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Proceedings ArticleDOI

Behavior Analysis through Routine Cluster Discovery in Ubiquitous Sensor Data

TL;DR: This work proposes a novel clustering technique for BA which can find hidden routines in ubiquitous data and also captures the pattern in the routines and efficiently works on high dimensional data for BA without performing any computationally expensive reduction operations.
Abstract: Behavioral analysis (BA) on ubiquitous sensor data is the task of finding the latent distribution of features for modeling user-specific characteristics. These characteristics, in turn, can be used for a number of tasks including resource management, power efficiency, and smart home applications. In recent years, the employment of topic models for BA has been found to successfully extract the dynamics of the sensed data. Topic modeling is popularly performed on text data for mining inherent topics. The task of finding the latent topics in textual data is done in an unsupervised manner. In this work we propose a novel clustering technique for BA which can find hidden routines in ubiquitous data and also captures the pattern in the routines. Our approach efficiently works on high dimensional data for BA without performing any computationally expensive reduction operations. We evaluate three different techniques namely LDA, the Non-negative Matrix Factorization (NMF) and the Probabilistic Latent Semantic Analysis (PLSA) for comparative study. We have analyzed the efficiency of the methods by using performance indices like perplexity and silhouette on three real-world ubiquitous sensor datasets namely, the Intel Lab Data, Kyoto Data, and MERL data. Through rigorous experiments, we achieve silhouette scores of 0.7049 over the Intel Lab dataset, 0.6547 over the Kyoto dataset and 0.8312 over the MERL dataset for clustering.
Citations
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Journal ArticleDOI
TL;DR: This survey provides a comprehensive summary of recent research on AI-based algorithms for intelligent sensing and presents a comparative analysis of algorithms, models, influential parameters, available datasets, applications and projects in the area of intelligent sensing.
Abstract: In recent years, intelligent sensing has gained significant attention because of its autonomous decision-making ability to solve complex problems. Today, smart sensors complement and enhance the capabilities of human beings and have been widely embraced in numerous application areas. Artificial intelligence (AI) has made astounding growth in domains of natural language processing, machine learning (ML), and computer vision. The methods based on AI enable a computer to learn and monitor activities by sensing the source of information in a real-time environment. The combination of these two technologies provides a promising solution in intelligent sensing. This survey provides a comprehensive summary of recent research on AI-based algorithms for intelligent sensing. This work also presents a comparative analysis of algorithms, models, influential parameters, available datasets, applications and projects in the area of intelligent sensing. Furthermore, we present a taxonomy of AI models along with the cutting edge approaches. Finally, we highlight challenges and open issues, followed by the future research directions pertaining to this exciting and fast-moving field.

5 citations

References
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Posted Content
TL;DR: Probabilistic Latent Semantic Analysis (PLSA) as mentioned in this paper is a statistical technique for the analysis of two-mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text and in related areas.
Abstract: Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two-mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent Semantic Analysis which stems from linear algebra and performs a Singular Value Decomposition of co-occurrence tables, the proposed method is based on a mixture decomposition derived from a latent class model. This results in a more principled approach which has a solid foundation in statistics. In order to avoid overfitting, we propose a widely applicable generalization of maximum likelihood model fitting by tempered EM. Our approach yields substantial and consistent improvements over Latent Semantic Analysis in a number of experiments.

2,233 citations

Journal ArticleDOI
TL;DR: The NMF package helps realize the potential of Nonnegative Matrix Factorization, especially in bioinformatics, providing easy access to methods that have already yielded new insights in many applications and facilitating the combination of these to produce new NMF strategies.
Abstract: Nonnegative Matrix Factorization (NMF) is an unsupervised learning technique that has been applied successfully in several fields, including signal processing, face recognition and text mining. Recent applications of NMF in bioinformatics have demonstrated its ability to extract meaningful information from high-dimensional data such as gene expression microarrays. Developments in NMF theory and applications have resulted in a variety of algorithms and methods. However, most NMF implementations have been on commercial platforms, while those that are freely available typically require programming skills. This limits their use by the wider research community. Our objective is to provide the bioinformatics community with an open-source, easy-to-use and unified interface to standard NMF algorithms, as well as with a simple framework to help implement and test new NMF methods. For that purpose, we have developed a package for the R/BioConductor platform. The package ports public code to R, and is structured to enable users to easily modify and/or add algorithms. It includes a number of published NMF algorithms and initialization methods and facilitates the combination of these to produce new NMF strategies. Commonly used benchmark data and visualization methods are provided to help in the comparison and interpretation of the results. The NMF package helps realize the potential of Nonnegative Matrix Factorization, especially in bioinformatics, providing easy access to methods that have already yielded new insights in many applications. Documentation, source code and sample data are available from CRAN.

