Skip to main content
Conference ProceedingsOPEN ACCESS

Indoor actions classification through long short term memory neural networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2017) 10484 LNCS 435-444
DOI: 10.1007/978-3-319-68560-1_39
5Citations
Citations of this article
6Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

This work presents a system based on a recurrent deep neural network to classify actions performed in an indoor environment. RGBD and infrared sensors positioned in the rooms are used as data source. The smart environment the user lives in can be adapted to his/her needs.

Author supplied keywords

Cite

CITATION STYLE

APA

Cipolla, E., Infantino, I., Maniscalco, U., Pilato, G., & Vella, F. (2017). Indoor actions classification through long short term memory neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10484 LNCS, pp. 435–444). Springer Verlag. https://doi.org/10.1007/978-3-319-68560-1_39

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free