Catálogo de publicaciones - libros

Mobile devices are more and more used for mobile interactions with things, places and people in the real world. However, so far no studies have discussed which interaction techniques are preferred by users in different contexts. This paper presents an experimental comparison of four different physical mobile interaction techniques: touching, pointing, scanning and user-mediated object interaction. To evaluate these techniques across different scenarios and to collect real usage data, four prototypes were implemented: a system for mobile interaction in smart environments, a mobile tourist guide, a mobile museum guide and a prototype for mobile interaction with advertisement posters. In each setting an experimental comparison was performed. Based on the results of these studies, which involved over 60 participants in total, advantages and disadvantages of these interaction techniques are described. Context-specific user preferences are presented for the interaction techniques, to help application designers and developers decide which interaction technique(s) to integrate into their application and which consequences this decision has.

A low-cost kit of stick-on wireless sensors that transmit data indicating whenever various objects are being touched or used might aid ubiquitous computing research efforts on rapid prototyping, context-aware computing,and ultra-dense object sensing, among others. Ideally, the sensors would besmall, easy-to-install, and affordable. The sensors would reliably recognize when specific objects are manipulated, despite vibrations produced by the usage of nearby objects and environmental noise. Finally, the sensors would operate continuously for several months, or longer. In this paper, we discuss the challenges and practical aspects associated with creating such "object usage" sensors. We describe the existing technologies used to recognize object usage and then present the design and evaluation of a new stick-on, wireless object usage sensor. The device uses (1) a simple classification rule tuned to differentiate real object usage from adjacent vibrations and noise in real-time based on data collected from a real home, and (2) two complimentary sensors to obtain good battery performance. Results of testing 168 of the sensors in an instrumented home for one month of normal usage are reported as well as results from a 4-hour session of a person busily cooking and cleaning in the home, where every object usage interaction was annotated and analyzed.

The development of engaging user interfaces that support collaboration is a great challenge – in particular if users are children. We consider mobile phones as appropriate devices for multi-user interactions with a system because novel forms of physical mobile interaction techniques with smart objects yield lots of benefits, such as being intuitive and playful to use, but also addressing children’s needs for curiosity. In this paper, we introduce our approach to multi-user game-like scenarios within an ambient intelligence context which are controlled via different mobile phones and their supported interaction techniques. By providing children with multiple mobile phones we structure interactions in multi-user settings and prevent undesirable situations, such as dominant users or off-topic actions. Children get access to various physical mobile interaction techniques for interactions with smart objects which are all required to fulfill one common goal. In this way, social interaction arises in a natural manner. In order to motivate and evaluate our approach, we developed two ambient intelligence applications called and . Results of a user study showed that children liked working in a group and that physical mobile interaction techniques are a promising approach to increase engagement and foster social interactions but also to prevent chaotic situations by balancing the distribution of activities in multi-user settings.

We explore the use of ubiquitous sensing in the home for context-sensitive microlearning. To assess how users would respond to frequent and brief learning interactions tied to context, a sensor-triggered mobile phone application was developed, with foreign language vocabulary as the learning domain. A married couple used the system in a home environment, during the course of everyday activities, for a four-week study period. Built-in and stick-on multi-modal sensors detected the participants’ interactions with hundreds of objects, furniture, and appliances. Sensor activations triggered the audio presentation of English and Spanish phrases associated with object use. Phrases were presented on average 57 times an hour; this intense interaction was found to be acceptable even after extended use. Based on interview feedback, we consider design attributes that may have reduced the interruption burden and helped sustain user interest, and which may be applicable to other context-sensitive, always-on systems.

This paper describes the results of the European Project AMIRA (Advanced Multimodal Intelligence for Remote Assistance) which deals with the support of emergency services (in particular fire services) by Ambient Intelligence. We address the problem of mobile decision support by providing mobile multi-modal access to mission-critical knowledge from different heterogeneous knowledge sources. For this purpose, we analyzed the requirements of fire service organizations as part of a socio-technical study. Further, the AMIRA system was designed based on methods from Artificial Intelligence. It implements domain specific search strategies to identify decision relevant knowledge from available knowledge sources. The developed system has been implemented as prototype and successfully evaluated in a comprehensive evaluation based on user trials.

Mobile-centric ambient intelligence refers to systems, where personal mobile device is used to access ambient information and services. In this paper, we present three scenarios describing how independent living of elderly can be supported with mobile-centric ambient intelligence services. The scenarios have been prepared in the MINAmI project, which is developing an open technology platform for mobile-centric ambient intelligence. The scenarios focus on supporting self-care and safety at home. The three scenarios have been evaluated by groups of elderly people and the two self-care related scenarios also by medical experts, in both Spain and Finland. We report the results of these evaluations, and draw conclusions for user acceptance of mobile-centric ambient intelligence supporting independent living of the elderly.

Location is an important topic on Ambient Intelligence. Different techniques are used, alone or together, to determine the position of people and objects. One aspect of this problem concerns to indoor location. Various authors propose the analysis of Radio Frequency (RF) signatures as a solution for this challenge. An approach for indoor location is the use of RF signals acquired from a Global System for Mobile Communications (GSM) by Mobile Units(MU).

In this paper we make a study based on around 485.000 signatures gathered from four buildings. We present our conclusions on the suitability and limitations of this approach for indoor location.

Localization technologies have been an important focus in ubiquitous computing. This paper explores an underrepresented area, namely computer vision technology, for outdoor positioning. More specifically we explore two modes of positioning in a challenging real world scenario: single snapshot based positioning, improved by a novel high-dimensional feature matching method, and continuous positioning enabled by combination of snapshot and incremental positioning. Quite interestingly, vision enables localization accuracies comparable to GPS. Furthermore the paper also analyzes and compares possibilities offered by the combination of different subsets of positioning technologies such as WiFi, GPS and dead reckoning in the same real world scenario as for vision based positioning.

Activity recognition has gained a lot of interest in recent years due to its potential and usefulness for context-aware computing. Most approaches for activity recognition focus on repetitive or long time patterns within the data. There is however high interest in recognizing very short activities as well, such as pushing and pulling an oil stick or opening an oil container as sub-tasks of checking the oil level in a car. This paper presents a method for the latter type of activity recognition using start and end postures (short fixed positions of the wrist) in order to identify segments of interest in a continuous data stream. Experiments show high discriminative power for using postures to recognize short activities in continuous recordings. Additionally, classifications using postures and HMMs for recognition are combined.

Research in many fields of AI, such as distributed planning and reasoning, agent teamwork and coalition formation, cooperative problem solving and action theory has advanced significantly over the last years, both from a theoretical and a practical perspective. In the light of the development towards ambient, pervasive and ubiquitous computing, this research will be tested under new, more demanding realistic conditions, stimulating the emergence of novel approaches to handle the challenges that these open, dynamic environments introduce. This paper identifies shortcomings of state-of-the-art techniques in handling the complexity of the Ambient Intelligence vision, motivated by the experience gained during the development and usage of a context-aware platform for mobile devices in dynamic environments. The paper raises research issues and discusses promising directions for realizing the objectives of near-future pervasive information systems.