Classifier that predicts activity class based on wearable sensor data. Source: http://groupware.les.inf.puc-rio.br/har
This classifier predicts somebody’s activity class (sitting, standing up, standing, sitting down, walking). It is based on the Human Activity Recognition dataset. Human Activity Recognition (HAR) is an active research area, results of which have the potential to benefit the development of assistive technologies in order to support care of the elderly, the chronically ill and people with special needs. Activity recognition can be used to provide information about patients’ routines to support the development of e-health systems. Two approaches are commonly used for HAR: image processing and use of wearable sensors. In this case we will use information generated by wearable sensors (Ugulino et al, 2012). The complete description of this experiment can be found here: http://www.md2c.nl/how-to-build-a-human-activity-classifier-with-azure-machine-learning/ This experiment needs the -cleaned- HAR dataset (http://groupware.les.inf.puc-rio.br/static/har/dataset-har-PUC-Rio-ugulino.zip) and the corrplot R package (http://cran.r-project.org/web/packages/corrplot/index.html) Sources: Ugulino, W.; Cardador, D.; Vega, K.; Velloso, E.; Milidiu, R.; Fuks, H. Wearable Computing: Accelerometers' Data Classification of Body Postures and Movements. Proceedings of 21st Brazilian Symposium on Artificial Intelligence. Advances in Artificial Intelligence - SBIA 2012. In: Lecture Notes in Computer Science. , pp. 52-61. Curitiba, PR: Springer Berlin / Heidelberg, 2012. ISBN 978-3-642-34458-9. DOI: 10.1007/978-3-642-34459-6_6.