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WearableAccelerometersDataset

Wearable Computing: Classification of Body Postures and Movements (PUC-Rio) Data Set

Contact: Ugulino - Submitted: 2013-07-30 03:58 - Views : 2611 - [Edit entry]
  • Authors: Wallace Ugulino, Débora Cardador, Katia Vega, Eduardo Velloso, Ruy Milidiu, Hugo Fuks
  • Key facts: Attribute Information:

    Detailed information in: http://groupware.les.inf.puc-rio.br/har
    user (text)
    gender (text)
    age (integer)
    how_tall_in_meters (real)
    weight (int)
    body_mass_index (real)
    x1 (type int, contains the read value of the axis 'x' of the 1st accelerometer, mounted on waist)
    y1 (type int, contains the read value of the axis 'y' of the 1st accelerometer, mounted on waist)
    z1 (type int, contains the read value of the axis 'z' of the 1st accelerometer, mounted on waist)
    x2 (type int, contains the read value of the axis 'x' of the 2nd accelerometer, mounted on the left thigh)
    y2 (type int, contains the read value of the axis 'y' of the 2nd accelerometer, mounted on the left thigh)
    z2 (type int, contains the read value of the axis 'z' of the 2nd accelerometer, mounted on the left thigh)
    x3 (type int, contains the read value of the axis 'x' of the 3rd accelerometer, mounted on the right ankle)
    y3 (type int, contains the read value of the axis 'y' of the 3rd accelerometer, mounted on the right ankle)
    z3 (type int, contains the read value of the axis 'z' of the 3rd accelerometer, mounted on the right ankle)
    x4 (type int, contains the read value of the axis 'x' of the 4th accelerometer, mounted on the right upper-arm)
    y4 (type int, contains the read value of the axis 'y' of the 4th accelerometer, mounted on the right upper-arm)
    z4 (type int, contains the read value of the axis 'z' of the 4th accelerometer, mounted on the right upper-arm)
  • Keywords: decision.tree, classification, multiclass, scalar, numeric, markov.decision.processes
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Abstract:

During the last 5 years, research on Human Activity Recognition (HAR) has reported on systems showing good overall recognition performance. As a consequence, HAR has been considered as a potential technology for e-health systems. Here, we propose a machine learning based HAR classifier. We also provide a full experimental description that contains the HAR wearable devices setup and a public domain dataset comprising 165,633 samples. We consider 5 activity classes, gathered from 4 subjects wearing accelerometers mounted on their waist, left thigh, right arm, and right ankle. As basic input features to our classifier we use 12 attributes derived from a time window of 150ms. Finally, the classifier uses a committee AdaBoost that combines ten Decision Trees. The observed classifier accuracy is 99.4%.

Read more: http://groupware.les.inf.puc-rio.br/work.jsf?p1=10335#ixzz2aUPirY4k

Comments / Questions / Answers

#1 Wallace Ugulino 2013-07-30 03:59:43

testing

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