Abstract
This paper deals with methods for identification of drone activities based on its sensor data. Several unsupervised and supervised approaches are proposed and tested for the task of activity analysis. We demonstrate that sensor data, although quite correlated, are still prone to standard dimensionality reduction techniques that in fact make the problem hard for unsupervised methods. On the other hand, a supervised model based on deep neural network is capable of learning the task from human operator data reformulated as a classification problem.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kwon, Y., Kang, K., Bae, C.: Unsupervised learning for human activity recognition using smartphone sensors. Exp. Syst. Appl. 41(14), 6067–6074 (2014)
Barták, R., Vomlelová, M.: Using machine learning to identify activities of a flying drone from sensor readings. In: Rus, V., Markov, Z. (eds.) Proceedings of the Thirtieth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2017, Marco Island, Florida, USA, 22–24 May 2017, pp. 436–441. AAAI Press (2017)
Bugdol, M.D., Mitas, A.W., Grzegorzek, M., Meyer, R., Wilhelm, C.: Human Activity Recognition Using Smartphone Sensors, pp. 41–47. Springer, Cham (2016)
Flach, P.: Machine Learning: The Art and Science of Algorithms That Make Sense of Data. Cambridge University Press, New York (2012)
Goodfellow, I.J., Bengio, Y., Courville, A.C.: Deep Learning. Adaptive Computation and Machine Learning, MIT Press, Cambridge (2016). http://www.deeplearningbook.org/
Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics, 2nd edn. Springer, New York (2009). http://www.worldcat.org/oclc/300478243
Krajník, T., Vonásek, V., Fišer, D., Faigl, J.: AR-drone as a platform for robotic research and education. In: Obdržálek, D., Gottscheber, A. (eds.) EUROBOT 2011. CCIS, vol. 161, pp. 172–186. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21975-7_16
Lara, O.D., Labrador, M.A.: A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutorials 15(3), 1192–1209 (2013)
Müller, A.C., Guido, S.: Introduction to Machine Learning with Python: A Guide for Data Scientists. O’Reilly Media, Inc., Sebastopol (2016)
Wooldridge, M.J.: An Introduction to MultiAgent Systems, 2nd edn. Wiley, New York (2009)
Acknowledgement
This research is supported by the Czech Science Foundation under the project P103-15-19877S.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Neruda, R., Pilát, M., Moudřík, J. (2017). Unsupervised and Supervised Activity Analysis of Drone Sensor Data. In: Figueroa-García, J., López-Santana, E., Villa-Ramírez, J., Ferro-Escobar, R. (eds) Applied Computer Sciences in Engineering. WEA 2017. Communications in Computer and Information Science, vol 742. Springer, Cham. https://doi.org/10.1007/978-3-319-66963-2_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-66963-2_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-66962-5
Online ISBN: 978-3-319-66963-2
eBook Packages: Computer ScienceComputer Science (R0)