Abstract-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.

Opportunistic sensing allows to efficiently collect information about the physical world and the persons behaving in it. This may mainstream human context and activity recognition in wearable and pervasive computing by removing requirements for a specific deployed infrastructure. In this paper we introduce the newly started European research project OPPORTUNITY within which we develop mobile opportunistic activity and context recognition systems. We outline the project's objective, the approach we follow along opportunistic sensing, data processing and interpretation, and autonomous adaptation and evolution to environmental and user changes, and we outline preliminary results.

Fluid intake is an important information for many health and assisted living applications. At the same time it is inherently difficult to monitor. Existing reliable solutions require augmented drinking containers, which severely limits the applicability of such systems. In this paper we investigate two key components of an unobtrusive, wearable solution that is independent of a particular drinking container or environment.We first describe a system for spotting individual instances of drinking (lifting a container to the mouth and taking a single sip) in a continuous stream of data from a wrist-worn acceleration sensor. We show that drinking motion can be detected across different drinking containers (glass, cup, large beer mug, bottle) on a large dataset (560 drinking motion instances from six users, embedded in 5.84 hours of complex natural activities). An average performance of 84% recall at 94% precision was achieved for the drinking motion spotting.Based on the events derived from drinking event spotting, we show how additional information can be obtained. Specifically, we demonstrate the recognition of container types and fluid level from upper body postures during drinking events. Nine containers and three container fluid levels were evaluated to recognize container type and fluid amounts with three users.Recognition rate for container type was 75%, and for fluid level 72%.

No abstract

We describe the design, implementation, and evaluation of an indoor positioning system based on resonant magnetic coupling. The system has an accuracy of less than 1 m 2 and, because of the underlying physical principle, is robust with respect to disturbances such as people moving around or changes in room configuration. It consists of 16x16x16 cm transmitter coils, each able to cover an area of up to 50 m 2 , and provides location information to an arbitrary number of mobile receivers with an update rate of up to 30Hz. We evaluate the actual accuracy of the positioning with a robotic arm and show quantitatively that even large metallic objects have little effect on the signal. We then present an elaborate study of the performance of our system for the recognition of abstract locations such as "at the table", "in front of a cabinet". It comprises four different sites with a total of 100 individual locations some as little as 50 cm apart.