@article{ART003152942},
author={Younguk Yun and Jung-woo Sohn},
title={Smart Device-Based Deep Learning Model for Sarcopenia Monitoring and Motion prediction},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2024},
volume={29},
number={12},
pages={65-74}
TY - JOUR
AU - Younguk Yun
AU - Jung-woo Sohn
TI - Smart Device-Based Deep Learning Model for Sarcopenia Monitoring and Motion prediction
JO - Journal of The Korea Society of Computer and Information
PY - 2024
VL - 29
IS - 12
PB - The Korean Society Of Computer And Information
SP - 65
EP - 74
SN - 1598-849X
AB - This study investigates deep learning models for predicting Sarcopenia and motion, such as falls, resulting from Sarcopenia. By leveraging the widespread use of Smartphones, We propose a system that monitors Sarcopenia without the need for additional equipment. A total of 307,584 data points were collected using the built-in 9-axis of IMU sensor of a smartphone, capturing normal walking, abnormal walking, falling, running, and squatting movements. We aims to identify the optimal algorithm through training. To classify Sarcopenia, both binary classification models and multi-class classification models for movement or motion recognition were evaluated. In the binary classification model, the GRU model achieved 100% accuracy, showing the highest performance in both accuracy and speed. For the multi-class classification model, the CNN-GRU combination reached the highest accuracy of 93.72%, and the proposed model demonstrated the fastest training time at 172.16 seconds. This research identifies the optimal combination of deep learning models for motion prediction and detection, and it has potential applications in the fields of digital healthcare and real-time artificial intelligence processing systems.
KW - Sacopenia;Human Activity Recognition;Context Awareness;Deep-Learning;Deep Neural Network;GRU;CNN-CRU
DO -
UR -
ER -
Younguk Yun and Jung-woo Sohn. (2024). Smart Device-Based Deep Learning Model for Sarcopenia Monitoring and Motion prediction. Journal of The Korea Society of Computer and Information, 29(12), 65-74.
Younguk Yun and Jung-woo Sohn. 2024, "Smart Device-Based Deep Learning Model for Sarcopenia Monitoring and Motion prediction", Journal of The Korea Society of Computer and Information, vol.29, no.12 pp.65-74.
Younguk Yun, Jung-woo Sohn "Smart Device-Based Deep Learning Model for Sarcopenia Monitoring and Motion prediction" Journal of The Korea Society of Computer and Information 29.12 pp.65-74 (2024) : 65.
Younguk Yun, Jung-woo Sohn. Smart Device-Based Deep Learning Model for Sarcopenia Monitoring and Motion prediction. 2024; 29(12), 65-74.
Younguk Yun and Jung-woo Sohn. "Smart Device-Based Deep Learning Model for Sarcopenia Monitoring and Motion prediction" Journal of The Korea Society of Computer and Information 29, no.12 (2024) : 65-74.
Younguk Yun; Jung-woo Sohn. Smart Device-Based Deep Learning Model for Sarcopenia Monitoring and Motion prediction. Journal of The Korea Society of Computer and Information, 29(12), 65-74.
Younguk Yun; Jung-woo Sohn. Smart Device-Based Deep Learning Model for Sarcopenia Monitoring and Motion prediction. Journal of The Korea Society of Computer and Information. 2024; 29(12) 65-74.
Younguk Yun, Jung-woo Sohn. Smart Device-Based Deep Learning Model for Sarcopenia Monitoring and Motion prediction. 2024; 29(12), 65-74.
Younguk Yun and Jung-woo Sohn. "Smart Device-Based Deep Learning Model for Sarcopenia Monitoring and Motion prediction" Journal of The Korea Society of Computer and Information 29, no.12 (2024) : 65-74.
