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At the times of obesity epidemic, innovative ways to nudge food shoppers toward healthier choices are needed. Key ideas from the conceptual metaphor processing, food symbolism and contextual priming literatures are synthetized to argue for the idea that visual, olfactory and gustatory cues can activate shoppers’ well-being goal and guide their in-store movements and healthy food purchasing behaviors. Two field experiments, separated by an 8-month interval, are conducted in the same grocery store. The results show that priming the well-being goal in real store can push consumers to move toward spots where healthier options are available – materializing also into greater sales of yogurts, vegetables and heart healthy-labeled products. Counterintuitively, the well-being goal activation possibly first facilitate approaching indulgent foods. Yet, the actual sales of palatable foods such as ice cream and minced beef meat may in fact diminish. This research spurs numerable academic and practical implications.

Sustainable work aims at improving working conditions to allow workers to effectively extend their working life. In this context, occupational safety and well-being are major concerns, especially in labor-intensive fields, such as construction-related work. Internet of Things and wearable sensors provide for unobtrusive technology that could enhance safety using human activity recognition techniques, and has the potential of improving work conditions and health. However, the research community lacks commonly used standard datasets that provide for realistic and variating activities from multiple users. In this article, our contributions are threefold. First, we present VTT-ConIoT, a new publicly available dataset for the evaluation of HAR from inertial sensors in professional construction settings. The dataset, which contains data from 13 users and 16 different activities, is collected from three different wearable sensor locations.Second, we provide a benchmark baseline for human activity recognition that shows a classification accuracy of up to 89% for a six class setup and up to 78% for a sixteen class more granular one. Finally, we show an analysis of the representativity and usefulness of the dataset by comparing it with data collected in a pilot study made in a real construction environment with real workers.

Sustainable work aims at improving working conditions to allow workers to effectively extend their working life. In this context, occupational safety and well-being are major concerns, especially in labor-intensive fields, such as construction-related work. Internet of Things and wearable sensors provide for unobtrusive technology that could enhance safety using human activity recognition techniques, and has the potential of improving work conditions and health. However, the research community lacks commonly used standard datasets that provide for realistic and variating activities from multiple users. In this article, our contributions are threefold. First, we present VTT-ConIoT, a new publicly available dataset for the evaluation of HAR from inertial sensors in professional construction settings. The dataset, which contains data from 13 users and 16 different activities, is collected from three different wearable sensor locations.Second, we provide a benchmark baseline for human activity recognition that shows a classification accuracy of up to 89% for a six class setup and up to 78% for a sixteen class more granular one. Finally, we show an analysis of the representativity and usefulness of the dataset by comparing it with data collected in a pilot study made in a real construction environment with real workers.

Terrorism is an international security challenge. The early detection of threats (e.g., explosives or firearms) could provide a valuable contribution to the ability to prevent, protect and respond to terrorism. This paper presents a system for the management of a plurality of sensors to improve the threat-detection capabilities without disrupting the flow of passengers. The system improves the prevention capabilities of soft targets (such as airports, undergrounds and railway stations) with a high number of daily commuters. The system architecture consists of three main components. The first component is 2D video tracking and re-identification (Re-ID), which allows the labelling and tracking of commuters in a small area. Thereby, it supports the fusion of sensors at different locations. The Re-ID has a smart training strategy with anonymized snippets to increase flexibility for new environments. The second component is 3D video tracking with a stereo camera, which gives a more accurate location measurement than 2D video. Location prediction is used to compensate for latency in the control of active elements in the threat detection sensor. Recurrent neural networks for location prediction were trained by using real 3D tracking data from a railway station. The performance is evaluated with a ground-truth based on Ultra-Wide Band (UWB) radio positioning and a coordinate conversion method was created to compensate for identified inaccuracies. The third component is Command & Control (C&C), which consists of three submodules: message broker, data-fusion and security client. The message broker is a publish-subscribe middleware layer to enable flexible integration of the various sensors and components. The data-fusion combines outputs of multiple sensors. In case of a suspect person, the security client triggers an alarm and a comprehensive report is sent to the security guards.

