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  • 1.
    Ahmed, Mobyen Uddin
    et al.
    Örebro University, School of Science and Technology.
    Banaee, Hadi
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Health monitoring for elderly: an application using case-based reasoning and cluster analysis2013In: ISRN Artificial Intelligence, ISSN 2090-7435, E-ISSN 2090-7443, Vol. 2013, no 2013, p. 1-11Article in journal (Refereed)
    Abstract [en]

    This paper presents a framework to process and analyze data from a pulse oximeter which measures pulse rate and blood oxygen saturation from a set of individuals remotely. Using case-based reasoning (CBR) as the backbone to the framework, records are analyzed and categorized according to how well they are similar. Record collection has been performed using a personalized health profiling approach where participants wore a pulse oximeter sensor for a fixed period of time and performed specific activities for pre-determined intervals. Using a variety of feature extraction in time, frequency and time-frequency domains, and data processing techniques, the data is fed into a CBR system which retrieves most similar cases and generates alarm and flag according to the case outcomes. The system has been compared with an expert's classification and 90% match is achieved between the expert's and CBR classification. Again, considering the clustered measurements the CBR approach classifies 93% correctly both for the pulse rate and oxygen saturation. Along with the proposed methodology, this paper provides a basis for which the system can be used in analysis of continuous health monitoring and be used as a suitable method as in home/remote monitoring systems.

  • 2.
    Ahmed, Mobyen Uddin
    et al.
    Örebro University, School of Science and Technology.
    Banaee, Hadi
    Örebro University, School of Science and Technology.
    Rafael-Palou, Xavier
    Barcelona Digital Technology Centre, Barcelona, Spain.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Intelligent Healthcare Services to Support Health Monitoring of Elderly2015In: INTERNET OF THINGS: USER-CENTRIC IOT, PT I, Springer, 2015, Vol. 150, p. 178-186Conference paper (Refereed)
    Abstract [en]

    This paper proposed an approach of intelligent healthcare services to support health monitoring of old people through the project named SAAPHO. Here, definition and architecture of the proposed healthcare services are presented considering six different health parameters such as: 1) physical activity, 2) blood pressure, 3) glucose, 4) medication compliance, 5) pulse monitoring and 6) weight monitoring. The outcome of the proposed services is evaluated in a case study where total 201 subjects from Spain and Slovenia are involved for user requirements analysis considering 1) end users, 2) clinicians, and 3) field study analysis perspectives. The result shows the potentiality and competence of the proposed healthcare services for the users.

  • 3.
    Ahmed, Mobyen Uddin
    et al.
    Örebro University, School of Science and Technology.
    Islam, Asif Moinul
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    A case-based patient identification system using pulseoximeter and a personalized health profile2012Conference paper (Refereed)
    Abstract [en]

    This paper proposes a case-based system framework in order to identify patient using their health parameters taken with physiological sensors. It combines a personalized health profiling protocol with a Case-Based Reasoning (CBR) approach. The personalized health profiling helps to determine a number of individual parameters which are important inputs for a clinician to make the final diagnosis and treatment plan. The proposed system uses a pulse oximeter that measures pulse rate and blood oxygen saturation. The measurements are taken through an android application in a smart phone which is connected with the pulseoximeter and bluetooth communication. The CBR approach helps clinicians to make a diagnosis, classification and treatment plan by retrieving the most similar previous case. The case may also be used to follow the treatment progress. Here, the cases are formulated with person’s contextual information and extracted features from sensor signal measurements. The features are extracted considering three domain analysis:1) time domain features using statistical measurement, 2) frequency domain features applying Fast Fourier Transform (FFT), and 3) time-frequency domain features applying Discrete Wavelet Transform (DWT). The initial result is acceptable that shows the advancement of the system while combining the personalized health profiling together with CBR.

  • 4.
    Ahmed, Mobyen Uddin
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Physical Activity Classification for Elderly based on Pulse Rate2013Conference paper (Refereed)
    Abstract [en]

    Physical activity is one of the key components for elderly in order to be actively ageing. However, it is difficult to differentiate and identify the body movement and actual physical activity using only accelerometer measurement. Therefore, this paper presents an application of case-based retrieval classification scheme to classify the physical activity of elderly based on pulse rate measurements. Here, case-based retrieval approach used the features extracted from both time and frequency domain. The evaluation result shows the best accuracy performance while considering the combination of time and frequency domain features. According to the evaluation result while considering the control measurements, the sensitivity, specificity and overall accuracy are achieved as 95%, 96% and 96% respectively. Considering the test dataset, the system was succeeded to identify 13 physical activities out of 16 i.e. the percentage of the correctness was 81%.

  • 5.
    Ahmed, Mobyen Uddin
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Physical activity identification using supervised machine learning and based on pulse rate2013In: International Journal of Advanced Computer Sciences and Applications, ISSN 2158-107X, E-ISSN 2156-5570, Vol. 4, no 7, p. 210-217Article in journal (Refereed)
    Abstract [en]

    Physical activity is one of the key components for elderly in order to be actively ageing. Pulse rate is a convenient physiological parameter to identify elderly’s physical activity since it increases with activity and decreases with rest. However, analysis and classification of pulse rate is often difficult due to personal variation during activity. This paper proposed a Case-Based Reasoning (CBR) approach to identify physical activity of elderly based on pulse rate. The proposed CBR approach has been compared with the two popular classification techniques, i.e. Support Vector Machine (SVM) and Neural Network (NN). The comparison has been conducted through an empirical experimental study where three experiments with 192 pulse rate measurement data are used. The experiment result shows that the proposed CBR approach outperforms the other two methods. Finally, the CBR approach identifies physical activity of elderly 84% accurately based on pulse rate

  • 6.
    Akalin, Neziha
    et al.
    Örebro University, School of Science and Technology.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Kristoffersson, Annica
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    An Evaluation Tool of the Effect of Robots in Eldercare on the Sense of Safety and Security2017In: Social Robotics: 9th International Conference, ICSR 2017, Tsukuba, Japan, November 22-24, 2017, Proceedings / [ed] Kheddar, A.; Yoshida, E.; Ge, S.S.; Suzuki, K.; Cabibihan, J-J:, Eyssel, F:, He, H., Springer International Publishing , 2017, p. 628-637Conference paper (Refereed)
    Abstract [en]

    The aim of the study presented in this paper is to develop a quantitative evaluation tool of the sense of safety and security for robots in eldercare. By investigating the literature on measurement of safety and security in human-robot interaction, we propose new evaluation tools. These tools are semantic differential scale questionnaires. In experimental validation, we used the Pepper robot, programmed in the way to exhibit social behaviors, and constructed four experimental conditions varying the degree of the robot’s non-verbal behaviors from no gestures at all to full head and hand movements. The experimental results suggest that both questionnaires (for the sense of safety and the sense of security) have good internal consistency.

