1,720,991 research outputs found
POLARIS: Probabilistic and Ontological Activity Recognition in Smart-homes
Full text linkRecognition of activities of daily living (ADLs) is an enabling technology for several ubiquitous computing applications. Most activity recognition systems rely on supervised learning to extract activity models from labeled datasets. A problem with that approach is the acquisition of comprehensive activity datasets, which is an expensive task. The problem is particularly challenging when focusing on complex ADLs characterized by large variability of execution. Moreover, several activity recognition systems are limited to offline recognition, while many applications claim for online activity recognition. In this paper, we propose POLARIS, a framework for unsupervised activity recognition. POLARIS can recognize complex ADLs exploiting the semantics of activities, context data, and sensors. Through ontological reasoning, our algorithm derives semantic correlations among activities and sensor events. By matching observed events with semantic correlations, a statistical reasoner formulates initial hypotheses about the occurred activities. Those hypotheses are refined through probabilistic reasoning, exploiting semantic constraints derived from the ontology. Our system supports online recognition, thanks to a novel segmentation algorithm. Extensive experiments with real-world datasets show that the accuracy of our unsupervised method is comparable to the one of supervised approaches. Moreover, the online version of our system achieves essentially the same accuracy of the offline version
newNECTAR : Collaborative active learning for knowledge-based probabilistic activity recognition
Full text linkThe increasing popularity of ambient assisted living solutions is claiming adaptive and scalable tools to monitor activities of daily living. Currently, most sensor-based activity recognition techniques rely on supervised learning algorithms. However, the acquisition of comprehensive training sets of activities in smart homes is expensive and violates the individual's privacy. In this work, we address this problem by proposing a novel hybrid approach that couples collaborative active learning with probabilistic and knowledge-based reasoning. The rationale of our approach is that a generic, and possibly incomplete, knowledge-based model of activities can be refined to target specific individuals and environments by collaboratively acquiring feedback from inhabitants. Specifically, we propose a collaborative active learning method exploiting users’ feedback to (i) refine correlations among sensor events and activity types that are initially extracted from a high-level ontology, and (ii) mine temporal patterns of sensor events that are frequently generated by the execution of specific activities. A Markov Logic Network is used to recognize activities with probabilistic rules that capture both the ontological knowledge and the information obtained by active learning. We experimented our solution with a real-world dataset of activities carried out by several individuals in an interleaved fashion. Experimental results show that our collaborative and personalized active learning solution significantly improves recognition rates, while triggering a small number of feedback requests. Moreover, the overall recognition rates compare favorably with existing supervised and unsupervised activity recognition methods
NECTAR: Knowledge-based collaborative active learning for activity recognition
No full textDue to the emerging popularity of pervasive healthcare applications, tools for monitoring activities in smart homes are gaining momentum. Existing methods mainly rely on supervised learning algorithms for recognizing activities based on sensor data. A key issue with those approaches is the acquisition of comprehensive training sets of activities. Indeed, that task incurs relevant costs in terms of manual labeling effort; moreover, labeling by external observers violates the individual's privacy. For these reasons, there is an increasing interest in unsupervised activity recognition methods. A popular approach relies on knowledge-based models expressed by ontologies of activities, environment and sensors. Unfortunately, those models require significant knowledge engineering efforts, and are often limited to a specific application. Our intuition is that a generic knowledge-based model of activities can be refined to target specific individuals and environments by collaboratively acquiring feedback from inhabitants. Specifically, we propose a collaborative active learning method to refine correlations among sensor events and activity types that are initially extracted from a high-level ontology. Generic correlations are personalized to each target smart-home considering the similarity between the feedback target and the feedback provider in terms of environment and inhabitant's profiles. Moreover, thanks to this method, new sensors installed in the home are seamlessly integrated in the recognition framework. In order to reduce the burden of providing feedback, we also propose a technique to carefully select the conditions that trigger a feedback request. We conducted experiments with a real-world dataset and a generic ontology of activities. Results show that our hybrid method outperforms state-of-the-art supervised and unsupervised activity recognition techniques while triggering an acceptable number of feedback queries
Unsupervised recognition of interleaved activities of daily living through ontological and probabilistic reasoning
No full textRecognition of activities of daily living (ADLs) is an enabling technology for several ubiquitous computing applications. In this field, most activity recognition systems rely on supervised learning methods to extract activity models from labeled datasets. An inherent problem of that approach consists in the acquisition of comprehensive activity datasets, which is expensive and may violate individuals’ privacy. The problem is particularly challenging when focusing on complex ADLs, which are characterized by large intra- and inter-personal variability of execution. In this paper, we propose an unsupervised method to recognize complex ADLs exploiting the semantics of activities, context data, and sensing devices. Through ontological reasoning, we derive semantic correlations among activities and sensor events. By matching observed sensor events with semantic correlations, a statistical reasoner formulates initial hypotheses about the occurred activities. Those hypotheses are refined through probabilistic reasoning, exploiting semantic constraints derived from the ontology. Extensive experiments with real-world datasets show that the accuracy of our unsupervised method is comparable to the one of state of the art supervised approaches
Sensor-based human activity recognition: Overcoming issues in a real world setting
Full text linkThe rapid growing of the population age in industrialized societies calls for advanced tools to continuous monitor the activities of people. The goals of those tools are usually to support active and healthy ageing, and to early detect possible health issues to enable a long and independent life. Recent advancements in sensor miniaturization and wireless communications have paved the way to unobtrusive activity recognition systems. Hence, many pervasive health care systems have been proposed which monitor activities through unobtrusive sensors and by machine learning or artificial intelligence methods. Unfortunately, while those systems are effective in controlled environments, their actual effectiveness out of the lab is still limited due to different shortcomings of existing approaches.
In this work, we explore such systems and aim to overcome existing limitations and shortcomings. Focusing on physical movements and crucial activities, our goal is to develop robust activity recognition methods based on external and wearable sensors that generate high quality results in a real world setting. Under laboratory conditions, existing research already showed that wearable sensors are suitable to recognize physical activities while external sensors are promising for activities that are more complex. Consequently, we investigate problems that emerge when coming out of the lab. This includes the position handling of wearable devices, the need of large expensive labeled datasets, the requirement to recognize activities in almost real-time, the necessity to adapt deployed systems online to changes in behavior of the user, the variability of executing an activity, and to use data and models across people. As a result, we present feasible solutions for these problems and provide useful insights for implementing corresponding techniques. Further, we introduce approaches and novel methods for both external and wearable sensors where we also clarify limitations and capabilities of the respective sensor types. Thus, we investigate both types separately to clarify their contribution and application use in respect of recognizing different types of activities in a real world scenario.
Overall, our comprehensive experiments and discussions show on the one hand the feasibility of physical activity recognition but also recognizing complex activities in a real world scenario. Comparing our techniques and results with existing works and state-of-the-art techniques also provides evidence concerning the reliability and quality of the proposed techniques. On the other hand, we also identify promising research directions and highlight that combining external and wearable sensors seem to be the next step to go beyond activity recognition. In other words, our results and discussions also show that combining external and wearable sensors would compensate weaknesses of the individual sensors in respect of certain activity types and scenarios. Therefore, by addressing the outlined problems, we pave the way for a hybrid approach. Along with our presented solutions, we conclude our work with a high-level multi-tier activity recognition architecture showing that aspects like physical activity, (emotional) condition, used objects, and environmental features are critical for reliable recognizing complex activities
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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