1,054 citations


"Behavior Analysis through Routine C..." refers methods in this paper

  • ...Here A represents P (w|d), X represents P (w|t), and D represents P (t|d) which is similar to Matrix factorization as described in previous models, so LDA is also based on the same concept as the PLSA and NMF....

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  • ...From the above results, it can be seen that the NMF model is the best fit for IL data....

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  • ...As shown in Figure 1(a), for IL dataset optimal number of topics in the case of NMF Model is 80 with coherence score 0.7315....

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  • ...Perplexity and coherence are both scored for probabilistic models like LDA, PLSA, and NMF where the scores essentially represent what chances are there of discovering a new cluster....

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  • ...Using those, the best result is given by LDA which yields 20 routines in Kyoto, and NMF which yields 26 in IL, and 80 in MERL....

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Proceedings ArticleDOI
21 Sep 2008
TL;DR: Experimental results show the ability of the approach to model and recognize daily routines without user annotation to be able to be used in this work.
Abstract: In this work we propose a novel method to recognize daily routines as a probabilistic combination of activity patterns. The use of topic models enables the automatic discovery of such patterns in a user's daily routine. We report experimental results that show the ability of the approach to model and recognize daily routines without user annotation.

473 citations


"Behavior Analysis through Routine C..." refers methods in this paper

  • ...[7] proposed a method for modeling and discovering daily routines from on-body sensor data....

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Journal ArticleDOI
TL;DR: This paper introduces an automated approach to activity tracking that identifies frequent activities that naturally occur in an individual's routine and can then track the occurrence of regular activities to monitor functional health and to detect changes in anindividual's patterns and lifestyle.
Abstract: The machine learning and pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to monitor the functional health of smart home residents, we need to design technologies that recognize and track activities that people normally perform as part of their daily routines. Although approaches do exist for recognizing activities, the approaches are applied to activities that have been preselected and for which labeled training data are available. In contrast, we introduce an automated approach to activity tracking that identifies frequent activities that naturally occur in an individual's routine. With this capability, we can then track the occurrence of regular activities to monitor functional health and to detect changes in an individual's patterns and lifestyle. In this paper, we describe our activity mining and tracking approach, and validate our algorithms on data collected in physical smart environments.

468 citations


"Behavior Analysis through Routine C..." refers background in this paper

  • ...[9] combine sequence mining and clustering algorithm to identify activities in a home environment which...

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Journal ArticleDOI
TL;DR: The results indicate that activity recognition and assessment can be automated using machine learning algorithms and smart home technology and will be useful for automating remote health monitoring and interventions.
Abstract: Objectives: Pervasive computing technology can provide valuable health monitoring and assistance technology to help individuals live independent lives in their own homes. As a critical part of this technology, our objective is to design software algorithms that recognize and assess the consistency of activities of daily living that individuals perform in their own homes. Methods: We have designed algorithms that automatically learn Markov models for each class of activity. These models are used to recognize activities that are performed in a smart home and to identify errors and inconsistencies in the performed activity. Results: We validate our approach using data collected from 60 volunteers who performed a series of activities in our smart apartment testbed. The results indicate that the algorithms correctly label the activities and successfully assess the completeness and consistency of the performed task. Conclusions: Our results indicate that activity recognition and assessment can be automated using machine learning algorithms and smart home technology. These algorithms will be useful for automating remote health monitoring and interventions.

373 citations


"Behavior Analysis through Routine C..." refers methods in this paper

  • ...For a Smart home environment, we focused on the Kyoto dataset generated by the Centre for Advanced Studies in Adaptive System(CASAS)[11] at the Washington State Universitys School of Electrical Engineering and Computer Science Department....

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