Providing accurate and opportune information on people's activities and behaviors is one of the most important tasks in pervasive computing. Innumerable applications can be visualized, for instance, in medical, security, entertainment, and tactical scenarios. Despite human activity recognition (HAR) being an active field for more than a decade, there are still key aspects that, if addressed, would constitute a significant turn in the way people interact with mobile devices. This paper surveys the state of the art in HAR based on wearable sensors. A general architecture is first presented along with a description of the main components of any HAR system. We also propose a two-level taxonomy in accordance to the learning approach (either supervised or semi-supervised) and the response time (either offline or online). Then, the principal issues and challenges are discussed, as well as the main solutions to each one of them. Twenty eight systems are qualitatively evaluated in terms of recognition performance, energy consumption, obtrusiveness, and flexibility, among others. Finally, we present some open problems and ideas that, due to their high relevance, should be addressed in future research.

A Survey on Human Activity Recognition using Wearable Sensors

The importance of contextual information has been recognized by researchers and practitioners in many disciplines, including e-commerce personalization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management. While a substantial amount of research has already been performed in the area of recommender systems, most existing approaches focus on recommending the most relevant items to users without taking into account any additional contextual information, such as time, location, or the company of other people (e.g., for watching movies or dining out). In this chapter we argue that relevant contextual information does matter in recommender systems and that it is important to take this information into account when providing recommendations. We discuss the general notion of context and how it can be modeled in recommender systems. Furthermore, we introduce three different algorithmic paradigms – contextual prefiltering, post-filtering, and modeling – for incorporating contextual information into the recommendation process, discuss the possibilities of combining several contextaware recommendation techniques into a single unifying approach, and provide a case study of one such combined approach. Finally, we present additional capabilities for context-aware recommenders and discuss important and promising directions for future research.

/pdf/context-aware-recommender-systems-8uash1fwm4.pdf

Context-aware recommender systems

Recommender systems have been researched extensively by the Technology Enhanced Learning (TEL) community during the last decade. By identifying suitable resources from a potentially overwhelming variety of choices, such systems offer a promising approach to facilitate both learning and teaching tasks. As learning is taking place in extremely diverse and rich environments, the incorporation of contextual information about the user in the recommendation process has attracted major interest. Such contextualization is researched as a paradigm for building intelligent systems that can better predict and anticipate the needs of users, and act more efficiently in response to their behavior. In this paper, we try to assess the degree to which current work in TEL recommender systems has achieved this, as well as outline areas in which further work is needed. First, we present a context framework that identifies relevant context dimensions for TEL applications. Then, we present an analysis of existing TEL recommender systems along these dimensions. Finally, based on our survey results, we outline topics on which further research is needed.

Context-Aware Recommender Systems for Learning: A Survey and Future Challenges

Cyber-physical system (CPS) is a new trend in the Internet-of-Things related research works, where physical systems act as the sensors to collect real-world information and communicate them to the computation modules (i.e. cyber layer), which further analyze and notify the findings to the corresponding physical systems through a feedback loop. Contemporary researchers recommend integrating cloud technologies in the CPS cyber layer to ensure the scalability of storage, computation, and cross domain communication capabilities. Though there exist a few descriptive models of the cloud-based CPS architecture, it is important to analytically describe the key CPS properties: computation, control, and communication. In this paper, we present a digital twin architecture reference model for the cloud-based CPS, C2PS, where we analytically describe the key properties of the C2PS. The model helps in identifying various degrees of basic and hybrid computation-interaction modes in this paradigm. We have designed C2PS smart interaction controller using a Bayesian belief network, so that the system dynamically considers current contexts. The composition of fuzzy rule base with the Bayes network further enables the system with reconfiguration capability. We also describe analytically, how C2PS subsystem communications can generate even more complex system-of-systems. Later, we present a telematics-based prototype driving assistance application for the vehicular domain of C2PS, VCPS, to demonstrate the efficacy of the architecture reference model.

C2PS: A Digital Twin Architecture Reference Model for the Cloud-Based Cyber-Physical Systems

Advanced internet of things for personalised healthcare systems

As the World Wide Web becomes a large source of digital music, the music recommendation system has got a great demand. There are several music recommendation systems for both commercial and academic areas, which deal with the user preference as fixed. However, since the music preferred by a user may change depending on the contexts, the conventional systems have inherent problems. This paper proposes a context-aware music recommendation system (CA-MRS) that exploits the fuzzy system, Bayesian networks and the utility theory in order to recommend appropriate music with respect to the context. We have analyzed the recommendation process and performed a subjective test to show the usefulness of the proposed system.

http://sclab.yonsei.ac.kr/publications/Papers/LNCS/FSKD2006_PHS.pdf

A context-aware music recommendation system using fuzzy bayesian networks with utility theory

