The development of activity recognition techniques relies on the availability of datasets of gestures to train and validate the proposed methods. In this work we introduce and describe a new dataset for activity recognition. The dataset is made up of 8 scenarios from everyday life and includes 17 activities composed of a total of 64 gestures. Each scenario has been repeated 10 times by 2 users. All activities and gestures are labeled. 5 different sensing modalities are implemented by using body worn and environmental sensors and smart objects. The paper describes our considerations in setting up the testbed and performing the experiments to record the dataset, our experiences with recording the data and discusses possible research questions to be tackled with the dataset.
Experiences with experiments in ambient intelligence environments
Elisabetta Farella;
2009-01-01
Abstract
The development of activity recognition techniques relies on the availability of datasets of gestures to train and validate the proposed methods. In this work we introduce and describe a new dataset for activity recognition. The dataset is made up of 8 scenarios from everyday life and includes 17 activities composed of a total of 64 gestures. Each scenario has been repeated 10 times by 2 users. All activities and gestures are labeled. 5 different sensing modalities are implemented by using body worn and environmental sensors and smart objects. The paper describes our considerations in setting up the testbed and performing the experiments to record the dataset, our experiences with recording the data and discusses possible research questions to be tackled with the dataset.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