  • 7.
    Akalin, Neziha
    et al.
    Örebro University, School of Science and Technology.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Kristoffersson, Annica
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Enhancing Social Human-Robot Interaction with Deep Reinforcement Learning.2018In: Proc. FAIM/ISCA Workshop on Artificial Intelligence for Multimodal Human Robot Interaction, 2018, MHRI , 2018, p. 48-50Conference paper (Refereed)
    Abstract [en]

    This research aims to develop an autonomous social robot for elderly individuals. The robot will learn from the interaction and change its behaviors in order to enhance the interaction and improve the user experience. For this purpose, we aim to use Deep Reinforcement Learning. The robot will observe the user’s verbal and nonverbal social cues by using its camera and microphone, the reward will be positive valence and engagement of the user.

  • 8.
    Alexopoulou, Sofia
    et al.
    Örebro University, School of Humanities, Education and Social Sciences.
    Fart, Frida
    Örebro University, School of Medical Sciences.
    Jonsson, Ann-Sofie
    Örebro University, School of Hospitality, Culinary Arts & Meal Science.
    Karni, Liran
    Örebro University, Örebro University School of Business.
    Kenalemang, Lame Maatla
    Örebro University, School of Humanities, Education and Social Sciences.
    Krishna, Sai
    Örebro University, School of Science and Technology.
    Lindblad, Katarina
    Örebro University, School of Music, Theatre and Art.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Lundin, Elin
    Örebro University, School of Health Sciences.
    Samzelius, Hanna
    Örebro University, School of Humanities, Education and Social Sciences.
    Schoultz, Magnus
    Örebro University, School of Humanities, Education and Social Sciences.
    Spang, Lisa
    Örebro University, School of Health Sciences.
    Söderman, Annika
    Örebro University, School of Health Sciences.
    Tarum, Janelle
    Örebro University, School of Health Sciences.
    Tsertsidis, Antonios
    Örebro University, Örebro University School of Business.
    Widell, Bettina
    Örebro University, School of Humanities, Education and Social Sciences.
    Nilsson, Kerstin (Editor)
    Örebro University, School of Medical Sciences.
    Successful ageing in an interdisciplinary context: popular science presentations2018Book (Other (popular science, discussion, etc.))
  • 9.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology. Örebro University, School of Law, Psychology and Social Work.
    Längkvist, Martin
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Exploiting Context and Semantics for UAV Path-finding in an Urban Setting2017In: Proceedings of the 1st International Workshop on Application of Semantic Web technologies in Robotics (AnSWeR 2017), Portoroz, Slovenia, May 29th, 2017 / [ed] Emanuele Bastianelli, Mathieu d'Aquin, Daniele Nardi, Technical University Aachen , 2017, p. 11-20Conference paper (Refereed)
    Abstract [en]

    In this paper we propose an ontology pattern that represents paths in a geo-representation model to be used in an aerial path planning processes. This pattern provides semantics related to constraints (i.e., ight forbidden zones) in a path planning problem in order to generate collision free paths. Our proposed approach has been applied on an ontology containing geo-regions extracted from satellite imagery data from a large urban city as an illustrative example.

  • 10.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Längkvist, Martin
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring2017In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 11, article id 2545Article in journal (Refereed)
    Abstract [en]

    This paper presents a framework in which satellite images are classified and augmented with additional semantic information to enable queries about what can be found on the map at a particular location, but also about paths that can be taken. This is achieved by a reasoning framework based on qualitative spatial reasoning that is able to find answers to high level queries that may vary on the current situation. This framework called SemCityMap, provides the full pipeline from enriching the raw image data with rudimentary labels to the integration of a knowledge representation and reasoning methods to user interfaces for high level querying. To illustrate the utility of SemCityMap in a disaster scenario, we use an urban environment—central Stockholm—in combination with a flood simulation. We show that the system provides useful answers to high-level queries also with respect to the current flood status. Examples of such queries concern path planning for vehicles or retrieval of safe regions such as “find all regions close to schools and far from the flooded area”. The particular advantage of our approach lies in the fact that ontological information and reasoning is explicitly integrated so that queries can be formulated in a natural way using concepts on appropriate level of abstraction, including additional constraints.

  • 11.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Knowing without telling: integrating sensing and mapping for creating an artificial companion2016In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, New York, NY, USA: Association for Computing Machinery (ACM), 2016, p. 11:1-11:4Conference paper (Refereed)
    Abstract [en]

    This paper depicts a sensor-based map navigation approach which targets users, who due to disabilities or lack of technical knowledge are currently not in the focus of map system developments for personalized information. What differentiates our approach from the state-of-art mostly integrating localized social media data, is that our vision is to integrate real time sensor generated data that indicates the situation of dfferent phenomena (such as the physiological functions of the body) related to the user. The challenge hereby is mainly related to knowledge representation and integration. The tentative impact of our vision for future navigation systems is re ected within a scenario.

