20 research outputs found

    Inferring Complex Activities for Context-aware Systems within Smart Environments

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    The rising ageing population worldwide and the prevalence of age-related conditions such as physical fragility, mental impairments and chronic diseases have significantly impacted the quality of life and caused a shortage of health and care services. Over-stretched healthcare providers are leading to a paradigm shift in public healthcare provisioning. Thus, Ambient Assisted Living (AAL) using Smart Homes (SH) technologies has been rigorously investigated to help address the aforementioned problems. Human Activity Recognition (HAR) is a critical component in AAL systems which enables applications such as just-in-time assistance, behaviour analysis, anomalies detection and emergency notifications. This thesis is aimed at investigating challenges faced in accurately recognising Activities of Daily Living (ADLs) performed by single or multiple inhabitants within smart environments. Specifically, this thesis explores five complementary research challenges in HAR. The first study contributes to knowledge by developing a semantic-enabled data segmentation approach with user-preferences. The second study takes the segmented set of sensor data to investigate and recognise human ADLs at multi-granular action level; coarse- and fine-grained action level. At the coarse-grained actions level, semantic relationships between the sensor, object and ADLs are deduced, whereas, at fine-grained action level, object usage at the satisfactory threshold with the evidence fused from multimodal sensor data is leveraged to verify the intended actions. Moreover, due to imprecise/vague interpretations of multimodal sensors and data fusion challenges, fuzzy set theory and fuzzy web ontology language (fuzzy-OWL) are leveraged. The third study focuses on incorporating uncertainties caused in HAR due to factors such as technological failure, object malfunction, and human errors. Hence, existing studies uncertainty theories and approaches are analysed and based on the findings, probabilistic ontology (PR-OWL) based HAR approach is proposed. The fourth study extends the first three studies to distinguish activities conducted by more than one inhabitant in a shared smart environment with the use of discriminative sensor-based techniques and time-series pattern analysis. The final study investigates in a suitable system architecture with a real-time smart environment tailored to AAL system and proposes microservices architecture with sensor-based off-the-shelf and bespoke sensing methods. The initial semantic-enabled data segmentation study was evaluated with 100% and 97.8% accuracy to segment sensor events under single and mixed activities scenarios. However, the average classification time taken to segment each sensor events have suffered from 3971ms and 62183ms for single and mixed activities scenarios, respectively. The second study to detect fine-grained-level user actions was evaluated with 30 and 153 fuzzy rules to detect two fine-grained movements with a pre-collected dataset from the real-time smart environment. The result of the second study indicate good average accuracy of 83.33% and 100% but with the high average duration of 24648ms and 105318ms, and posing further challenges for the scalability of fusion rule creations. The third study was evaluated by incorporating PR-OWL ontology with ADL ontologies and Semantic-Sensor-Network (SSN) ontology to define four types of uncertainties presented in the kitchen-based activity. The fourth study illustrated a case study to extended single-user AR to multi-user AR by combining RFID tags and fingerprint sensors discriminative sensors to identify and associate user actions with the aid of time-series analysis. The last study responds to the computations and performance requirements for the four studies by analysing and proposing microservices-based system architecture for AAL system. A future research investigation towards adopting fog/edge computing paradigms from cloud computing is discussed for higher availability, reduced network traffic/energy, cost, and creating a decentralised system. As a result of the five studies, this thesis develops a knowledge-driven framework to estimate and recognise multi-user activities at fine-grained level user actions. This framework integrates three complementary ontologies to conceptualise factual, fuzzy and uncertainties in the environment/ADLs, time-series analysis and discriminative sensing environment. Moreover, a distributed software architecture, multimodal sensor-based hardware prototypes, and other supportive utility tools such as simulator and synthetic ADL data generator for the experimentation were developed to support the evaluation of the proposed approaches. The distributed system is platform-independent and currently supported by an Android mobile application and web-browser based client interfaces for retrieving information such as live sensor events and HAR results

