publication . Article . Other literature type . 2018

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments

Alejandro Baldominos; Yago Saez; Pedro Isasi;
Open Access English
  • Published: 23 Apr 2018 Journal: Sensors (Basel, Switzerland), volume 18, issue 4 (eissn: 1424-8220, Copyright policy)
  • Publisher: MDPI
  • Country: Spain
Abstract
Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choo...
Subjects
free text keywords: Informática, Article, neuroevolution, deep learning, convolutional neural networks, human activity recognition, Chemical technology, TP1-1185, Electrical and Electronic Engineering, Analytical Chemistry, Atomic and Molecular Physics, and Optics, Biochemistry
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Abstract
Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choo...
Subjects
free text keywords: Informática, Article, neuroevolution, deep learning, convolutional neural networks, human activity recognition, Chemical technology, TP1-1185, Electrical and Electronic Engineering, Analytical Chemistry, Atomic and Molecular Physics, and Optics, Biochemistry
Related Organizations

García, O.; Chamoso, P.; Prieto, J.; Rodríguez, S.; de la Prieta, F. A Serious Game to Reduce Consumption in Smart Buildings. In Highlights of Practical Applications of Cyber-Physical Multi-Agent Systems; Springer International Publishing: Cham, Switzerland, 2017; pp. 481-493.

Canizes, B.; Pinto, T.; Soares, J.; Vale, Z.; Chamoso, P.; Santos, D. Smart City: A GECAD-BISITE Energy Management Case Study. In Proceedings of the Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection-15th International Conference, PAAMS 2017; Springer International Publishing: Cham, Switzerland, 2018; pp. 92-100.

Webb, G.I. Decision tree grafting from the all-tests-but-one partition. In Proceedings of the 16th International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 31 July-6 August 1999; Volume 2, pp. 702-707.

Yang, J.B.; Nguyen, M.N.; San, P.P.; Li, X.L.; Krishnaswamy, S. Deep convolutional neural networks on multichannel time series for human activity recognition. In Proceedings of the 24th International Conference on Artificial Intelligence, Buenos Aires, Argentina, 25-31 July 2015; pp. 3995-4001.

Vinyard, J. Efficient Overlapping Windows with Numpy, 2012. Available online: http://www.johnvinyard.

com/blog/?p=268 (accessed on 23 February 2017).

In Proceedings of the 1st European Workshop on Genetic Programming; Lecture Notes in Computer Science; Springer: New York, NY, USA, 1998; Volume 1391, pp. 83-95.

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