  • 12.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Automated reasoning using abduction for interpretation of medical signals2014In: Journal of Biomedical Semantics, ISSN 2041-1480, E-ISSN 2041-1480, Vol. 5, article id 35Article in journal (Refereed)
    Abstract [en]

    This paper proposes an approach to leverage upon existing ontologies in order to automate the annotation of time series medical data. The annotation is achieved by an abductive reasoner using parsimonious covering theorem in order to determine the best explanation or annotation for specific user defined events in the data. The novelty of this approach resides in part by the system’s flexibility in how events are defined by users and later detected by the system. This is achieved via the use of different ontologies which find relations between medical, lexical and numerical concepts. A second contribution resides in the application of an abductive reasoner which uses the online and existing ontologies to provide annotations. The proposed method is evaluated on datasets collected from ICU patients and the generated annotations are compared against those given by medical experts.

  • 13.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Automatic annotation of sensor data streams using abductive reasoning2013In: Automatic Annotation of Sensor Data Streams using AbductiveReasoning, SCITEPRESS, 2013, p. 345-354Conference paper (Refereed)
  • 14.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Ontology alignment for classification of low level sensor data2012Conference paper (Refereed)
  • 15.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Reasoning for Improved Sensor Data Interpretation in a Smart Home2014Conference paper (Refereed)
    Abstract [en]

    In this paper an ontological representation and reasoning paradigm has been proposed for interpretation of time-series signals. The signals come from sensors observing a smart environment. The signal chosen for the annotation process is a set of unintuitive and complexgas sensor data. The ontology of this paradigm is inspired form the SSNontology (Semantic Sensor Network) and used for representation of both the sensor data and the contextual information. The interpretation process is mainly done by an incremental ASP solver which as input receivesa logic program that is generated from the contents of the ontology. The contextual information together with high level domain knowledge given in the ontology are used to infer explanations (answer sets) for changes in the ambient air detected by the gas sensors.

  • 16.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Reasoning for sensor data interpretation: an application to air quality monitoring2015In: Journal of Ambient Intelligence and Smart Environments, ISSN 1876-1364, E-ISSN 1876-1372, Vol. 7, no 4, p. 579-597Article in journal (Refereed)
    Abstract [en]

    In this paper we introduce a representation and reasoning model for the interpretation of time-series signals of a gas sensor situated in a sensor network. The interpretation process includes inferring high level explanations for changes detected over the gas signals. Inspired from the Semantic Sensor Network (SSN), the ontology used in this work provides an adaptive way of modelling the domain-related knowledge. Furthermore, exploiting (Incremental) Answer Set Programming (ASP) enables a declarative and automatic way of rule definition. Converting the ontology concepts and relations into ASP logic programs, the interpretation process defines a logic program whose answer sets are considered as eventual explanations for the detected changes in the gas sensor signals. The proposed approach is tested in a kitchen environment which contains several objects monitored by different sensors. The contextual information provided by the sensor network together with high level domain knowledge are used to infer explanations for changes in the ambient air detected by the gas sensors.

  • 17.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Towards Automatic Ontology Alignment for Enriching Sensor Data Analysis2013In: Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937, Vol. 415, p. 179-193Article in journal (Refereed)
    Abstract [en]

    In this work ontology alignment is used to align an ontology comprising high level knowledge to a structure representing the results of low-level sensor data classification. To resolve inherent uncertainties from the data driven classifier, an ontology about application domain is aligned to the classifier output and the result is recommendation system able to suggest a course of action that will resolve the uncertainty. This work is instantiated in a medical application domain where signals from an electronic nose are classified into different bacteria types. In case of misclassifications resulting from the data driven classifier, the alignment to an ontology representing traditional microbiology tests suggests a subset of tests most relevant to use. The result is a hybrid classification system (electronic nose and traditional testing) that automatically exploits domain knowledge in the identification process.

  • 18.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Längkvist, Martin
    Örebro University, School of Science and Technology.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Open GeoSpatial Data as a Source of Ground Truth for Automated Labelling of Satellite Images2016In: SDW 2016: Spatial Data on the Web, Proceedings / [ed] Krzysztof Janowicz et al., CEUR Workshop Proceedings , 2016, p. 5-8Conference paper (Refereed)
  • 19.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Längkvist, Martin
    Örebro University, School of Science and Technology.
    Sioutis, Michael
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    A Symbolic Approach for Explaining Errors in Image Classification Tasks2018Conference paper (Refereed)
    Abstract [en]

    Machine learning algorithms, despite their increasing success in handling object recognition tasks, still seldom perform without error. Often the process of understanding why the algorithm has failed is the task of the human who, using domain knowledge and contextual information, can discover systematic shortcomings in either the data or the algorithm. This paper presents an approach where the process of reasoning about errors emerging from a machine learning framework is automated using symbolic techniques. By utilizing spatial and geometrical reasoning between objects in a scene, the system is able to describe misclassified regions in relation to its context. The system is demonstrated in the remote sensing domain where objects and entities are detected in satellite images.

  • 20.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Context Recognition: Towards Automatic Query Generation2015In: Ambient Intelligence: 12th European Conference, AmI 2015, Athens, Greece, November 11-13, 2015, Proceedings, Springer, 2015, p. 205-218Conference paper (Refereed)
    Abstract [en]

    In this paper, we present an ontology-based approach in designing knowledge model for context recognition (CR) systems. The main focus in this paper is on the use of an ontology to facilitate the generation of user-based queries to the CR system. By leveraging from the ontology, users need not know about sensor details and the structure of the ontology in expressing queries related to events of interest. To validate the approach and demonstrate the flexibility of the ontology for query generation, the ontology has been integrated in two separate application domains. The first domain considers a health care system implemented for the GiraffPlus project where the query generation process is automated to request information about activities of daily living. The second application uses the same ontology for an air quality monitoring application in the home. Since these two systems are independently developed for different purposes, the ease of applying the ontology upon them can be considered as a credit for its generality.

