Context-Aware Reasoning Framework for Multi-user Recommendations in Smart Home
17 Dec 2019-pp 302-310
TL;DR: A context-aware reasoning framework that adapts to the needs and preferences of inhabitants continuously to provide contextually relevant recommendations to the group of users in a smart home environment is introduced.
Abstract: This paper introduces a context-aware reasoning framework that adapts to the needs and preferences of inhabitants continuously to provide contextually relevant recommendations to the group of users in a smart home environment. User’s activity and mobility plays a crucial role in defining various contexts in and around the home. The observation data acquired from disparate sensors, called user’s context, is interpreted semantically to implicitly disambiguate the users that are being recommended to. The recommendations are provided based on the relationship that exist among multiple users and the decision is made as per the preference or priority. The proposed approach makes extensive use of multimedia ontology in the life cycle of situation recognition to explicitly model and represent user’s context in smart home. Further, dynamic reasoning is exploited to facilitate context-aware situation tracking and intelligently recommending appropriate actions which suit the situation. We illustrate use of the proposed framework for Smart Home use-case.
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TL;DR: This paper surveys context awareness from an IoT perspective and addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT.
Abstract: As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.
2,542 citations
20 Jun 2005
TL;DR: The experimental results in a real-world environment have confirmed the belief that directly modeling shared structures not only reduces computational cost, but also improves recognition accuracy when compared with the tree HHMM and the flat HMM.
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357 citations
08 May 2005
TL;DR: The simultaneous tracking and activity recognition (STAR) problem is introduced, which exploits the synergy between location and activity to provide the information necessary for automatic health monitoring.
Abstract: In this paper we introduce the simultaneous tracking and activity recognition (STAR) problem, which exploits the synergy between location and activity to provide the information necessary for automatic health monitoring. Automatic health monitoring can potentially help the elderly population live safely and independently in their own homes by providing key information to caregivers. Our goal is to perform accurate tracking and activity recognition for multiple people in a home environment. We use a “bottom-up” approach that primarily uses information gathered by many minimally invasive sensors commonly found in home security systems. We describe a Rao-Blackwellised particle filter for room-level tracking, rudimentary activity recognition (i.e., whether or not an occupant is moving), and data association. We evaluate our approach with experiments in a simulated environment and in a real instrumented home.
348 citations
Journal Article•
106 citations
01 Jan 2006
TL;DR: The main contributions of this paper lie in the integration of multiple HHMMs for recognising high-level behaviours of multiple people and the construction of the Rao-Blackwellised particle filters (RBPF) for approximate inference.
Abstract: Recognising behaviours of multiple people, especially high-level behaviours, is an important task in surveillance systems. When the reliable assignment of people to the set of observations is unavailable, this task becomes complicated. To solve this task, we present an approach, in which the hierarchical hidden Markov model (HHMM) is used for modeling the behaviour of each person and the joint probabilistic data association filters (JPDAF) is applied for data association. The main contributions of this paper lie in the integration of multiple HHMMs for recognising high-level behaviours of multiple people and the construction of the Rao-Blackwellised particle filters (RBPF) for approximate inference. Preliminary experimental results in a real environment show the robustness of our integrated method in behaviour recognition and its advantage over the use of Kalman filter in tracking people.
43 citations