, S.; Johannessen, T.S.; Jiao, L.; Hansen, B.H.; Berntsen, S.; Goodwin, M. Deep Learning for Classifying Physical Activities from Accelerometer Data. Sensors2021, 21, 5564.
Nunavath, V.; Johansen, S.; Johannessen, T.S.; Jiao, L.; Hansen, B.H.; Berntsen, S.; Goodwin, M. Deep Learning for Classifying Physical Activities from Accelerometer Data. Sensors 2021, 21, 5564.
Abstract
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 physical movement datasets collected from several volunteers who carried tri-axial accelerometer sensors. The first dataset is from the UCI machine learning repository, which contains 14 different activities-of-daily-life (ADL) and is collected from 16 volunteers who carried a single wrist-worn tri-axial accelerometer. The second dataset includes ten other ADLs and is gathered from 8 volunteers who placed the sensors on their hips. Our experiment results show that the RNN model provides the accuracy performance compared to the state-of-the-art methods in classifying the fundamental movement patterns with an overall accuracy of 84.89% and an overall F1-score of 82.56%. Our results indicate that the proposed method will provide the medical doctors and trainers a promising way to precisely track and understand a patient’s physical activities for better treatment.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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