  • 21.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Renoux, Jennifer
    Örebro University, School of Science and Technology.
    Köckemann, Uwe
    Örebro University, School of Science and Technology.
    Kristoffersson, Annica
    Örebro University, School of Science and Technology.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Blomqvist, Eva
    RISE SICS East, Linköping, Sweden.
    Tsiftes, Nicolas
    RISE SICS, Stockholm, Sweden.
    Voigt, Thiemo
    RISE SICS, Stockholm, Sweden.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    An Ontology-based Context-aware System for Smart Homes: E-care@home2017In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 7, article id 1586Article in journal (Refereed)
    Abstract [en]

    Smart home environments have a significant potential to provide for long-term monitoring of users with special needs in order to promote the possibility to age at home. Such environments are typically equipped with a number of heterogeneous sensors that monitor both health and environmental parameters. This paper presents a framework called E-care@home, consisting of an IoT infrastructure, which provides information with an unambiguous, shared meaning across IoT devices, end-users, relatives, health and care professionals and organizations. We focus on integrating measurements gathered from heterogeneous sources by using ontologies in order to enable semantic interpretation of events and context awareness. Activities are deduced using an incremental answer set solver for stream reasoning. The paper demonstrates the proposed framework using an instantiation of a smart environment that is able to perform context recognition based on the activities and the events occurring in the home.

  • 22.
    Asadi, Sahar
    et al.
    Örebro University, School of Science and Technology.
    Pashami, Sepideh
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    TD Kernel DM+V: time-dependent statistical gas distribution modelling on simulated measurements2011In: Olfaction and Electronic Nose: proceedings of the 14th International Symposium on Olfaction and Electronic Nose (ISOEN) / [ed] Perena Gouma, Springer Science+Business Media B.V., 2011, p. 281-282Conference paper (Refereed)
    Abstract [en]

    To study gas dispersion, several statistical gas distribution modelling approaches have been proposed recently. A crucial assumption in these approaches is that gas distribution models are learned from measurements that are generated by a time-invariant random process. While a time-independent random process can capture certain fluctuations in the gas distribution, more accurate models can be obtained by modelling changes in the random process over time. In this work we propose a time-scale parameter that relates the age of measurements to their validity for building the gas distribution model in a recency function. The parameters of the recency function define a time-scale and can be learned. The time-scale represents a compromise between two conflicting requirements for obtaining accurate gas distribution models: using as many measurements as possible and using only very recent measurements. We have studied several recency functions in a time-dependent extension of the Kernel DM+V algorithm (TD Kernel DM+V). Based on real-world experiments and simulations of gas dispersal (presented in this paper) we demonstrate that TD Kernel DM+V improves the obtained gas distribution models in dynamic situations. This represents an important step towards statistical modelling of evolving gas distributions.

  • 23.
    Banaee, Hadi
    et al.
    Örebro University, School of Science and Technology.
    Ahmed, Mobyen Uddin
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    A framework for automatic text generation of trends in physiological time series data2013In: IEEE International Conference on Systems, Man, and Cybernetics, 13-16 Oct. 2013, Manchester, IEEE conference proceedings, 2013, p. 3876-3881Conference paper (Refereed)
    Abstract [en]

    Health monitoring systems using wearable sensorshave rapidly grown in the biomedical community. The mainchallenges in physiological data monitoring are to analyse largevolumes of health measurements and to represent the acquiredinformation. Natural language generation is an effective methodto create summaries for both clinicians and patients as it candescribe useful information extracted from sensor data in textualformat. This paper presents a framework of a natural languagegeneration system that provides a text-based representation ofthe extracted numeric information from physiological sensorsignals. More specifically, a new partial trend detection algorithmis introduced to capture the particular changes and events ofhealth parameters. The extracted information is then representedconsidering linguistic characterisation of numeric features. Ex-perimental analysis was performed using a wearable sensor and demonstrates a possible output in natural language text.

  • 24.
    Banaee, Hadi
    et al.
    Örebro University, School of Science and Technology.
    Ahmed, Mobyen Uddin
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges2013In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 13, no 12, p. 17472-17500Article in journal (Refereed)
    Abstract [en]

    The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems

  • 25.
    Banaee, Hadi
    et al.
    Örebro University, School of Science and Technology.
    Ahmed, Mobyen Uddin
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Descriptive Modelling of Clinical Conditions with Data-driven Rule Mining in Physiological Data2015In: Proceedings of the 8th International conference of Health Informatics (HEALTHINF 2015), SciTePress, 2015Conference paper (Refereed)
    Abstract [en]

    This paper presents an approach to automatically mine rules in time series data representing physiologicalparameters in clinical conditions. The approach is fully data driven, where prototypical patterns are mined foreach physiological time series data. The generated rules based on the prototypical patterns are then describedin a textual representation which captures trends in each physiological parameter and their relation to the otherphysiological data. In this paper, a method for measuring similarity of rule sets is introduced in order tovalidate the uniqueness of rule sets. This method is evaluated on physiological records from clinical classesin the MIMIC online database such as angina, sepsis, respiratory failure, etc.. The results show that the rulemining technique is able to acquire a distinctive model for each clinical condition, and represent the generatedrules in a human understandable textual representation

  • 26.
    Banaee, Hadi
    et al.
    Örebro University, School of Science and Technology.
    Ahmed, Mobyen Uddin
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Towards NLG for Physiological Data Monitoring with Body Area Networks2013In: 14th European Workshop on Natural Language Generation, 2013, p. 193-197Conference paper (Refereed)
    Abstract [en]

    This position paper presents an on-goingwork on a natural language generationframework that is particularly tailored fornatural language generation from bodyarea networks. We present an overview ofthe main challenges when considering thistype of sensor devices used for at homemonitoring of health parameters. The paperpresents the first steps towards the implementationof a system which collectsinformation from heart rate and respirationusing a wearable sensor.

