Activity Recognition Using Wearable Sensors for Elder Care
FGCN '08 Proceedings of the 2008 Second International Conference on Future Generation Communication and Networking - Volume 02
Unsupervised Adaptation to On-body Sensor Displacement in Acceleration-Based Activity Recognition
ISWC '11 Proceedings of the 2011 15th Annual International Symposium on Wearable Computers
Unsupervised Activity Recognition with User's Physical Characteristics Data
ISWC '11 Proceedings of the 2011 15th Annual International Symposium on Wearable Computers
Accelerometer Placement for Posture Recognition and Fall Detection
IE '11 Proceedings of the 2011 Seventh International Conference on Intelligent Environments
A New Multi-task Learning Method for Personalized Activity Recognition
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Research on Classification of Human Daily Activities Based on a Single Tri-Axial Accelerometer
IWCDM '11 Proceedings of the 2011 First International Workshop on Complexity and Data Mining
Hi-index | 0.00 |
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%.