74 research outputs found
Surveying human habit modeling and mining techniques in smart spaces
A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field
Activity recognition with weighted frequent patterns mining in smart environments
In the past decades, activity recognition has aroused a great interest for the research groups majoring in context-awareness computing and human behaviours monitoring. However, the correlations between the activities and their frequent patterns have never been directly addressed by traditional activity recognition techniques. As a result, activities that trigger the same set of sensors are difficult to differentiate, even though they present different patterns such as different frequencies of the sensor events. In this paper, we propose an efficient association rule mining technique to find the association rules between the activities and their frequent patterns, and build an activity classifier based on these association rules. We also address the classification of overlapped activities by incorporating the global and local weight of the patterns. The experiment results using publicly available dataset demonstrate that our method is able to achieve better performance than traditional recognition methods such as Decision Tree, Naive Bayesian and HMM. Comparison studies show that the proposed association rule mining method is efficient, and we can further improve the activity recognition accuracy by considering global and local weight of frequent patterns of activities
Multioccupant Activity Recognition in Pervasive Smart Home Environments
been the center of lot of research for many years now. The aim is to recognize the sequence of actions by a specific person using sensor readings. Most of the research has been devoted to activity recognition of single occupants in the environment. However, living environments are usually inhabited by more than one person and possibly with pets. Hence, human activity recognition in the context of multi-occupancy is more general, but also more challenging. The difficulty comes from mainly two aspects: resident identification, known as data association, and diversity of human activities. The present survey paper provides an overview of existing approaches and current practices for activity recognition in multi-occupant smart homes. It presents the latest developments and highlights the open issues in this field
GENERIC FRAMEWORKS FOR INTERACTIVE PERSONALIZED INTERESTING PATTERN DISCOVERY
The traditional frequent pattern mining algorithms generate an exponentially large number of patterns of which a substantial portion are not much significant for many data analysis endeavours. Due to this, the discovery of a small number of interesting patterns from the exponentially large number of frequent patterns according to a particular user\u27s interest is an important task. Existing works on patter
Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations
The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov
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Activity recognition in smart homes with self verification of assignments
Activity recognition in smart homes provides valuable benefits in the field of health and elderly care by remote monitoring of patients. In health care, capabilities of both performing the correct recognition and reducing the wrong assignments are of high importance. The novelty of the proposed activity recognition approach lies in being able to assign a category to the incoming activity, while measuring the confidence score of the assigned category that reduces the false positives in the assignments. Multiple sensors deployed at different locations of a smart home are used for activity observations. For multi-class activity classification, we propose a binary solution using support vector machines, which simplifies the problem to correct/incorrect assignments. We obtain the confidence score of each assignment by estimating the activity distribution within each class such that the assignments with low confidence are separated for further investigation by a human operator. The proposed approach is evaluated using a comprehensive performance evaluation metrics. Experimental results obtained from nine publicly available smart home datasets demonstrate a better performance of the proposed approach compared to the state of the art
Generierung menschlicher Verhaltensprofile mittels unüberwachter Methoden zur Bewertung des Gesundheitszustandes
In the context of ambient assisted living, implementation of human behavior profiling is expected to occur through pervasive computing.
As for information extraction from measured data, the typical way are supervised methods. However, due to the low adaptivity and high dependency on lab-setting, and the necessity of data labeling and model training, these types of methods are limited in human behavior profiling in real-life scenarios.
Therefore, simple and unobtrusive sensors are relied upon to obtain daily behavior information. In spite of the incomplete observation, these sensors are able to provide key information. Thus, unsupervised methods have to be designed based on this measurement. In contrast to supervised data analysis, unsupervised methods have inherent advantages: Firstly, data labeling and training are not necessary. Secondly, they are more adaptive, making them suitable for use by different individuals. Thirdly, unknown knowledge might be discovered.