  • 27.
    Banaee, Hadi
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Data-driven rule mining and representation of temporal patterns in physiological sensor data2015In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 19, no 5, p. 1557-1566Article in journal (Refereed)
    Abstract [en]

    Mining and representation of qualitative patterns is a growing field in sensor data analytics. This paper leverages from rule mining techniques to extract and represent temporal relation of prototypical patterns in clinical data streams. The approach is fully data-driven, where the temporal rules are mined from physiological time series such as heart rate, respiration rate, and blood pressure. To validate the rules, a novel similarity method is introduced, that compares the similarity between rule sets. An additional aspect of the proposed approach has been to utilize natural language generation techniques to represent the temporal relations between patterns. In this study, the sensor data in the MIMIC online database was used for evaluation, in which the mined temporal rules as they relate to various clinical conditions (respiratory failure, angina, sepsis, ...) were made explicit as a textual representation. Furthermore, it was shown that the extracted rule set for any particular clinical condition was distinct from other clinical conditions.

  • 28.
    Banaee, Hadi
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Using Conceptual Spaces to Model Domain Knowledge in Data-to-Text Systems2014In: Proceedings of the 8th International Natural Language Generation Conference, Association for Computational Linguistics, 2014, p. 11-15Conference paper (Refereed)
    Abstract [en]

    This position paper introduces the utilityof the conceptual spaces theory to conceptualisethe acquired knowledge in data-totextsystems. A use case of the proposedmethod is presented for text generationsystems dealing with sensor data. Modellinginformation in a conceptual spaceexploits a spatial representation of domainknowledge in order to perceive unexpectedobservations. This ongoing work aimsto apply conceptual spaces in NLG forgrounding numeric information into thesymbolic representation and confrontingthe important step of acquiring adequateknowledge in data-to-text systems.

  • 29.
    Banaee, Hadi
    et al.
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Data-Driven Conceptual Spaces: Creating Semantic Representations for Linguistic Descriptions of Numerical Data2018In: The journal of artificial intelligence research, ISSN 1076-9757, E-ISSN 1943-5037, Vol. 63, p. 691-742Article in journal (Refereed)
    Abstract [en]

    There is an increasing need to derive semantics from real-world observations to facilitate natural information sharing between machine and human. Conceptual spaces theory is a possible approach and has been proposed as mid-level representation between symbolic and sub-symbolic representations, whereby concepts are represented in a geometrical space that is characterised by a number of quality dimensions. Currently, much of the work has demonstrated how conceptual spaces are created in a knowledge-driven manner, relying on prior knowledge to form concepts and identify quality dimensions. This paper presents a method to create semantic representations using data-driven conceptual spaces which are then used to derive linguistic descriptions of numerical data. Our contribution is a principled approach to automatically construct a conceptual space from a set of known observations wherein the quality dimensions and domains are not known a priori. This novelty of the approach is the ability to select and group semantic features to discriminate between concepts in a data-driven manner while preserving the semantic interpretation that is needed to infer linguistic descriptions for interaction with humans. Two data sets representing leaf images and time series signals are used to evaluate the method. An empirical evaluation for each case study assesses how well linguistic descriptions generated from the conceptual spaces identify unknown observations. Furthermore,  comparisons are made with descriptions derived on alternative approaches for generating semantic models.

  • 30.
    Beeson, Patrick
    et al.
    TRACLabs Inc., Webster TX, USA.
    Kortenkamp, David
    TRACLabs Inc., Webster TX, USA.
    Bonasso, R. Peter
    TRACLabs Inc., Webster TX, USA.
    Persson, Andreas
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Bona, Jonathan P
    State University of New York, Buffalo, USA.
    An Ontology-Based Symbol Grounding System for Human-Robot Interaction2014In: Artificial Intelligence for Human-Robot Interaction: 2014 AAAI Fall Symposium, AAAI Press, 2014Conference paper (Refereed)
    Abstract [en]

    This paper presents an ongoing collaboration to develop a perceptual anchoring framework which creates and maintains the symbol-percept links concerning household objects. The paper presents an approach to non-trivialize the symbol system using ontologies and allow for HRI via enabling queries about objects properties, their affordances, and their perceptual characteristics as viewed from the robot (e.g. last seen). This position paper describes in brief the objective of creating a long term perceptual anchoring framework for HRI and outlines the preliminary work done this far.

  • 31.
    Broxvall, Mathias
    et al.
    Örebro University, Department of Technology.
    Coradeschi, Silvia
    Örebro University, Department of Technology.
    Loutfi, Amy
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    An ecological approach to odour recognition in intelligent environments2006In: 2006 IEEE International Conference on Robotics and automation, ICRA 2006, 2006, p. 2066-2071Conference paper (Refereed)
    Abstract [en]

    We present a new approach for odour detection and recognition based on a so-called PEIS-Ecology: a network of gas sensors and a mobile robot are integrated in an intelligent environment. The environment can provide information regarding the location of potential odour sources, which is then relayed to a mobile robot equipped with an electronic nose. The robot can then perform a more thorough analysis of the odour character. This is a novel approach which alleviates some the challenges in mobile olfaction techniques by single and embedded mobile robots. The environment also provides contextual information which can be used to constrain the learning of odours, which is shown to improve classification performance.

  • 32.
    Broxvall, Mathias
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Interacting with a robot ecology using task templates2007In: 2007 RO-MAN: 16TH IEEE INTERNATIONAL SYMPOSIUM ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION, VOLS 1-3, NEW YORK: IEEE , 2007, p. 486-491Chapter in book (Other academic)
    Abstract [en]

    Robot ecologies provide a new paradigm for assistive, service, industrial, and entertainment robotics which is quickly gaining popularity. These ecologies contain a large number of robotic components pervasively embedded in the environment and interacting with each other. Human users of such systems need to be able to interface with both the system as a whole and, if desired, which each individual component. The humans should be able to transmit, in a natural way, commands that range from basic ones, such as ''turn on the lights in the bedroom'', to abstract ones, such as ''bring me a cup of coffee''. Human users may also need to interact with task execution especially at decision points. In this paper, we introduce an approach to interface a human user to a specific type of robot ecology, called an ecology of Physically Embedded Intelligent Systems, or PEIS-Ecology. The ecology includes simple sensors and actuators and more complicated devices such as mobile robots. The proposed interface satisfies two requirements: 1) to easily and automatically generate component interfaces, and 2) to provide a simple mechanism by which to request and monitor the execution of tasks in the ecology.

