Collecting complex activity datasets in highly rich networked sensor environments

• Published in: 2010 Seventh International Conference on Networked Sensing Systems pp. 233 - 240
• Main Authors: Roggen, D, Calatroni, A, Rossi, M, Holleczek, T, Forster, K, Troster, G, Lukowicz, P, Bannach, D, Pirkl, G, Ferscha, A, Doppler, J, Holzmann, C, Kurz, M, Holl, G, Chavarriaga, R, Sagha, H, Bayati, H, Creatura, M, del R Millan, Jose
• Format: Conference Proceeding
• Language: English; Japanese
• Published: IEEE 01.06.2010
• Subjects: Activity recognition dataset; Artificial intelligence; Bismuth; Bluetooth; Electrocardiography; Human behavior recognition; Humidity; Lead; Machine learning; Microphones; Pattern classification; Ubiquitous computing; Wearable computing
• ISBN: 9781424479115; 1424479118
• DOI: 10.1109/INSS.2010.5573462

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.