Context-aware mobile communication in hospitals

A, collaborative handheld system extends the instant messaging paradigm by adding context-awareness to support the intensive and distributed nature of information management within a hospital setting.     

Automatic feature selection for context recognition in mobile devices

In mobile devices there exist several in-built sensor units and sources which provide data for context reasoning. More context sources can be attached via wireless network connections. Usually, the mobile devices and the context sources are battery powered and their computational and space resources are limited. This sets special requirements for the context recognition algorithms. In this paper, several classification and automatic feature selection algorithms are compared in the context recognition domain. The main goal of this study is to investigate how much advantage can be achieved by using sophisticated and complex classification methods compared with a simple method that can easily be implemented in mobile devices. The main result is that even a simple linear classification algorithm can achieve a reasonably good accuracy if the features calculated from raw data are selected in a suitable way. Usually context recognition algorithms are fitted to a particular problem instance in an off-line manner and modifying methods for on-line learning is difficult or impossible. An on-line version of the Minimum-distance classifier is presented in this paper and it is justified that it leads to considerably higher classification accuracies compared with the static off-line version of the algorithm. Moreover, we report superior performance for the Minimum-distance classifier compared to other classifiers from the view point of computational load and power consumption of a smart phone.     

Context-aware broadcasting approaches in mobile ad hoc networks

The aim of this paper is to compare different context-aware broadcasting approaches in mobile ad hoc networks (MANETs) and to evaluate their respective performances. Message broadcasting is one of the core challenges brought up by distributed systems and has therefore largely been studied in the context of traditional network structures, such as the Internet. With the emergence of MANETs, new broadcasting algorithms especially geared at these networks have been introduced. The goal of these broadcasting algorithms is to ensure that a maximum number of nodes deliver the broadcasted message (reliability), while ensuring that the minimum number of nodes retransmit the broadcasted message (efficiency), in order to save their resources, such as bandwidth or battery. In recent years, as more and more mobile devices have become context-aware, several broadcasting algorithms have been introduced that take advantage of contextual information in order to improve their performance. We distinguish four approaches with respect to context: (1) context-oblivious approaches, (2) network traffic-aware approaches, (3) power-aware approaches, and (4) location-aware approaches. This paper precisely aims at presenting these four different broadcasting approaches and at measuring the performance of algorithms built upon them.     

移动设备的个性化推荐在上下文感知应用

个性化推荐对上下文感知系统具有广泛而重要应用,现在大多数个性化推荐系统很少考虑用户的认知风格.文中比较了场独立用户和场依赖用户在上下文感知环境中所存在的差异,提出一种基于用户认知风格的个性化推荐系统(CSBRS).通过CSBRS的实现例子,表明基于用户认知风格的个性化推荐有助于提高个性化推荐的质量.     

Authentication in mobile devices through hand gesture recognition

This article proposes an innovative biometric technique based on the idea of authenticating a person on a mobile device by gesture recognition. To accomplish this aim, a user is prompted to be recognized by a gesture he/she performs moving his/her hand while holding a mobile device with an accelerometer embedded. As users are not able to repeat a gesture exactly in the air, an algorithm based on sequence alignment is developed to correct slight differences between repetitions of the same gesture. The robustness of this biometric technique has been studied within 2 different tests analyzing a database of 100 users with real falsifications. Equal Error Rates of 2.01 and 4.82% have been obtained in a zero-effort and an active impostor attack, respectively. A permanence evaluation is also presented from the analysis of the repetition of the gestures of 25 users in 10 sessions over a month. Furthermore, two different gesture databases have been developed: one made up of 100 genuine identifying 3-D hand gestures and 3 impostors trying to falsify each of them and another with 25 volunteers repeating their identifying 3-D hand gesture in 10 sessions over a month. These databases are the most extensive in published studies, to the best of our knowledge.     

Context-aware multimodal recommendations of multimedia data in cyber situational awareness

Multimedia Tools and Applications - The current proliferation of large amounts of multimedia data creates an unprecedented challenge for security analysts in the context of Cyber Situational...     

