TY - GEN
T1 - Wearable computing: Accelerometers’ data classification of body postures and movements
AU - Ugulino, W.
AU - Cardador, D.
AU - Vega, K.
AU - Velloso, E.
AU - Milidiú, R.
AU - Fuks, H.
N1 - Conference code: 21
PY - 2012
Y1 - 2012
N2 - 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%.
AB - 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%.
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-84952063069&partnerID=MN8TOARS
U2 - 10.1007/978-3-642-34459-6_6
DO - 10.1007/978-3-642-34459-6_6
M3 - Conference contribution
SN - 978-3-642-34458-9
SP - 52
EP - 61
BT - Advances in Artificial Intelligence - SBIA 2012
PB - Springer
T2 - 21th Brazilian Symposium on Artificial Intelligence, SBIA 2012
Y2 - 20 October 2012 through 25 October 2012
ER -