    Ambient intelligence for monitoring weight and physical activity

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    Dissertação de mestrado em Engenharia InformáticaWe have an increasingly sedentary population without the concern about a healthy diet. Therefore, it becomes necessary to give the population the opportunity, despite living a very busy and tiring life, to have control over important aspects of their health. This work aims to analyze and evaluate the impact of an ambient intelligence system on weight control and physical activity in active individuals. To accomplish this objective we have developed a mobile application that allows users to monitor their weight over a period of time, identify the amount of food they consume and the amount of exercise they practice. University students will be invited and selected, in a first stage, to participate in this study. All of the students must be considered “active students”, according to our selection criteria. Students with physical disabilities will be excluded from the study. This mobile application gives information to the users about dietary and physical activity guidelines in order to improve their lifestyles. It is expected that students improve their lifestyles

    Navigation, Path Planning, and Task Allocation Framework For Mobile Co-Robotic Service Applications in Indoor Building Environments

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    Recent advances in computing and robotics offer significant potential for improved autonomy in the operation and utilization of today’s buildings. Examples of such building environment functions that could be improved through automation include: a) building performance monitoring for real-time system control and long-term asset management; and b) assisted indoor navigation for improved accessibility and wayfinding. To enable such autonomy, algorithms related to task allocation, path planning, and navigation are required as fundamental technical capabilities. Existing algorithms in these domains have primarily been developed for outdoor environments. However, key technical challenges that prevent the adoption of such algorithms to indoor environments include: a) the inability of the widely adopted outdoor positioning method (Global Positioning System - GPS) to work indoors; and b) the incompleteness of graph networks formed based on indoor environments due to physical access constraints not encountered outdoors. The objective of this dissertation is to develop general and scalable task allocation, path planning, and navigation algorithms for indoor mobile co-robots that are immune to the aforementioned challenges. The primary contributions of this research are: a) route planning and task allocation algorithms for centrally-located mobile co-robots charged with spatiotemporal tasks in arbitrary built environments; b) path planning algorithms that take preferential and pragmatic constraints (e.g., wheelchair ramps) into consideration to determine optimal accessible paths in building environments; and c) navigation and drift correction algorithms for autonomous mobile robotic data collection in buildings. The developed methods and the resulting computational framework have been validated through several simulated experiments and physical deployments in real building environments. Specifically, a scenario analysis is conducted to compare the performance of existing outdoor methods with the developed approach for indoor multi-robotic task allocation and route planning. A simulated case study is performed along with a pilot experiment in an indoor built environment to test the efficiency of the path planning algorithm and the performance of the assisted navigation interface developed considering people with physical disabilities (i.e., wheelchair users) as building occupants and visitors. Furthermore, a case study is performed to demonstrate the informed retrofit decision-making process with the help of data collected by an intelligent multi-sensor fused robot that is subsequently used in an EnergyPlus simulation. The results demonstrate the feasibility of the proposed methods in a range of applications involving constraints on both the environment (e.g., path obstructions) and robot capabilities (e.g., maximum travel distance on a single charge). By focusing on the technical capabilities required for safe and efficient indoor robot operation, this dissertation contributes to the fundamental science that will make mobile co-robots ubiquitous in building environments in the near future.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143969/1/baddu_1.pd

    Human-Computer Interaction in Intelligent Environments

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    Nowadays, smart devices populate our environments, providing services and being more and more interactive and user-friendly. However, they usually require a centralised unit that processes all the dialogues to produce an answer. On the other hand, ubiquitous and pervasive solutions are a valid alternative, but it is hard to arrange them in a well-organised environment. In this thesis, I question if a ubiquitous infrastructure can be reactive, flexible and scalable without disadvantaging a uniform environment. Reactivity defines rapid interactions; flexibility concerns both network issues and interactions with users, through customised interfaces; scalability, instead, ensures that the adopted model does not have constrained networks' size. This investigation focuses on Human-Computer Interaction studies, because people without a required technological background will be the final users of the system. I propose a novel distributed model where each node is a device that can independently interact with users through natural interfaces; in addition, nodes collaborate with other similar devices to support people. Nodes' intelligence is limited to their own context. In order to improve the collaboration, devices share partial knowledge and have a common strategy to forward requests they are not able to accept. The resulting network is an Intelligent Environment where the intelligence comes from a composition of connected interactive behaviours. I investigated the best approach to navigate requests, proposing a routing algorithm and considering also security and consistency issues. I contextualised this work in both a smart house and a smart museum. With the devised process, I paid specific attention to professionals involved in the design steps. I identified actors with different roles and needs; in order to meet their requirements, I proposed a designing process, with automated solutions that simplify the implementation of the presented model. The system has been tested in simulated scenarios in order to evaluate all the novel parts. Results showed that the designed model is reactive, flexible and scalable. Furthermore, in order to enhance the final outcome, I characterised design patterns to design the network. Future improvements are oriented to the initialisation of the network, that now requires an expert; In addition, a more complex interaction is under investigation to support users in museum visits