Mid-level features and spatio-temporal context for activity recognition

Local spatio-temporal features have been shown to be effective and robust in order to represent simple actions. However, for high level human activities with long-range motion or multiple interactive body parts and persons, the limitation of low-level features blows up because of their localness. This paper addresses the problem by suggesting a framework that computes mid-level features and takes into account their contextual information.First, we represent human activities by a set of mid-level components, referred to as activity components, which have consistent structure and motion in spatial and temporal domain respectively. These activity components are extracted hierarchically from videos, i.e., extracting key-points, grouping them into trajectories and finally clustering trajectories into components. Second, to further exploit the interdependencies of the activity components, we introduce a spatio-temporal context kernel (STCK), which not only captures local properties of features but also considers their spatial and temporal context information. Experiments conducted on two challenging activity recognition datasets show that the proposed approach outperforms standard spatio-temporal features and our STCK context kernel improves further the performance.     

Context-aware and quality-aware algorithms for efficient mobile object management

The management of positions of mobile objects is an essential prerequisite for many context-aware systems such as advanced traffic management systems or personal assistance systems. In this paper, we present two approaches for the scalable tracking of mobile object trajectories and the efficient processing of continuous spatial range queries, respectively. We show in detail how both approaches utilize the basic concepts of accuracy relaxation and utilization of context information, such as movement predictions, to minimize the number of position updates, the size of trajectory data, and the number of energy-consuming position sensing operations.     

Indoor scene recognition by a mobile robot through adaptive object detection

Mobile robotics has achieved notable progress, however, to increase the complexity of the tasks that mobile robots can perform in natural environments, we need to provide them with a greater semantic understanding of their surrounding. In particular, identifying indoor scenes, such as an Office or a Kitchen, is a highly valuable perceptual ability for an indoor mobile robot, and in this paper we propose a new technique to achieve this goal. As a distinguishing feature, we use common objects, such as Doors or furniture, as a key intermediate representation to recognize indoor scenes. We frame our method as a generative probabilistic hierarchical model, where we use object category classifiers to associate low-level visual features to objects, and contextual relations to associate objects to scenes. The inherent semantic interpretation of common objects allows us to use rich sources of online data to populate the probabilistic terms of our model. In contrast to alternative computer vision based methods, we boost performance by exploiting the embedded and dynamic nature of a mobile robot. In particular, we increase detection accuracy and efficiency by using a 3D range sensor that allows us to implement a focus of attention mechanism based on geometric and structural information. Furthermore, we use concepts from information theory to propose an adaptive scheme that limits computational load by selectively guiding the search for informative objects. The operation of this scheme is facilitated by the dynamic nature of a mobile robot that is constantly changing its field of view. We test our approach using real data captured by a mobile robot navigating in Office and home environments. Our results indicate that the proposed approach outperforms several state-of-the-art techniques for scene recognition.     

Adaptive content recommendation for mobile users: Ordering recommendations using a hierarchical context model with granularity

Retrieving timely and relevant information on-site is an important task for mobile users. A context-aware system can understand a user’s information needs and thus select contents according to relevance. We propose a context-dependent search engine that represents user context in a knowledge-based context model, implemented in a hierarchical structure with granularity information. Search results are ordered based on semantic relevance computed as similarity between the current context and tags of search results. Compared against baseline algorithms, the proposed approach enhances precision by 22% and pooled recall by 17%. The use of size-based granularity to compute similarity makes the approach more robust against changes in the context model in comparison to graph-based methods, facilitating import of existing knowledge repositories and end-user defined vocabularies (folksonomies). The reasoning engine being light-weight, privacy protection is ensured, as all user information is processed locally on the user’s phone without requiring communication with an external server.     

