Activity recognition system using inbuilt sensors of smart mobile phone and minimizing feature vectors

Author:

Acharjee Dulal,Mukherjee Amitava,Mandal J. K.,Mukherjee Nandini

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Hardware and Architecture,Condensed Matter Physics,Electronic, Optical and Magnetic Materials

Reference21 articles.

1. Acharjee D, Mukherjee A, Mukherjee N (2012) Computing aspects of monitoring walking disorder using body sensor network and neural network. The 2nd IEEE international conference on ‘parallel, distributed and grid computing-PDGC2012’. http://ieeeXplore.ieee.org

2. Aiello F, Bellifemine FL, Fortino G, Galzarano S, Gravina R (2011) An agent-based signal processing in-node environment for real-time human activity monitoring based on wireless body sensor networks. Eng Appl Artif Intell. Elsevier Pub. 24:1147–1161. doi: 10.1016/j.engappai.2011.06.007

3. Autoregression coefficient with burg order 4 follow: http://paulbourke.net/miscellaneous/ar/ . Accessed on 24 June 2014

4. Bao L, Intille SS (2004) Activity recognition from user-annotated acceleration data. Pervasive. Springer-Verlag Berlin Heidelberg, LNCS 3001. pp 1–17

5. Davide A, Ghio A, Oneto L, Parra X, Reyes-Ortiz JL (2012) Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. International Workshop of Ambient Assisted Living (IWAAL). Vitoria-Gasteiz, Spain

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