In order to propose unsupervised methods for human behavior profiling that can be practically applied, the following research is conducted in this doctoral thesis: First, abstractions of events and patterns of in-home behavior scenario are defined. Second, the discovering algorithm is derived, whereby regularly occurring sensor events that can represent lifestyle patterns can be discovered. Third, with the lifestyle depicted, the change of human behavior is modeled to present the variance of lifestyle. Aiming to investigate the effectiveness of these methods, they are applied to the datasets obtained in GAL-NATARS study, which is carried out in the setting of real-life, and their effectiveness is evaluated through comparison with medical assessment results.Im Rahmen von Ambient Assisted Living sollen menschliche Verhaltensprofile durch den Pervasive Computing generiert werden. Zur Extraktion von Informationen aus Messdaten werden typischerweise überwachte Methoden verwendet. In Bezug sind diese Methoden wegen ihrer geringen Anpassungsfähigkeit, hohen Abhängigkeit von Laborumgebungen, der Notwendigkeit der Kennzeichnung und der Lernphase in realen Szenarien zur Generierung von menschliche Verhaltensprofile sehr eingeschränkt.
Daher sollten einfache und unauffällige Sensoren verwendet werden, um täglich Verhaltensinformationen zu erhalten. Trotz der unvollständigen Beobachtung sind diese Sensoren in der Lage, die wichtige Informationen zu liefern. Hierfür sind unüberwachte Methoden notwendig, die auf der Grundlage dieser Messungen ausgeführt werden. Im Gegensatz zur überwachten Datenanalyse, haben unüberwachte Methoden folgende Vorteile: Zum einen sind keine Kennzeichnung von Daten und keine Lernphase erforderlich. Zweitens sind sie anpassungsfähiger, so dass sie für die Verwendung bei verschiedenen Individuen geeignet sind. Drittens können siebisher unbekanntes Wissen entdecken.
Zur Entwicklung von praktisch anwendbaren unüberwachten Methoden für die Generierung menschlicher Verhaltensprofile, wird in dieser Doktorarbeit die folgende Forschung durchgeführt: Erstens, Definition von Abstraktionen für Ereignisse und Muster häuslichen Verhaltens. Zweitens wird ein Entdeckungsalgorithmus abgeleitet, der regelmäßig auftretende Sensorereignisse, die Lebensgewohnheiten darstellen können, entdecken kann. Drittens, wird mit den so gewonnenen Lebensgewohnheiten, die Änderung des menschlichen Verhaltens modelliert, um die Varianz des Lebensstils abzubilden. Mit dem Ziel, die Wirksamkeit dieser Methoden zu untersuchen, werden sie auf Datensätze aus dem Feld, gesammelt in der GAL-NATARS Studie durchgeführt wird, angewendet. Ihre Wirksamkeit wird durch den Vergleich mit den Ergebnissen der medizinischen Beurteilung bewertet
A geographic knowledge discovery approach to property valuation
This thesis involves an investigation of how knowledge discovery can be applied in the area Geographic Information Science. In particular, its application in the area of
property valuation in order to reveal how different spatial entities and their interactions affect the price of the properties is explored. This approach is entirely
data driven and does not require previous knowledge of the area applied.
To demonstrate this process, a prototype system has been designed and implemented. It employs association rule mining and associative classification algorithms to uncover any existing inter-relationships and perform the valuation. Various algorithms that perform the above tasks have been proposed in the literature. The algorithm developed in this work is based on the Apriori algorithm. It has been
however, extended with an implementation of a ‘Best Rule’ classification scheme based on the Classification Based on Associations (CBA) algorithm.
For the modelling of geographic relationships a graph-theoretic approach has been employed. Graphs have been widely used as modelling tools within the geography
domain, primarily for the investigation of network-type systems. In the current context, the graph reflects topological and metric relationships between the spatial
entities depicting general spatial arrangements. An efficient graph search algorithm has been developed, based on the Djikstra shortest path algorithm that enables the
investigation of relationships between spatial entities beyond first degree connectivity.
A case study with data from three central London boroughs has been performed to validate the methodology and algorithms, and demonstrate its effectiveness for computer aided property valuation. In addition, through the case study, the influence of location in the value of properties in those boroughs has been examined. The results are encouraging as they demonstrate the effectiveness of the proposed methodology and algorithms, provided that the data is appropriately pre processed and is of high quality
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