Smart phones have become a powerful platform for wearable context recognition. We present a service-based recognition architecture which creates an evolving classification system using feedback from the user community. The approach utilizes classifiers based on fuzzy inference systems which use live annotation to personalize the classifier instance on the device. Our recognition system is designed for everyday use: it allows flexible placement of the device (no assumed or fixed position), requires only minimal personalization effort from the user (1-3 minutes per activity) and is capable of detecting a high number of activities. The components of the service are shown in an evaluation scenario, in which recognition rates up to 97% can be achieved for ten activity classes.

This paper presents a mobile, low-cost particulate matter sensing approach for the use in Participatory Sensing scenarios. It shows that cheap commercial off-the-shelf (COTS) dust sensors can be used in distributed or mobile personal measurement devices at a cost one to two orders of magnitude lower than that of current hand-held solutions, while reaching meaningful accuracy. We conducted a series of experiments to juxtapose the performance of a gauged high-accuracy measurement device and a cheap COTS sensor that we fitted on a Bluetooth-enabled sensor module that can be interconnected with a mobile phone. Calibration and processing procedures using multi-sensor data fusion are presented, that perform very well in lab situations and show practically relevant results in a realistic setting. An on-the-fly calibration correction step is proposed to address remaining issues by taking advantage of co-located measurements in Participatory Sensing scenarios. By sharing few measurement across devices, a high measurement accuracy can be achieved in mobile urban sensing applications, where devices join in an ad-hoc fashion. A performance evaluation was conducted by co-locating measurement devices with a municipal measurement station that monitors particulate matter in a European city, and simulations to evaluate the on-the-fly cross-device data processing have been done.

A variety of studies in the past decades have shown that fine particulate matter can be a serious health hazard, contributing to respiratory and cardiovascular disease. Due to this, more and more regulations defining certain permissible concentration limits have been set by governments around the world. However, current standard measurement equipment is large, expensive and sparsely deployed. Additionally, both the exposure to hazardous conditions and the susceptibility to negative health effects vary from person to person. As a result, we see the need for fine-grained, mobile and distributed measurements, e.g. to identify hot spots or monitor people at risk. Our research investigates the feasibility of particulate matter measurements using cheap, commodity dust sensors which are small enough to be incorporated into mobile devices. This paper first discusses application scenarios which would benefit from inexpensive methods to assess the particulate matter load. Subsequently, commercial-off-the-shelf (COTS) sensors are compared and their general suitability for the application scenarios is examined. Finally, an experimental setup for the evaluation of one of the sensors is presented along with preliminary results.

We present MoA2, a context-aware smartphone app for the ambulatory assessment of mood, tiredness and stress level. In principle, it has two features: (1) mood assessment and (2) mood recognition. The mood assessment system combines benefits of state of the art approaches. The mood recognition is concluded by smartphone-based wearable sensing. In a formative study, we evaluated the usability and unobtrusiveness of our mood assessment. A median SUS score of 90 shows a high usability. Subjects reported an easy, fast and intuitive use. The mood recognition was evaluated in terms of classification accuracy. First, we analyzed which features are best for the recognition. Spatio-temporal attributes, i.e. daytime, day of week and location, correlate most with the monitored mood. Based on the identified attributes, we trained personalized classifiers using Naïve Bayes and applied ten-fold-cross validation. The average recognition accuracy was 0.76 which is comparable to related work.