context inference

Recently, context-awareness has been a hot topic in the ubiquitous computing field. Numerous methods for capturing, representing and inferring context have been developed and relevant projects have been performed. Existing research has tried to determine user's contextual information physically by using stereo type cameras, RFID, smart devices, etc. Especially, these are focusing on physical context such as location, temperature, light, etc. However, cognitive elements of the context are important and needs more study. Therefore, this paper confines its research domain to the web-based information system (IS) and proposes an algorithm for inferring cognitive context in the IS domain. First, a context inference algorithm aims at recognition of a user intention on the IS. To apply this algorithm, we use a text categorization technique to classify various text-based sources users are using such as web pages, pdf document, MS-word documents, etc. Second, the service inference algorithm is based on the similarity measurement between the user preference and the deliver-enabled services and tries to recommend a user-adaptive service to the user. The context and service inference algorithm that this paper proposes can help the IS user to work with a IS conveniently and enable an existing IS to deliver ubiquitous service. In this fashion, the paper shows the direction an existing IS heads and ultimately, shows the typical services of a ubiquitous computing-based IS.

Abstract. Instant messaging (IM) systems allow users to spontaneously communicate over distance, yet they bear the risk for disruption of the recipient. In order to reduce disruption, novel approaches for detecting and presenting mutual availability are needed. In this paper we show how fine-grained IM availability predictions can be made for nomadic users solely based on sensors installed on a laptop computer. Our approach provides comparable accuracies to previous work, while it eliminates the need for augmenting the offices or the users with further sensors. We performed a user study to collect sensor data. Alongside with labels collected by means of Experience Sampling, the data allow for creating probabilistic models for predicting selective availability. This way, we demonstrate how the required effort involved in proactively managing one’s availability selectively towards a variety of recipients can be reduced by automatic adaptation, and give insights in the lessons learned.

Recent years have witnessed a huge progress in the automatic identification of individual primitives of human behavior, such as activities or locations. However, the complex nature of human behavior demands more abstract contextual information for its analysis. This work presents an ontology-based method that combines low-level primitives of behavior, namely activity, locations and emotions, unprecedented to date, to intelligently derive more meaningful high-level context information. The paper contributes with a new open ontology describing both low-level and high-level context information, as well as their relationships. Furthermore, a framework building on the developed ontology and reasoning models is presented and evaluated. The proposed method proves to be robust while identifying high-level contexts even in the event of erroneously-detected low-level contexts. Despite reasonable inference times being obtained for a relevant set of users and instances, additional work is requi...

Mobile crowdsensing is a powerful mechanism to aggregate hyperlocal knowledge about the environment. Indeed, users may contribute valuable observations across time and space using the sensors embedded in their smartphones. However, the relevance of the provided measurements depends on the adequacy of the sensing context with respect to the phenomena that are analyzed. This paper concentrates more specifically on assessing the sensing context when gathering observations about the physical environment beyond its geographical position in the Euclidean space, i.e., whether the phone is in-/out-pocket, in-/out-door and on-/under-ground. We introduce an online learning approach to the local inference of the sensing context so as to overcome the disparity of the classification performance due to the heterogeneity of the sensing devices as well as the diversity of user behavior and novel usage scenarios. Our approach specifically features a hierarchical algorithm for inference that requires few opportunistic feedbacks from the user, while increasing the accuracy of the context inference per user.

Recent years have witnessed a huge progress in the automatic identification of individual primitives of human behavior, such as activities or locations. However, the complex nature of human behavior demands more abstract contextual information for its analysis. This work presents an ontology-based method that combines low-level primitives of behavior, namely activity, locations and emotions, unprecedented to date, to intelligently derive more meaningful high-level context information. The paper contributes with a new open ontology describing both low-level and high-level context information, as well as their relationships. Furthermore, a framework building on the developed ontology and reasoning models is presented and evaluated. The proposed method proves to be robust while identifying high-level contexts even in the event of erroneously-detected low-level contexts. Despite reasonable inference times being obtained for a relevant set of users and instances, additional work is requi...

Abstract—Context-aware application models can provide personalized services to users through user-centric integration of contexts. Recently, several research activities on context integration have been reported, However, the existing research activities don't consider much how contexts are integrated in a unified way. In this paper, we propose a unified method of user-centric integration of contexts for context-aware applications. The proposed method enables to extract the meaningful contexts based on each fusion procedure of ...