Since 1990s, with an advancement of network technology and the popularization of the Internet, information that people can access has proliferated, thus information recommendation has been investigated as an important issue. Because preference to information recommendation can be different as context that the users are related to, we should consider this context to provide a good service. This paper proposes the recommendation system that considers the preferences of group users in mobile environment and applied the system to recommendation of restaurants. Since mobile environment has plenty of uncertainty, our system have used Bayesian network which showed reliable performance with uncertain input to model individual user's preference. Also, restaurant recommendation mostly considers the preference of group users, so we have used AHP (Analytic Hierarchy Process) of multi-criteria decision making method to get the preference of group users from individual users' preferences. For experiments, we have assumed 10 different situations and compared the proposed method with random recommendation and simple rule-based recommendation. Finally, we have confirmed that the proposed system provides high usability with SUS (System Usability Scale).

/pdf/restaurant-recommendation-for-group-of-people-in-mobile-2wp9k5vxll.pdf

Restaurant Recommendation for Group of People in Mobile Environments Using Probabilistic Multi-criteria Decision Making

With the recent popularization of smart phones, context sharing systems in mobile environment attract attention of people Mobile context sharing systems can share more information than web-based social network services because they have various sensors For sharing high-level contexts such as activity and emotion, a user had to manually annotate them in previous works This paper proposes a mobile context sharing system that can recognize high-level contexts automatically by using Bayesian networks based on collected mobile logs We have implemented the Context Viewer application which consists of a phonebook and a map browser to prove the feasibility of the proposed system Also, we have confirmed that the proposed system is useful, by evaluating Bayesian networks and performing the SUS test

A Mobile Context Sharing System Using Activity and Emotion Recognition with Bayesian Networks

The metabolic syndrome is a set of risk factors that include abdominal obesity, insulin resistance, dyslipidemia and hypertension. It has affected around 25% of adults in the US and become a serious problem in Asian countries recently due to the change in dietary habit and life style. On the other hand, Bayesian networks that are the models to solve the problems of uncertainty provide a robust and transparent formalism for probabilistic modeling, so they have been used as a method for diagnostic or prognostic model in medical domain. Since the K2 algorithm, a well-known algorithm for Bayesian networks structure learning, is influenced by an input order of the attributes, an optimization of BN attribute ordering has been studied as a research issue. This paper proposes a novel ordering optimization method using a genetic algorithm based on medical expert knowledge in order to solve this problem. For experiments, we use the dataset examined twice in 1993 and 1995 in Yonchon County of Korea. It has 18 attributes of 1193 subjects participated in both surveys. Using this dataset, we make the prognostic model of the metabolic syndrome using Bayesian networks with an optimized ordering by evolutionary approach. Through an ordering optimization, the prognostic model of higher performance is constructed, and the optimized Bayesian network model by the proposed method outperforms the conventional BN model as well as neural networks and k-nearest neighbors. Finally, we present the application program using the prognostic model of the metabolic syndrome in order to show the usefulness of the proposed method.

https://isiarticles.com/bundles/Article/pre/pdf/29158.pdf

Evolutionary attribute ordering in Bayesian networks for predicting the metabolic syndrome

Highlights? Implementing high-level context-aware service for home robot. ? Presenting a systematic modeling approach for modular design of many Bayesian networks. ? Applying the proposed modeling process to context-aware service of home robot. Recently, demand for service robots increases, and, particularly, one for personal service robots, which requires robot intelligence, will be expected to increase more. Accordingly, studies on intelligent robots are spreading all over the world. In this situation, we attempt to realize context-awareness for home robot while previous robot research focused on image processing, control and low-level context recognition. This paper uses probabilistic modeling for service robots to provide users with high-level context-aware services required in home environment, and proposes a systematic modeling approach for modeling a number of Bayesian networks. The proposed approach supplements uncertain sensor input using Bayesian network modeling and enhances the efficiency in modeling and reasoning processes using modular design based on domain knowledge. We verify the proposed method is useful as measuring the performance of context-aware module and conducting subjective test.

http://sclab.yonsei.ac.kr/publications/Papers/IJ/A%20modular%20design%20of%20Bayesian%20networks%20using%20expert%20knowledge,%20Context-aware%20home%20service%20robot.pdf

Han-Saem Park

Papers

A context-aware music recommendation system using fuzzy bayesian networks with utility theory

Restaurant Recommendation for Group of People in Mobile Environments Using Probabilistic Multi-criteria Decision Making

A Mobile Context Sharing System Using Activity and Emotion Recognition with Bayesian Networks

Evolutionary attribute ordering in Bayesian networks for predicting the metabolic syndrome

A modular design of Bayesian networks using expert knowledge