  • 33.
    Broxvall, Mathias
    et al.
    Örebro University, Department of Technology.
    Loutfi, Amy
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Interacting with a robot ecology using task templates2007In: 16th IEEE international symposium on robot and human interactive communication, RO-MAN 2007, New York: IEEE , 2007, p. 487-492Conference paper (Refereed)
    Abstract [en]

    Robot ecologies provide a new paradigm for assistive, service, industrial, and entertainment robotics which is quickly gaining popularity. These ecologies contain a large number of robotic components pervasively embedded in the environment and interacting with each other. Human users of such systems need to be able to interface with both the system as a w hole and, if desired, which each individual component. The humans should be able to transmit, in a natural way, commands that range from basic ones, such as "turn on the lights in the bedroom", to abstract ones, such as "bring me a cup of coffee". Human users may also need to interact with task execution, especially at decision points. In this paper, we introduce an approach to interface a human user to a specific type of robot ecology, called an ecology of Physically Embedded Intelligent Systems, or PEIS-Ecology. The ecology includes simple sensors and actuators and more complicated devices such as mobile robots. The proposed interface satisfies two requirements: 1) to easily and automatically generate component interfaces, and 2) to provide a simple mechanism by which to request and monitor the execution of tasks in the ecology.

  • 34.
    Coradeschi, Silvia
    et al.
    Örebro University, School of Science and Technology.
    Cesta, Amadeo
    Consiglio Nazionale Delle Ricerche, Bari, Italy.
    Cortellessa, Gabriella
    Consiglio Nazionale Delle Ricerche, Bari, Italy.
    Coraci, Luca
    Consiglio Nazionale Delle Ricerche, Bari, Italy.
    Gonzalez, Javier
    Málaga University, Málaga, Spain.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Furfari, Francesco
    Consiglio Nazionale Delle Ricerche, Bari, Italy.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Orlandini, Andrea
    Consiglio Nazionale Delle Ricerche, Bari, Italy.
    Palumbo, Filippo
    Consiglio Nazionale Delle Ricerche, Bari, Italy.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    von Rump, Stephan
    Giraff AB, Stockholm, Sweden.
    Ullberg, Jonas
    Örebro University, School of Science and Technology.
    Östlund, Britt
    Lund University, Lund, Sweden.
    GiraffPlus: combining social interaction and long term monitoring for promoting independent living2013In: 2013 6TH INTERNATIONAL CONFERENCE ON HUMAN SYSTEM INTERACTIONS (HSI), New York, 2013, p. 578-585Conference paper (Refereed)
    Abstract [en]

    Early detection and adaptive support to changing individual needs related to ageing is an important challenge in today’s society. In this paper we present a system called GiraffPlus that aims at addressing such a challenge and is developed in an on-going European project. The system consists of a network of home sensors that can be automatically configured to collect data for a range of monitoring services; a semi-autonomous telepresence robot; a sophisticated context recognition system that can give high-level and long term interpretations of the collected data and respond to certain events; and personalized services delivered through adaptive user interfaces for primary users. The system performs a range of services including data collection and analysis of long term trends in behaviors and physiological parameters (e.g. relating to sleep or daily activity); warnings, alarms and reminders; and social interaction through the telepresence robot. The latter is based on the Giraff telepresence robot, which is already in place in a number of homes. Particular emphasis is put on user evaluation outside the laboratories. A distinctive aspect of the project is that the GiraffPlus system will be installed and evaluated in at least 15 homes of elderly people. The concept of “useworthiness” is central in order to assure that the GiraffPlus system provides services that are easy to use and worth using. In addition, by using existing and affordable components we strive to achieve a system that is affordable and close to commercialization.

  • 35.
    Coradeschi, Silvia
    et al.
    Örebro University, School of Science and Technology.
    Cortellessa, GabriellaInstitute of Cognitive Science and Technology, National Research Council of Italy, Rome, Italy.Kristoffersson, AnnicaÖrebro University, School of Science and Technology.Loutfi, AmyÖrebro University, School of Science and Technology.
    Proceedings of the Ro-man 2012 Workshop on Social Robotics Telepresence2012Conference proceedings (editor) (Other academic)
  • 36.
    Coradeschi, Silvia
    et al.
    School of Science and Technology, Örebro University, Örebro, Sweden.
    Cortellessa, GabriellaCNR - National Research Council of Italy, Istituto di Scienze e Tecnologie della Cognizione, Rome, Italy.Kristoffersson, AnnicaÖrebro University, School of Science and Technology.Loutfi, AmyÖrebro University, School of Science and Technology.Severinson Eklundh, KerstinCI Group, CSC, KTH Royal Institute of Technology, Stockholm, Sweden.
    Proceedings of the 2011 HRI Workshop on Social Robotic Telepresence2011Conference proceedings (editor) (Other academic)
  • 37.
    Coradeschi, Silvia
    et al.
    Örebro University, School of Science and Technology.
    Kristoffersson, Annica
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Von Rump, Stephen
    Cesta, Amedeo
    Cortellessa, Gabriella
    Gonzalez, Javier
    Towards a methodology for longitudinal evaluation of social robotic telepresence for elderly2011In: 1st Workshop on Social Robotic Telepresence at HRI 2011, 2011Conference paper (Refereed)
    Abstract [en]

    This paper describes a methodology for performing longitudinal evaluations when a social robotic telepresence system is deployed in realistic environments. This work is the core of an Ambient Assisted Living Project called ExCITE, Enabling Social Interaction Through Telepresence. The ExCITE project is geared towards an elderly audience and has as aim to increase social interaction among elderly, their family and healthcare services by using robotic telepresence. The robotic system used in the project is called the Giraff robot and over a three year period, prototypes of this platform are deployed at a number of test-sites in different European countries where user feedback is collected and fedback into the refinement of the prototype. In this paper, we discuss the methodology of ExCITE in particular relation to other methodologies for longitudinal evaluation. The paper also provides a discussion of the possible pitfalls and risks in performing longitudinal studies of this nature particularly as they relate to social robotic telepresence technologies.