Recent years have witnessed a huge progress in the automatic identification of individual primitives of human behavior, such as activities or locations. However, the complex nature of human behavior demands more abstract contextual information for its analysis. This work presents an ontology-based method that combines low-level primitives of behavior, namely activity, locations and emotions, unprecedented to date, to intelligently derive more meaningful high-level context information. The paper contributes with a new open ontology describing both low-level and high-level context information, as well as their relationships. Furthermore, a framework building on the developed ontology and reasoning models is presented and evaluated. The proposed method proves to be robust while identifying high-level contexts even in the event of erroneously-detected low-level contexts. Despite reasonable inference times being obtained for a relevant set of users and instances, additional work is requi...

Recent years have witnessed a huge progress in the automatic identification of individual primitives of human behavior, such as activities or locations. However, the complex nature of human behavior demands more abstract contextual information for its analysis. This work presents an ontology-based method that combines low-level primitives of behavior, namely activity, locations and emotions, unprecedented to date, to intelligently derive more meaningful high-level context information. The paper contributes with a new open ontology describing both low-level and high-level context information, as well as their relationships. Furthermore, a framework building on the developed ontology and reasoning models is presented and evaluated. The proposed method proves to be robust while identifying high-level contexts even in the event of erroneously-detected low-level contexts. Despite reasonable inference times being obtained for a relevant set of users and instances, additional work is requi...

The growing processing capabilities of mobile devices coupled with portable and wearable sensors have enabled the development of context-aware services tailored to the user environment and its daily activities. The problem of determining the user context at each particular point in time is one of the main challenges in this area. In this paper, we describe the approach pursued in the UPCASE project, which makes use of sensors available in the mobile device as well as sensors externally connected via Bluetooth. We describe the system architecture from raw data acquisition to feature extraction and context inference. As a proof of concept, the inference of contexts is based on a decision tree to learn and identify contexts automatically and dynamically at runtime. Preliminary results suggest that this is a promising approach for context inference in several application scenarios.

The growing processing capabilities of mobile devices coupled with portable and wearable sensors have enabled the development of context-aware services tailored to the user environment and its daily activities. The problem of determining the user context at each particular point in time is one of the main challenges in this area. In this paper, we describe the approach pursued in the UPCASE project, which makes use of sensors available in the mobile device as well as sensors externally connected via Bluetooth. We describe the system architecture from raw data acquisition to feature extraction and context inference. As a proof of concept, the inference of contexts is based on a decision tree to learn and identify contexts automatically and dynamically at runtime. Preliminary results suggest that this is a promising approach for context inference in several application scenarios.

This work presents the Mining Minds Context Ontology, an ontology for the identification of human behavior. This ontology comprehensively models high-level context based on low-level information, including the user activities, locations, and emotions. The Mining Minds Context Ontology is the means to infer high-level context from the lowlevel information. High-level contexts can be inferred from unclassified contexts by reasoning on the Mining Minds Context Ontology. The Mining Minds Context Ontology is shown to be flexible enough to operate in real life scenarios in which emotion recognition systems may not always be available. Furthermore, it is demonstrated that the activity and the location might not be enough to detect some of the high-level contexts, and that the emotion enables a more accurate high-level context identification. This work paves the path for the future implementation of the high-level context recognition system in the Mining Minds project.

The monitoring of human lifestyles has gained much attention in the recent years. This work presents a novel approach to combine multiple context-awareness technologies for the automatic analysis of people's conduct in a comprehensive and holistic manner. Activity recognition, emotion recognition, location detection, and social analysis techniques are integrated with ontological mechanisms as part of a framework to identify human behavior. Key architectural components, methods and evidences are described in this paper to illustrate the interest of the proposed approach.

— In recent years, healthcare and wellness platforms are developed rapidly with the advent of smart devices which possess diverse sensors. Existing systems are limited to provide simple health status visualization services from single device or single sensor, which make them unable to provide timely high quality services. This paper proposes Human Centric Awareness Framework based on diverse IoT devices to provide high quality services timely.

—The combination of ontology based context-awareness and machine learning context classification is an interesting research area. The determined contexts are obtained using semantic reasoning based on context ontology developed by expert using domain specific rules. This reasoning suffer challenges of soundness and completeness in real-time deployment. This paper addresses the aforementioned challenges from semantic reasoning by embracing machine learning modeling and classification benefits. Machine learning relies on data, for this we developed training and deployment phase for ontological ABox assertions. Approximately 99.99% precision through machine learning approach was achieved over 91.5% accuracy with semantic reasoning. The statistical evaluation proves the improvement in terms of accuracy for context prediction and overall performance.

