DocumentCode :
636507
Title :
Comparing metabolic energy expenditure estimation using wearable multi-sensor network and single accelerometer
Author :
Bo Dong ; Biswas, Santosh ; Montoye, Alexander ; Pfeiffer, Klaus
Author_Institution :
Dept. of Electr. & Comput. Eng., Michigan State Univ., East Lansing, MI, USA
fYear :
2013
fDate :
3-7 July 2013
Firstpage :
2866
Lastpage :
2869
Abstract :
This paper presents the implementation details, system architecture and performance of a wearable sensor network that was designed for human activity recognition and energy expenditure estimation. We also included ActiGraph GT3X+ as a popular single sensor solution for detailed comparison with the proposed wearable sensor network. Linear regression and Artificial Neural Network are implemented and tested. Through a rigorous system study and experiment, it is shown that the wearable multi-sensor network outperforms the single sensor solution in terms of energy expenditure estimation.
Keywords :
accelerometers; biochemistry; biomedical measurement; body sensor networks; neural nets; pattern recognition; regression analysis; wearable computers; ActiGraph GT3X+; Artificial Neural Network; Linear regression; human activity recognition; metabolic energy expenditure estimation; single accelerometer; single sensor solution; system architecture; wearable sensor network performance; Accelerometers; Artificial neural networks; Biomedical monitoring; Estimation; Feature extraction; Linear regression; Wearable sensors; Activity Recognition; Energy Expenditure Estimation; Wearable Sensor Network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE
Conference_Location :
Osaka
ISSN :
1557-170X
Type :
conf
DOI :
10.1109/EMBC.2013.6610138
Filename :
6610138
Link To Document :
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