Saving Human Lives: What Complexity Science and Information Systems can Contribute

We discuss models and data of crowd disasters, crime, terrorism, war and disease spreading to show that conventional recipes, such as deterrence strategies, are often not effective and sufficient to contain them. Many common approaches do not provide a good picture of the actual system behavior, because they neglect feedback loops, instabilities and cascade effects. The complex and often counter-intuitive behavior of social systems and their macro-level collective dynamics can be better understood by means of complexity science. We highlight that a suitable system design and management can help to stop undesirable cascade effects and to enable favorable kinds of self-organization in the system. In such a way, complexity science can help to save human lives.Comment: 67 pages, 25 figures; accepted for publication in Journal of Statistical Physics [for related work see http://www.futurict.eu/

Scalable Recognition of Daily Activities with Wearable Sensors

Abstract. High-level and longer-term activity recognition has great potentials in areas such as medical diagnosis and human behavior modeling. So far however, activity recognition research has mostly focused on lowlevel and short-term activities. This paper therefore makes a first step towards recognition of high-level activities as they occur in daily life. For this we record a realistic 10h data set and analyze the performance of four different algorithms for the recognition of both low- and high-level activities. Here we focus on simple features and computationally efficient algorithms as this facilitates the embedding and deployment of the approach in real-world scenarios. While preliminary, the experimental results suggest that the recognition of high-level activities can be achieved with the same algorithms as the recognition of low-level activities.

A Tutorial on Human Activity Recognition Using Body-worn Inertial Sensors

The last 20 years have seen an ever increasing research activity in the field of human activity recognition. With activity recognition having considerably matured so did the number of challenges in designing, implementing and evaluating activity recognition systems. This tutorial aims to provide a comprehensive hands-on introduction for newcomers to the field of human activity recognition. It specifically focuses on activity recognition using on-body inertial sensors. We first discuss the key research challenges that human activity recognition shares with general pattern recognition and identify those challenges that are specific to human activity recognition. We then describe the concept of an activity recognition chain (ARC) as a general-purpose framework for designing and evaluating activity recognition systems. We detail each component of the framework, provide references to related research and introduce the best practise methods developed by the activity recognition research community. We conclude with the educational example problem of recognising different hand gestures from inertial sensors attached to the upper and lower arm. We illustrate how each component of this framework can be implemented for this specific activity recognition problem and demonstrate how different implementations compare and how they impact overall recognition performance

Smart crowds in smart cities: Real life, city scale deployments of a smartphone based participatory crowd management platform

We describe a platform for smart, city-wide crowd management based on participatory mobile phone sensing and location/situation specific information delivery. The platform supports quick and flexible deployments of end-to-end applications for specific events or spaces that include four key functionalities: (1) Mobile phone based delivery of event/space specific information to the users, (2) participatory sensor data collection (from app users) and flexible analysis, (3) location and situation specific message multicast instructing people in different areas to act differently in case of an emergency and (4) post mortem event analysis. This paper describes the requirements that were derived through a series of test deployments, the system architecture, the implementation and the experiences made during real life, large scale deployments. Thus, until today it has been deployed at 14 events in three European countries (UK, Netherlands, Switzerland) and was used by well over 100,000 people.ISSN:1867-4828ISSN:1869-023

Mobile Computing, Applications, and Services5th International Conference, MobiCASE 2013, Paris, France, November 7-8, 2013, Revised Selected Papers /

XVI, 334 p. 127 illus.online resource

Characterizing Sleeping Trends from Postures

We present an approach to model sleeping trends, using a light-weight setup to be deployed over longer time-spans and with a minimum of maintenance by the user. Instead of characterizing sleep with traditional signals such as EEG and EMG, we propose to use sensor data that is a lot weaker, but also less invasive and that can be deployed unobtrusively for longer periods. By recording wrist-worn accelerometer data during a 4-week-long study, we explore in this poster how sleeping trends can be characterized over long periods of time by using sleeping postures only