Collecting complex activity data sets in highly rich networked sensor environments

We deployed 15 wireless and wired networked sensor systems comprising 72 sensors of 10 modalities - 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 occurences observed during post-processing, and estimate that over 11000 and 17000 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; less than 2.5% packet were lost lost 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.