Conference Paper
A Basic Study of Activity Type Detection and Energy Expenditure Estimation for Children and Youth in Daily Life Using 3-axis Accelerometer and 3-Stage Cascaded Artificial Neural Network
14NC5300, Development of smart calorie tracking technology and service business models for obesity management in child and youth,
Seunghwan Kim
Abstract
It is important to prevent obesity in childhood given that many obese adults have been obese since childhood. An activity monitor could provide an effective aid in preventing obesity if it records not only the calorie assessment but also activity detection to check how active a child is in daily life. The current study is for activity monitoring algorithm and we designed 3-stage cascaded artificial neural network. To develop the algorithm, we recruited 76 participants, made 3-axis accelerometer for them, and acquired activity data and calorie consumption data through them. Finally, we designed 3-stage cascaded network to classify the activities and to assess energy consumption. The 3-stage network classifies 4 activities of walking, running, stairs moving, and jumping rope with overall accuracy of 94.70%, and predicts calorie consumption with average accuracy of 81.91%, which is better than the results of the 2-stage network. Future work would include the enhancement of the network performance.
The materials provided on this website are subject to copyrights owned by ETRI and protected by the Copyright Act. Any reproduction, modification, or distribution, in whole or in part, requires the prior explicit approval of ETRI. However, under Article 24.2 of the Copyright Act, the materials may be freely used provided the user complies with the following terms:
The materials to be used must have attached a Korea Open Government License (KOGL) Type 4 symbol, which is similar to CC-BY-NC-ND (Creative Commons Attribution Non-Commercial No Derivatives License). Users are free to use the materials only for non-commercial purposes, provided that original works are properly cited and that no alterations, modifications, or changes to such works is made. This website may contain materials for which ETRI does not hold full copyright or for which ETRI shares copyright in conjunction with other third parties. Without explicit permission, any use of such materials without KOGL indication is strictly prohibited and will constitute an infringement of the copyright of ETRI or of the relevant copyright holders.
J. Kim et. al, "Trends in Lightweight Kernel for Many core Based High-Performance Computing", Electronics and Telecommunications Trends. Vol. 32, No. 4, 2017, KOGL Type 4: Source Indication + Commercial Use Prohibition + Change Prohibition
J. Sim et.al, “the Fourth Industrial Revolution and ICT – IDX Strategy for leading the Fourth Industrial Revolution”, ETRI Insight, 2017, KOGL Type 4: Source Indication + Commercial Use Prohibition + Change Prohibition
If you have any questions or concerns about these terms of use, or if you would like to request permission to use any material on this website, please feel free to contact us
KOGL Type 4:(Source Indication + Commercial Use Prohibition+Change Prohibition)
Contact ETRI, Research Information Service Section
Privacy Policy
ETRI KSP Privacy Policy
ETRI does not collect personal information from external users who access our Knowledge Sharing Platform (KSP). Unathorized automated collection of researcher information from our platform without ETRI's consent is strictly prohibited.
[Researcher Information Disclosure] ETRI publicly shares specific researcher information related to research outcomes, including the researcher's name, department, work email, and work phone number.
※ ETRI does not share employee photographs with external users without the explicit consent of the researcher. If a researcher provides consent, their photograph may be displayed on the KSP.