TY  - CONF
ID  - Roggen10INSS
T1  - Collecting complex activity datasets in highly rich networked sensor environments
A1  - Roggen, Daniel
A1  - Calatroni, Alberto
A1  - Rossi, Mirco
A1  - Holleczek, Thomas
A1  - Förster, Kilian
A1  - Tröster, Gerhard
A1  - Lukowicz, Paul
A1  - Bannach, David
A1  - Pirkl, Gerald
A1  - Ferscha, Alois
A1  - Doppler, Jacob
A1  - Holzmann, Clemens
A1  - Kurz, Marc
A1  - Holl, G.
A1  - Chavarriaga, Ricardo
A1  - Sagha, Hesam
A1  - Bayati, Hamidreza
A1  - Creatura, Marco
A1  - del R. Millán, José
TI  - Networked Sensing Systems (INSS), 2010 Seventh International Conference on
Y1  - 2010
SP  - 233
EP  - 240
M2  - doi: 10.1109/INSS.2010.5573462
KW  - OPPORTUNITY
N2  - We deployed 72 sensors of 10 modalities in 15 wireless and wired networked sensor systems in the environment, in objects, and on the body to create a sensor-rich environment for the machine recognition of human activities. We acquired data from 12 subjects performing morning activities, yielding over 25 hours of sensor data. We report the number of activity occurrences observed during post-processing, and estimate that over 13000 and 14000 object and environment interactions occurred. We describe the networked sensor setup and the methodology for data acquisition, synchronization and curation. We report on the challenges and outline lessons learned and best practice for similar large scale deployments of heterogeneous networked sensor systems. We evaluate data acquisition quality for on-body and object integrated wireless sensors; there is less than 2.5% packet loss after tuning. We outline our use of the dataset to develop new sensor network self-organization principles and machine learning techniques for activity recognition in opportunistic sensor configurations. Eventually this dataset will be made public.
ER  -