The impending exhaustion of non-renewable natural resources emphasizes the importance of sustainable materials. One environmentally friendlier alternative to plastics are biocomposite materials, which are composed of recycled or virgin polymer and a natural fiber material. In order to be to be truly sustainable, the material has to be efficiently recycled, a task which demands a means for their sorting. This paper outlines a method for distinguishing different plastic and biocomposite samples from one another based on hyperspectral imaging. The developed regression model correctly classified 96 % of the samples in the dataset. In the case of biocomposite samples, the accompanying polymer was quite accurately recognized.

In this paper, we present an interactive game for a large public display. Our focus was on experimenting new ways to enhance the effectiveness of a large, billboard size, display by adding a collaborative interaction mechanism for a user crowd. Working with a pop festival organizer, we developed a technology proof-of-concept of a multiplayer raffle game supporting crowd interaction with a large public display using an air-filled ball, equipped with an accelerometer and a barometer, as an interaction device. We experimented on its possibility to engage festival audience in live pilots in two occasions. Our observations showed that the audience enjoyed the raffle game and participated willingly. In addition, advertisers found the solution interesting.

To recognise just the same human reaction (for example, a strong excitement) in different contexts, customary behaviours in these contexts have to be taken into account; e.g. a happy sport audience may be cheering for long time, while a happy theatrical audience may produce only short bursts of laughter in order to not interrupt the performance. Tailoring recognition algorithms to contexts can be achieved by building either a context-specific or a generic system. The former is individually trained for each context to recognise sets of characteristic responses, whereas the latter—in contrast to the context-specific one—adapts to the context via significantly more lightweight modification of parameters. This paper follows the latter way and proposes a simple modification of a hidden Markov model (HMM) classifier that enables end users to adapt the generic system to a context or a personal perception of an annotator by labelling a fairly small number of data samples of each context. For better adaptability to the limited number of the user’s annotations, the proposed semi-supervised HMM classifier employs the maximum posterior marginal, rather than the more conventional maximum a posteriori decision rule. The proposed user- and context-adaptable semi-supervised HMM classifier was tested on recognising excitement of a show audience in three contexts (a concert hall, a circus, and a sport event), differing in how the excitement is expressed. In our experiments the proposed classifier recognised reactions of a non-neutral audience with 10% higher accuracy than the conventional HMM and support vector machine based classifiers.

Demand for efficient ways to represent vast amount of video data has grown rapidly in recent years. The advances in positioning services have led to new possibilities in combining location information to video content. In this paper we present an automatic video editing system for geotagged mobile videos. In our solution the system creates automatically a video summary from a set of unedited video clips. Location information and timestamps are used to group video clips with the same context properties. The groups are used to create a video summary where subshots from same context group are represented as scenes. The novelty in our solution lies in combining geotags with low level content analysis tools in video abstraction. We have evaluated the created video summaries with a group of users and the system usability for service creation by building a semi-automatic web-based video editing service. The evaluations prove that our concept is useful as it improves coherence and enjoyability of the automatic video summaries.

Multimedia applications such as video conference, digital video broadcasting (DVB), and streaming video and audio have been gaining popularity during last years and the trend has been to allocate these services more and more also on mobile users. The demand of quality of service (QoS) for multimedia raises huge challenges on the network design, not only concerning the physical bandwidth but also the protocol design and services. One of the goals for system design is to provide efficient solutions for adaptive multimedia transmission over different access networks in all-IP environment. The joint source and channel coding (JSCC/D) approach has already given promising results in optimizing multimedia transmission. However, in practice, arranging the required control mechanism and delivering the required side information through network and protocol stack have caused problems and quite often the impact of network has been neglected in studies. In this paper we propose efficient cross-layer communication methods and protocol architecture in order to transmit the control information and to optimize the multimedia transmission over wireless and wired IP networks. We also apply this architecture to the more specific case of streaming of scalable video streams. Scalable video coding has been an active research topic recently and it offers simple and flexible solutions for video transmission over heterogeneous networks to heterogeneous terminals. In addition it provides easy adaptation to varying transmission conditions. In this paper we illustrate how scalable video transmission can be improved with efficient use of the proposed cross-layer design, adaptation mechanisms and control information.

