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Estimating Human Activities from Smartwatches with Feedforward Neural Networks

  • Sebastián BasterrechEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 535)

Abstract

A key task in the Human Activity Recognition area consists in defining a Machine Learning Classifier. We analyse an approach for modelling the activities using Neural Networks. We consider feedforward networks applied for temporal learning, therefore the network inputs collect information from the past. The input patterns cover information on a time-range window of the past activities, as well as external variables. We evaluate our approach on a well-known dataset and we compare our results with the obtained results in the literature.

Keywords

Human Activity recognition Neural Network Classification Temporal learning 

Notes

Acknowledgement

This work is supported by Grant of SGS No. SP2016/68 and SP2016/97, VŠB–Technical University of Ostrava, Czech Republic.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering and Computer Science, Department of Computer ScienceVŠB-Technical University of OstravaOstravaCzech Republic

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