TY  - CONF
ID  - Calatroni2011_SMC
T1  - Collection and curation of a large reference dataset for activity recognition
A1  - Calatroni, Alberto
A1  - Roggen, Daniel
A1  - Tröster, Gerhard
TI  - Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on
Y1  - 2011
SP  - 30
EP  - 35
SN  - 1062-922X
M2  - doi: 10.1109/ICSMC.2011.6083638
KW  - Activity Recognition
KW  - data collection
KW  - data curation
KW  - data synchronization
KW  - labeling
KW  - OPPORTUNITY
N2  - The field of research on activity recognition is relatively young compared to others, like computer vision. In more mature fields, algorithms are usually tested on standardized, reference datasets. This way, algorithms coming from different groups can be tested in a fair manner, which accelerates the process of developing new knowledge. Collecting a reference dataset under realistic settings for activity recognition poses many challenges due to the large amount of sensors and sensor modalities which are needed to provide a sufficiently complete playground. We here report on some lessons learned while collecting such a reference dataset with a heterogeneous setup. We argue for the importance of a few principles to obtain a clean dataset, starting from the sampling and acquisition, down to the synchronization and labeling of the data.
ER  -