A content search system considering the activity and context of a mobile user

People routinely carry mobile devices in their daily lives and obtain a variety of information from the Internet in many different situations. In searching for information (content) with a mobile device, a user’s activity (e.g., moving or stationary) and context (e.g., commuting in the morning or going downtown in the evening) often change, and such changes can affect the user’s degree of concentration on his or her mobile device’s display and information needs. Therefore, a search system should provide the user with an amount of information suitable for the current activity and a type of information suitable for the current context. In this study, we present the design and implementation of a content search system that considers a mobile user’s activity and context, with the goal of reducing the user’s operation load for content search. The proposed system switches between two kinds of content search systems according to the user’s activity: the location-based content search system is activated when the user is stationary (e.g., standing and sitting), while a menu-based content search system is activated when the user is moving (e.g., walking). Both systems present information according to user context. The location-based system presents detailed information via menus and a map according to location-based categories. The menu-based system presents only a few options to enable users to get content easily. Through user experiments, we confirmed that participants could get desired information more easily with this system than with a commercial search system.     

Pull-based recommendations in mobile environments

In the field of Context-Aware Recommendation Systems (CARS), only static contextual information is usually considered. However, the dynamic contextual information would very helpful in mobile computing scenarios. Despite this interest, the design and implementation of flexible and generic frameworks to support an easy development of context-aware mobile recommendation systems have been relatively unexplored. In this paper, we describe a framework that facilitates the development of CARS for mobile environments. We mainly focus on the development of the elements needed to support pull-based recommendations and the experimental evaluation of the proposed system.     

Face detection and recognition in an unconstrained environment for mobile visual assistive system

We present a visual assistive system that features mobile face detection and recognition in an unconstrained environment from a mobile source using convolutional neural networks. The goal of the system is to effectively detect individuals that approach facing towards the person equipped with the system. We find that face detection and recognition becomes a very difficult task due to the movement of the user which causes camera shakes resulting in motion blur and noise in the input for the visual assistive system. Due to the shortage of related datasets, we create a dataset of videos captured from a mobile source that features motion blur and noise from camera shakes. This makes the application a very challenging aspect of face detection and recognition in unconstrained environments. The performance of the convolutional neural network is further compared with a cascade classifier. The results show promising performance in daylight and artificial lighting conditions while the challenges lie for moonlight conditions with the need for reduction of false positives in order to develop a robust system. We also provide a framework for implementation of the system with smartphones and wearable devices for video input and auditory notification from the system to guide the visually impaired.     

Locating group members in a mobile environment

The Internet Engineering Task Force lists multicasting as a desired service for our future mobile computing environment. To provide this service, location management of a group of users involved in a multicasting application needs to be addressed. In this paper, we propose two protocols that can be used by a group of mobile hosts to share their current location with other group members efficiently.     

Diversifying personalized mobile multimedia application recommendations through the Latent Dirichlet Allocation and clustering optimization

Multimedia Tools and Applications - The rapid development of mobile multimedia applications explosively increased the availability of the number of applications in the apps market. Among the crowd...     

Managing context information in mobile devices

We present a uniform mobile terminal software framework that provides systematic methods for acquiring and processing useful context information from a user's surroundings and giving it to applications. The framework simplifies the development of context-aware mobile applications by managing raw context information gained from multiple sources and enabling higher-level context abstractions.     

Reasoning about shadows in a mobile robot environment

This paper describes a logic-based formalism for qualitative spatial reasoning with cast shadows (Perceptual Qualitative Relations on Shadows, or PQRS) and presents results of a mobile robot qualitative self-localisation experiment using this formalism. Shadow detection was accomplished by mapping the images from the robot’s monocular colour camera into a HSV colour space and then thresholding on the V dimension. We present results of self-localisation using two methods for obtaining the threshold automatically: in one method the images are segmented according to their grey-scale histograms, in the other, the threshold is set according to a prediction about the robot’s location, based upon a qualitative spatial reasoning theory about shadows. This theory-driven threshold search and the qualitative self-localisation procedure are the main contributions of the present research. To the best of our knowledge this is the first work that uses qualitative spatial representations both to perform robot self-localisation and to calibrate a robot’s interpretation of its perceptual input.     

Primary and secondary context in mobile video communication

The new video capabilities of mobile phones are starting to change the field of mobile communication. It is now dramatically easier to publish video in quasi-real time. We discuss how this change will affect the way people perceive video-recording, in terms of privacy, transparency, and the notion of context. We use a model of primary and secondary contexts to analyze usage situations, highlighting newly relevant research issues.