Intelligent computer applications need to adapt their behaviour to contexts and users, but conventional classifier adaptation methods require long data collection and/or training times. Therefore classifier adaptation is often performed as follows: at design time application developers define typical usage contexts and provide reasoning models for each of these contexts, and then at runtime an appropriate model is selected from available ones. Typically, definition of usage contexts and reasoning models heavily relies on domain knowledge. However, in practice many applications are used in so diverse situations that no developer can predict them all and collect for each situation adequate training and test databases. Such applications have to adapt to a new user or unknown context at runtime just from interaction with the user, preferably in fairlylightweightways, that is, requiring limited user effort to collect training data and limited time of performing the adaptation. This paper analyses adaptation trends in several emerging domains and outlines promising ideas, proposed for making multimodal classifiers user-specific and context-specific without significant user efforts, detailed domain knowledge, and/or complete retraining of the classifiers. Based on this analysis, this paper identifies important application characteristics and presents guidelines to consider these characteristics in adaptation design.

Automatic classification of everyday activities can be used for promotion of health-enhancing physical activities and a healthier lifestyle. In this paper, methods used for classification of everyday activities like walking, running, and cycling are described. The aim of the study was to find out how to recognize activities, which sensors are useful and what kind of signal processing and classification is required. A large and realistic data library of sensor data was collected. Sixteen test persons took part in the data collection, resulting in approximately 31 h of annotated, 35-channel data recorded in an everyday environment. The test persons carried a set of wearable sensors while performing several activities during the 2-h measurement session. Classification results of three classifiers are shown: custom decision tree, automatically generated decision tree, and artificial neural network. The classification accuracies using leave-one-subject-out cross validation range from 58 to 97% for custom decision tree classifier, from 56 to 97% for automatically generated decision tree, and from 22 to 96% for artificial neural network. Total classification accuracy is 82 % for custom decision tree classifier, 86% for automatically generated decision tree, and 82% for artificial neural network.

The usability of a mobile device and services can be enhanced by context awareness. The aim of this experiment was to expand the set of generally recognizable constituents of context concerning personal mobile device usage. Naive Bayesian networks were applied to classify the contexts of a mobile device user in her normal daily activities. The distinguishing feature of this experiment in comparison to earlier context recognition research is the use of a naive Bayes framework, and an extensive set of audio features derived partly from the algorithms of the upcoming MPEG-7 standard. The classification was based mainly on audio features measured in a home scenario. The classification results indicate that with a resolution of one second in segments of 5–30 seconds, situations can be extracted fairly well, but most of the contexts are likely to be valid only in a restricted scenario. Naive Bayes framework is feasible for context recognition. In real world conditions, the recognition accuracy using leave-one-out cross validation was 87% of true positives and 95% of true negatives, averaged over nine eight-minute scenarios containing 17 segments of different lengths and nine different contexts. Respectively, the reference accuracies measured by testing with training data were 88% and 95%, suggesting that the model was capable of covering the variability introduced in the data on purpose. Reference recognition accuracy in controlled conditions was 96% and 100%, respectively. However, from the applicability viewpoint, generalization remains a problem, as from a wider perspective almost any feature may refer to many possible real world situations.