  • 38.
    Coradeschi, Silvia
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    A review of Past and Future Trends in Perceptual Anchoring2008In: Tools in Artificial Intelligence, Vienna: I-Tech Eduacation and Publishing , 2008Chapter in book (Other academic)
  • 39.
    Coradeschi, Silvia
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Kristoffersson, Annica
    Örebro University, School of Science and Technology.
    Cortellessa, Gabriella
    Consiglio Nazionale delle Ricerche (CNR), Rome, Italy; Istituto di Scienze e Tecnologie della Cognizione (ISTC-CNR), Rome, Italy.
    Severinson Eklundh, Kerstin
    KTH, Royal Institute of Technology, Stockholm, Sweden.
    Social robotic telepresence2011In: the 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI 2011).HRI, ACM Digital Library , 2011, p. 5-6Conference paper (Refereed)
  • 40.
    Coradeschi, Silvia
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Wrede, Britta
    Bielefeld University, Bielefeld, Germany.
    A short review of symbol grounding in robotic and intelligent systems2013In: Künstliche Intelligenz, ISSN 0933-1875, E-ISSN 1610-1987, Vol. 27, no 2, p. 129-136Article in journal (Refereed)
    Abstract [en]

    This paper gives an overview of the research papers published in Symbol Grounding in the period from the beginning of the 21st century up 2012. The focus is in the use of symbol grounding for robotics and intelligent system. The review covers a number of subtopics, that include, physical symbol grounding, social symbol grounding, symbol grounding for vision systems, anchoring in robotic systems, and learning symbol grounding in software systems and robotics. This review is published in conjunction with a special issue on Symbol Grounding in the Künstliche Intelligenz Journal.

  • 41. Cortellessa, Gabriella
    et al.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    An on-going evaluation of domestic robots2008In: Robotic helpers: user interaction, interfaces and companions in assistive and therapy robotics, 2008, p. 87-91Conference paper (Refereed)
    Abstract [en]

    In this position paper we describe an on-going effort to provide an in-depth and cross-cultural evaluation of how elderly users perceive robotic systems for domestic cognitive support. Our work is grounded on two implemented smarthome prototypes, namely the RoboCare Smart Home developed in Italy, and the PEIS Home developed in Sweden. The former project has provided a testbed for an a-posteriori evaluation of smart home technology with Italian user groups. The presence in Sweden of the PEIS Home, a system which shares numerous commonalities with the RoboCare Smart Home, gives us the opportunity to extend these results by (1) providing a cross-cultural perspective on the perception of smart home technology, and (2) lay the foundations for a live, Wizard of Oz based evaluation within the PEIS Home.

     

  • 42. Cortellessa, Gabriella
    et al.
    Scopelliti, Massimiliano
    Tiberio, Lorenza
    Koch Svedberg, Gion
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    A cross-cultural evaluation of domestic assistive robots2008In: AAAI fall symposium: technical report, v FS-08-02, American Association for Artificial Intelligence , 2008, p. 24-31Conference paper (Refereed)
    Abstract [en]

    This paper presents the first steps in a series of on-going user evaluations of intelligent environments for supporting elderly users at home. We specifically focus on a comparison of elderly perceptions of social assistive domestic robots between Italian and Swedish user groups. The evaluation was carried out in Rome, Italy and O¨ rebro, Sweden, including surrounding towns. The results, obtained through a videobased methodology, highlight the variety in level of appreciation of domestic robots for elderly care as it relates to a number of aspects of culture which are not necessarily trivial to identify. Our results suggest some specific factors as important for interpreting the difference in perception, e.g., the user’s acquaintance with ICT (Information and Communication Technology) and the social policies implemented in the two countries. Also, the results show interesting commonalities, such as the general agreement among Swedish and Italian user groups on the physical aspect of the robot.

  • 43.
    d. C. Silva-Lopez, Lia Susana
    et al.
    Örebro University, School of Science and Technology.
    Broxvall, Mathias
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Towards configuration planning with partially ordered preferences: representation and results2015In: Künstliche Intelligenz, ISSN 0933-1875, E-ISSN 1610-1987, Vol. 9, no 2, p. 173-183Article in journal (Refereed)
    Abstract [en]

    Configuration planning for a distributed robotic system is the problem of how to configure the system over time in order to achieve some causal and/or information goals. A configuration plan specifies what components (sensor, actuator and computational devices), should be active at different times and how they should exchange information. However, not all plans that solve a given problem need to be equally good, and for that purpose it may be important to take preferences into account. In this paper we present an algorithm for configuration planning that incorporates general partially ordered preferences. The planner supports multiple preference categories, and hence it solves a multiple-objective optimization problem: for a given problem, it finds all possible valid, non-dominated configuration plans. The planner has been able to successfully cope with partial ordering relations between quantitative preferences in practically acceptable times, as shown in the empirical results. Preferences here are represented as c-semirings, and are used for establishing dominance of a solution over another in order to obtain a set of configuration plans that will constitute the solution of a configuration planning problem with partially ordered preferences. The dominance operators tested in this paper are Pareto and Lorenz dominance. Our solver considers one guiding heuristic for obtaining the first solution, and then switches to a dominance based monotonically decreasing heuristic used for pruning dominated partial configuration plans. In our empirical results, we perform a statistical study in the space of problem instances and establish families of problems for which our approach is computationally feasible.

