A low-complexity activity classification algorithm with optimized selection of accelerometric features-Reference-Cited by-同舟云学术

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A low-complexity activity classification algorithm with optimized selection of accelerometric features

Published:2016-11-08 Issue:6 Volume:8 Page:681-695
ISSN:1876-1372
Container-title:Journal of Ambient Intelligence and Smart Environments
language:
Short-container-title:AIS

Author:

Giuberti Matteo¹,Ferrari Gianluigi²

Affiliation:

1. Xsens Technologies B.V., Pantheon 6a, 7521 PR, Enschede, The Netherlands. E-mail: matteo.giuberti@xsens.com

2. Department of Information Engineering, University of Parma, Parco Area delle Scienze, I-43123, Parma, Italy. E-mail: gianluigi.ferrari@unipr.it

Publisher

IOS Press

Subject

Software

Reference22 articles.

1. A compact, high-speed, wearable sensor network for biomotion capture and interactive media

2. Activity Recognition from User-Annotated Acceleration Data

3. Unsupervised adaptation for acceleration-based activity recognition: Robustness to sensor displacement and rotation;Chavarriaga;Personal and Ubiquitous Computing,2013

4. The opportunity challenge: A benchmark database for on-body sensor-based activity recognition;Chavarriaga;Pattern Recognition Letters,2013

5. R.O. Duda, P.E. Hart and D.G. Stork, Pattern Classification and Scene Analysis, 2nd edn, Wiley-Interscience, New York, NY, USA, 2000.

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