The emerging research on automatic identification of user's contexts from the cross-domain environment in ubiquitous and pervasive computing systems has proved to be successful. Monitoring the diversified user's contexts and behaviors can help in controlling lifestyle associated to chronic diseases using context-aware applications. However, availability of cross-domain heterogeneous contexts provides a challenging opportunity for their fusion to obtain abstract information for further analysis. This work demonstrates extension of our previous work from a single domain (i.e., physical activity) to multiple domains (physical activity, nutrition and clinical) for context-awareness. We propose multi-level Context-aware Framework (mlCAF), which fuses the multi-level cross-domain contexts in order to arbitrate richer behavioral contexts. This work explicitly focuses on key challenges linked to multi-level context modeling, reasoning and fusioning based on the mlCAF open-source ontology. More specifically, it addresses the interpretation of contexts from three different domains, their fusioning conforming to richer contextual information. This paper contributes in terms of ontology evolution with additional domains, context definitions, rules and inclusion of semantic queries. For the framework evaluation, multi-level cross-domain contexts collected from 20 users were used to ascertain abstract contexts, which served as basis for behavior modeling and lifestyle identification. The experimental results indicate a context recognition average accuracy of around 92.65% for the collected cross-domain contexts.

Recent years have witnessed a huge progress in the automatic identification of individual primitives of human behavior, such as activities or locations. However, the complex nature of human behavior demands more abstract contextual information for its analysis. This work presents an ontology-based method that combines low-level primitives of behavior, namely activity, locations and emotions, unprecedented to date, to intelligently derive more meaningful high-level context information. The paper contributes with a new open ontology describing both low-level and high-level context information, as well as their relationships. Furthermore, a framework building on the developed ontology and reasoning models is presented and evaluated. The proposed method proves to be robust while identifying high-level contexts even in the event of erroneously-detected low-level contexts. Despite reasonable inference times being obtained for a relevant set of users and instances, additional work is required to scale to long-term scenarios with a large number of users.

There is sufficient evidence proving the impact that negative lifestyle choices have on people's health and wellness. Changing unhealthy behaviours requires raising people's self-awareness and also providing healthcare experts with a thorough and continuous description of the user's conduct. Several monitoring techniques have been proposed in the past to track users' behaviour; however, these approaches are either subjective and prone to misreporting, such as questionnaires, or only focus on a specific component of context, such as activity counters. This work presents an innovative multimodal context mining framework to inspect and infer human behaviour in a more holistic fashion. The proposed approach extends beyond the state-of-the-art, since it not only explores a sole type of context, but also combines diverse levels of context in an integral manner. Namely, low-level contexts, including activities, emotions and locations, are identified from heterogeneous sensory data through machine learning techniques. Low-level contexts are combined using ontological mechanisms to derive a more abstract representation of the user's context, here referred to as high-level context. An initial implementation of the proposed framework supporting real-time context identification is also presented. The developed system is evaluated for various realistic scenarios making use of a novel multimodal context open dataset and data on-the-go, demonstrating prominent context-aware capabilities at both low and high levels. The last World Health Organization (WHO) global status report on noncommunicable diseases reveals that illnesses associated with lifestyle choices are currently the leading cause of death worldwide [1]. As a matter of fact, non-communicable diseases are responsible for more than two-thirds of the world's deaths, with more than 40% of them representing premature deaths under the age of 70 years. Recognizing this seriously worrying epidemic scenario, the WHO has defined a clear roadmap to alter the course of the so-called " slow-moving public health disaster ". Most of the policies

Modern digital technologies are paving the path to a revolutionary new concept of health and wellness care. Nowadays, many new solutions are being released and put at the reach of most consumers for promoting their health and wellness self-management. However, most of these applications are of very limited use, arguable accuracy and scarce interoperability with other similar systems. Accordingly, frameworks that may orchestrate, and intelligently leverage, all the data, information and knowledge generated through these systems are particularly required. This work introduces Mining Minds, an innovative framework that builds on some of the most prominent modern digital technologies, such as Big Data, Cloud Computing, and Internet of Things, to enable the provision of personalized healthcare and wellness support. This paper aims at describing the efficient and rational combination and interoperation of these technologies, as well as their integration with current and future personalized health and wellness services and business.

This work presents the Mining Minds Context Ontology, an ontology for the identification of human behavior. This ontology comprehensively models high-level context based on low-level information, including the user activities, locations, and emotions. The Mining Minds Context Ontology is the means to infer high-level context from the low-level information. High-level contexts can be inferred from unclassified contexts by reasoning on the Mining Minds Context Ontology. The Mining Minds Context Ontology is shown to be flexible enough to operate in real life scenarios in which emotion recognition systems may not always be available. Furthermore, it is demonstrated that the activity and the location might not be enough to detect some of the high-level contexts, and that the emotion enables a more accurate high-level context identification. This work paves the path for the future implementation of the high-level context recognition system in the Mining Minds project.