    A user centred approach to the modelling of contextualised experience adaptation in relation to video consumption

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    This research focused on the development of a user centric framework for the interpretation of contextualised TV and video viewing experiences (UX). Methods to address content overload and provide better contextualisation when consuming video have been an area of academic discussion for almost 20 years (Burke, Felfernig, & Goker, 2011). However over the same period technical system design for video has actually moved away from attempts to model the nature of real viewing contexts. With now near ubiquitous access to video from a range of disparate devices the addition of contextualisation within video applications and devices represents an opportunity in terms of improving viewer UX. Three user studies were carried out to inform development of the framework and employed mixed method approaches. The first focused on understanding where video is watched and the contextual factors that defined those places as viewing situations. This study derived eight Archetype viewing situations and associated contextual cues. The second study measured viewing UX in context. Significant differences in subjective ratings for measured UX were found when viewing was compared within subjects across Viewing Archetype situations. A third study characterised viewing UX, identifying behavioural, environmental and technological factors which through observed frequency and duration were identified as indicative enablers and detractors in the creation of viewing UX. Concepts generated within the studies that related to viewing context identification and viewing UX classification through experiential factors were integrated into the framework. The framework provides a way through which to identify, describe and improve viewing UX across contexts. Additionally the framework was referenced to develop an exemplar system model for contextual adaptation in order to show its relevance to the generation of technical system design. Finally information for designers was created in the form of scenarios and suggestions for use in order to bring the framework to life as a resource for development teams

    Smart Sensor Technologies for IoT

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    The recent development in wireless networks and devices has led to novel services that will utilize wireless communication on a new level. Much effort and resources have been dedicated to establishing new communication networks that will support machine-to-machine communication and the Internet of Things (IoT). In these systems, various smart and sensory devices are deployed and connected, enabling large amounts of data to be streamed. Smart services represent new trends in mobile services, i.e., a completely new spectrum of context-aware, personalized, and intelligent services and applications. A variety of existing services utilize information about the position of the user or mobile device. The position of mobile devices is often achieved using the Global Navigation Satellite System (GNSS) chips that are integrated into all modern mobile devices (smartphones). However, GNSS is not always a reliable source of position estimates due to multipath propagation and signal blockage. Moreover, integrating GNSS chips into all devices might have a negative impact on the battery life of future IoT applications. Therefore, alternative solutions to position estimation should be investigated and implemented in IoT applications. This Special Issue, “Smart Sensor Technologies for IoT” aims to report on some of the recent research efforts on this increasingly important topic. The twelve accepted papers in this issue cover various aspects of Smart Sensor Technologies for IoT

    2004 Sixteenth Annual IMSA Presentation Day

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    Students who attend the Illinois Mathematics and Science Academy do not have to wait until they graduate from college to begin to make significant contributions to science, mathematics, the humanities and the world around them.https://digitalcommons.imsa.edu/archives_sir/1012/thumbnail.jp

    Enhancing Free-text Interactions in a Communication Skills Learning Environment

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    Learning environments frequently use gamification to enhance user interactions.Virtual characters with whom players engage in simulated conversations often employ prescripted dialogues; however, free user inputs enable deeper immersion and higher-order cognition. In our learning environment, experts developed a scripted scenario as a sequence of potential actions, and we explore possibilities for enhancing interactions by enabling users to type free inputs that are matched to the pre-scripted statements using Natural Language Processing techniques. In this paper, we introduce a clustering mechanism that provides recommendations for fine-tuning the pre-scripted answers in order to better match user inputs
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