This paper presents an Adobe Flash-based augmented reality video streaming application and its practical use in web marketing. The application enables augmenting the content of a web cam view by adding video content to it. Aller Media, a Nordic media company, used the application for advertising two Finnish movies with promotional video content. We have examined combining of conventional print media and digital media and the suitability of the augmented reality video streaming application for the web environment and marketing purposes. Additionally, we measured the technical performance of the application. The feedback from Aller Media and the end-users indicates that the application is useful for marketing purposes. In addition, the results show that the application is well suited for web environment as its performance is sufficient and as the distribution is more efficient compared to desktop or mobile applications.

Demand for efficient ways to represent vast amount of video data has grown rapidly in recent years. The advances in positioning services have led to new possibilities of combining location information to video content. In this paper we present an automatic video editing system for geotagged mobile videos. In our solution the system creates automatically a video summary from a set of unedited video clips. Geotags are used to group video clips with the same context properties. The groups are used to create a video summary where the videos from the same group are represented as scenes. The novelty in our solution lies in the combining of geotags with low level content analysis tools in video abstraction. Evaluations of the system prove the concept useful as it improves coherence and enjoyability of the automatic video summaries.

Cooking assistant is an application that needs to find a trade-off between providing efficient help to the users (e.g., reminding them to stir a meal if it is about to burn) and avoiding users' annoyance. This trade-off may vary in different contexts, such as cooking alone or in a group, cooking new or known recipe etc. The results of the user study, presented in this paper, show which features of a multimodal interface users perceive as socially acceptable or unacceptable in different situations, and how this perception depends on user's age.

The multimedia content created by users with their mobile phones is often shared with family and friends to recreate personal experiences. It is difficult for a single media sharing service to cover all variations on how people would like topresent their experiences of different events; thus it is important to be able to easily create versions of these services. This paper presents in detail our platform implementation for enabling the creation of lightweight content- and context-aware mobile multimedia services. The platform supports state-of-the-art content management functionalities in order to enable easy creation of specialized multimedia services for various target groups and purposes. Our solution includes context metadata support for mobile multimedia content and creation of locationaware multimedia services. We have built example services on top of the platform using web-based technologies such as JavaScript, Media RSS feed and Java. The functionality of the platform has been tested in user evaluations with promising results.

Although mobile devices are rich sources of context information, the distribution of context in mobile environments is often hindered by the limited battery life, and wireless connections with low bandwidth. In this paper, we present two different implementations that enable context distribution in mobile environments. The first solution relies on a presence service architecture and publish-subscribe messaging, whereas the second solution is based on a mobile Web server architecture where the context information is fetched from the mobile device when needed. This paper evaluates the feasibility of the two implementations in mobile context distribution. The testing results show that the mobile server-based solution sets higher demands for the mobile device capabilities, but is able to provide more up-to-date context information. However, the solution based on the centralized context service provides higher interoperability as it utilizes the existing IETF standards.

This paper presents a solution for creating light-weighted content and context-aware mobile multimedia services. The main application domain is in user created multimedia content and experience sharing. Our approach is to have a platform supporting state-of-the art content management functionalities in order to enable easy creation of specialized multimedia services for various target groups and purposes. We have developed a mobile multimedia content creation platform with integrated context metadata support. To verify the overall functionality of our platform we have defined a multimedia content service template and created a set of exemplary services using Web-based technologies such as JavaScript, media RSS feed and Java. (11 refs.)

Video management research has been neglecting the increased attractiveness of using cameraequipped mobile phones for the production of short home video clips. But specific capabilities of modern phones — especially the availability of rich context data — open up new approaches to traditional video management problems, such as the notorious lack of annotated metadata for home video content. In this chapter, we present MobiCon, a mobile, context-aware home video production tool. MobiCon allows users to capture video clips with their camera phones, to semi-automatically create MPEG-7-conformant annotations by exploiting available context data at capture time, to upload both clips and annotations to the users’ video collections, and to share these clips with friends using OMA DRM. Thereby, MobiCon enables mobile users to effortlessly create richly annotated home video clips with their camera phones, paving the way to a more effective organization of their home video collections.