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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
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Authors
Yongwon Jang, Yoonseon Song, Hyung Wook Noh, Seunghwan Kim
Issue Date
2015-08
Citation
International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2015, pp.2860-2863
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/EMBC.2015.7318988
Project Code
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.
KSP Keywords
3-axis accelerometer, Activity Detection, Activity monitoring, Activity type, Artificial Neural Network, Cascaded network, Energy expenditure estimation, Network performance, Overall accuracy, calorie consumption, consumption data