  • 44. Dahlbom, Anders
    et al.
    Niklasson, Lars
    Falkman, Göran
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Towards template-based situation recognition2009In:  Intelligent sensing, situation management, impact assessment, and cyber-sensing / [ed] Stephen Mott, John F. Buford, Gabriel Jakobson, Michael J. Mendenhall, 2009, Vol. 7352, no 1, p. 7352 05-Conference paper (Other academic)
    Abstract [en]

    The process of tracking and identifying developing situations is an ability of importance within the surveillance domain. We refer to this as situation recognition and believe that it can enhance situation awareness for decision makers. Situation recognition requires that many subproblems are solved. For instance, we need to establish which situations are interesting, how to represent these situations, and which inferable events and states that can be used for representing them. We also need to know how to track and identify situations and how to determine the correlation between present information about situations with knowledge. For some of these subproblems, data-driven approaches are suitable, whilst knowledge-driven approaches are more suitable for others. In this paper we discuss our current research efforts and goals concerning template-based situation recognition. We provide a categorization of approaches for situation recognition together with a formalization of the template-based situation re ognition problem. We also discuss this formalization in the light of a pick-pocket scenario. Finally, we discuss future directions for our research on situation recognition. We conclude that situation recognition is an important problem to look into for enhancing the overall situation awareness of decision makers. ©2009 SPIE.

  • 45.
    Daoutis, Marios
    et al.
    Örebro University, School of Science and Technology.
    Coradeschi, Silvia
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Cooperative knowledge based perceptual anchoring2012In: International journal on artificial intelligence tools, ISSN 0218-2130, Vol. 21, no 3, article id 1250012Article in journal (Refereed)
    Abstract [en]

    In settings where heterogenous robotic systems interact with humans, information from the environment must be systematically captured, organized and maintained in time. In this work, we propose a model for connecting perceptual information to semantic information in a multi-agent setting. In particular, we present semantic cooperative perceptual anchoring, that captures collectively acquired perceptual information and connects it to semantically expressed commonsense knowledge. We describe how we implemented the proposed model in a smart environment, using different modern perceptual and knowledge representation techniques. We present the results of the systemand investigate different scenarios in which we use the common sense together with perceptual knowledge, for communication, reasoning and exchange of information.

  • 46.
    Daoutis, Marios
    et al.
    Örebro University, School of Science and Technology.
    Coradeschi, Silvia
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Grounding commonsense knowledge in intelligent systems2009In: Journal of Ambient Intelligence and Smart Environments, ISSN 1876-1364, E-ISSN 1876-1372, Vol. 1, no 4, p. 311-321Article in journal (Refereed)
    Abstract [en]

    Ambient environments which integrate a number of sensing devices and actuators intended for use by human users need to be able to express knowledge about objects, their functions and their properties to assist in the performance of everyday tasks. For this to occur perceptual data must be grounded to symbolic information that in its turn can be used in the communication with the human. For symbolic information to be meaningful it should be part of a rich knowledge base that includes an ontology of concepts and common sense. In this work we present an integration between ResearchCyc and an anchoring framework that mediates the connection between the perceptual information in an intelligent home environment and the reasoning system. Through simple dialogues we validate how objects placed in the home environment are grounded by a network of sensors and made available to a larger KB where reasoning is exploited. This first integration work is a step towards integrating the richness of a KRR system developed over many years in isolation, with a physically embedded intelligent system.

  • 47.
    Daoutis, Marios
    et al.
    Örebro University, School of Science and Technology.
    Coradeschi, Silvia
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Integrating common sense in physically embedded intelligent systems2009In: Intelligent environments 2009 / [ed] V. Callaghan, A. Kameas, A. Reyes, D. Royo, M. Weber, Amsterdam: IOS Press , 2009, p. 212-219Conference paper (Refereed)
    Abstract [en]

    In this paper we describe an implemented framework that integrates knowledge representation and reasoning in a symbiotic system. In such systems a number of heterogeneous sensors pervasively embedded in the environment, mobile robots and humans co-exist and communicate. In this work, the integration is mediated through perceptual anchoring, which creates and maintains the correspondences between the symbol system and the perceptual data that refer to the same physical object. The overall framework is evaluated using ResearchCyc as the knowledge representation and reasoning system, within the context of a physical testbed, which consists of a small apartment-like home.

  • 48.
    Daoutis, Marios
    et al.
    Örebro University, School of Science and Technology.
    Coradeschi, Silvia
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Knowledge representation for anchoring symbolic concepts to perceptual data2012In: Bridges between the Methodological and Practical Work of the Robotics and Cognitive Systems Communities - From Sensors to Concepts / [ed] Springet Publishing, Springer Publishing Company, 2012Chapter in book (Refereed)
  • 49.
    Daoutis, Marios
    et al.
    Örebro University, School of Science and Technology.
    Coradeschi, Silvia
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Towards concept anchoring for cognitive robots2012In: Intelligent Service Robotics, ISSN 1861-2784, Vol. 5, no 4, p. 213-228Article in journal (Refereed)
    Abstract [en]

    We present a model for anchoring categorical conceptual information which originates from physical perception and the web. The model is an extension of the anchoring framework which is used to create and maintain over time semantically grounded sensor information. Using the augmented anchoring framework that employs complex symbolic knowledge from a commonsense knowledge base, we attempt to ground and integrate symbolic and perceptual data that are available on the web. We introduce conceptual anchors which are representations of general, concrete conceptual terms. We show in an example scenario how conceptual anchors can be coherently integrated with perceptual anchors and commonsense information for the acquisition of novel concepts.

  • 50.
    Di Lello, Enrico
    et al.
    Dept. of Informatics and Automation, University Roma-3, Italy.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Robotic furniture in a smart environment: the PEIS table2009In: Workshops Proceedings of the 5th International Conference on Intelligent Environments / [ed] Michael Schneider et al., Amsterdam: IOS Press, 2009, p. 185-192Conference paper (Refereed)
    Abstract [en]

    According to a recent trend, robotic technologies will be included into domestic environments in the form of simple, networked robotic devices able to cooperate in the performance of tasks. These devices may take the form of smart appliances, distributed sensors, or robotic furniture. In this paper, we describe the design of an autonomous robotic table and its inclusion in a smart environment, the PEIS Ecology. The design takes into account the constraints posed by the domestic environment. The robotic table can perform autonomous point-to-point navigation, and it can collaborate with the other devices in the ecology to perform complex tasks that go beyond simple navigation.

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