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Human Activity Recognition Using Smartphones Data Set

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The Benchmark has the following main features:
• Dataset has been built from the recordings of 30 subjects performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors.
• The daily living activities performed by the subjects are: WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING.
• Dataset is composed by around 10000 instances of multivariate time-series.
• For each record, it is provided its activity label, an identifier of the subject who carried out the experiment, triaxial acceleration from the phone’s accelerometer and the estimated body acceleration, triaxial angular velocity from the gyroscope, and a 561-features vector with time and frequency domain variables.
• Dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data.

Scientific Area:
Machine Learning

C, C++, MatLab, Octave, Python, R

Target Group:

Cite as:
Anguita, D. and Ghio, A. and Oneto, L. and Parra, X. and Reyes-Ortiz, J. L., Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine, International Workshop of Ambient Assisted Living, (2012): 216-223.

Anguita, D. and Ghio, A. and Oneto, L. and Parra, X. and Reyes-Ortiz, J. L., A Public Domain Dataset for Human Activity Recognition Using Smartphones, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, (2013): 437-442.

Author of the review:
Giulia Cademartori
University of Genoa


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