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\n  \n 2024\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Sequential Monte Carlo Bandits.\n \n \n \n \n\n\n \n Urteaga, I.; and Wiggins, C. H.\n\n\n \n\n\n\n Foundations of Data Science. 2024.\n \n\n\n\n
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@Article{j-Urteaga2024,\n  author  = {I{\\~n}igo Urteaga and Chris H. Wiggins},\n  journal = {Foundations of Data Science},\n  title   = {{Sequential Monte Carlo Bandits}},\n  year    = {2024},\n  doi     = {10.3934/fods.2024005},\n  url     = {https://www.aimsciences.org/article/id/65e052983cb6602db4904b02},\n}\n\n
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\n \n\n \n \n \n \n \n \n Nonparametric Gaussian mixture models for the multi-armed contextual bandit.\n \n \n \n \n\n\n \n Urteaga, I.; and Wiggins, C. H.\n\n\n \n\n\n\n Journal of Machine Learning Research. 2023.\n (Under Review)\n\n\n\n
\n\n\n\n \n \n \"NonparametricPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@Article{j-Urteaga2023b,\n  author        = {I{\\~n}igo Urteaga and Chris H. Wiggins},\n  journal       = {Journal of Machine Learning Research},\n  title         = {{Nonparametric Gaussian mixture models for the multi-armed contextual bandit}},\n  year          = {2023},\n  note          = {(Under Review)},\n  archiveprefix = {arXiv},\n  eprint        = {1808.02932},\n  keywords      = {Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Computation, I.2.6},\n  owner         = {iurteaga},\n  primaryclass  = {stat.ML},\n  timestamp     = {2017.11.01},\n  url           = {https://arxiv.org/abs/1808.02932},\n}\n\n
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\n \n\n \n \n \n \n \n User Engagement Metrics and Patterns in Phendo, an Endometriosis Research Mobile App.\n \n \n \n\n\n \n Urteaga, I.; Lipsky-Gorman, S.; McKillop, M.; and Elhadad, N.\n\n\n \n\n\n\n Nature Partner Journal Digital Medicine. 2022.\n (Under review, Minor revisions.)\n\n\n\n
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@Article{j-Urteaga2022a,\n  author  = {I{\\~n}igo Urteaga and Sharon Lipsky-Gorman and Mollie McKillop and Noémie Elhadad},\n  journal = {Nature Partner Journal Digital Medicine},\n  title   = {{User Engagement Metrics and Patterns in Phendo, an Endometriosis Research Mobile App}},\n  year    = {2022},\n  note    = {(Under review, Minor revisions.)},\n}\n\n
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\n  \n 2021\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n A predictive model for next cycle start date that accounts for adherence in menstrual self-tracking.\n \n \n \n \n\n\n \n Li, K.; Urteaga, I.; Shea, A.; Vitzthum, V. J.; Wiggins, C. H.; and Elhadad, N.\n\n\n \n\n\n\n Journal of the American Medical Informatics Association, 29(1): 3 – 11. 09 2021.\n \n\n\n\n
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@Article{j-Li2021,\n  author   = {Kathy Li and I{\\~n}igo Urteaga and Amanda Shea and Virginia J. Vitzthum and Chris H. Wiggins and Noémie Elhadad},\n  journal  = {Journal of the American Medical Informatics Association},\n  title    = {A predictive model for next cycle start date that accounts for adherence in menstrual self-tracking},\n  year     = {2021},\n  issn     = {1527-974X},\n  month    = {09},\n  number   = {1},\n  pages    = {3 -- 11},\n  volume   = {29},\n  abstract = {The study sought to build predictive models of next menstrual cycle start date based on mobile health self-tracked cycle data. Because app users may skip tracking, disentangling physiological patterns of menstruation from tracking behaviors is necessary for the development of predictive models.We use data from a popular menstrual tracker (186 000 menstruators with over 2 million tracked cycles) to learn a predictive model, which (1) accounts explicitly for self-tracking adherence; (2) updates predictions as a given cycle evolves, allowing for interpretable insight into how these predictions change over time; and (3) enables modeling of an individual's cycle length history while incorporating population-level information.Compared with 5 baselines (mean, median, convolutional neural network, recurrent neural network, and long short-term memory network), the model yields better predictions and consistently outperforms them as the cycle evolves. The model also provides predictions of skipped tracking probabilities.Mobile health apps such as menstrual trackers provide a rich source of self-tracked observations, but these data have questionable reliability, as they hinge on user adherence to the app. By taking a machine learning approach to modeling self-tracked cycle lengths, we can separate true cycle behavior from user adherence, allowing for more informed predictions and insights into the underlying observed data structure.Disentangling physiological patterns of menstruation from adherence allows for accurate and informative predictions of menstrual cycle start date and is necessary for mobile tracking apps. The proposed predictive model can support app users in being more aware of their self-tracking behavior and in better understanding their cycle dynamics.},\n  doi      = {10.1093/jamia/ocab182},\n  url      = {https://doi.org/10.1093/jamia/ocab182},\n}\n\n
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\n The study sought to build predictive models of next menstrual cycle start date based on mobile health self-tracked cycle data. Because app users may skip tracking, disentangling physiological patterns of menstruation from tracking behaviors is necessary for the development of predictive models.We use data from a popular menstrual tracker (186 000 menstruators with over 2 million tracked cycles) to learn a predictive model, which (1) accounts explicitly for self-tracking adherence; (2) updates predictions as a given cycle evolves, allowing for interpretable insight into how these predictions change over time; and (3) enables modeling of an individual's cycle length history while incorporating population-level information.Compared with 5 baselines (mean, median, convolutional neural network, recurrent neural network, and long short-term memory network), the model yields better predictions and consistently outperforms them as the cycle evolves. The model also provides predictions of skipped tracking probabilities.Mobile health apps such as menstrual trackers provide a rich source of self-tracked observations, but these data have questionable reliability, as they hinge on user adherence to the app. By taking a machine learning approach to modeling self-tracked cycle lengths, we can separate true cycle behavior from user adherence, allowing for more informed predictions and insights into the underlying observed data structure.Disentangling physiological patterns of menstruation from adherence allows for accurate and informative predictions of menstrual cycle start date and is necessary for mobile tracking apps. The proposed predictive model can support app users in being more aware of their self-tracking behavior and in better understanding their cycle dynamics.\n
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\n  \n 2020\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile health data.\n \n \n \n\n\n \n Li, K.; Urteaga, I.; H. Wiggins, C.; Druet, A.; Shea, A.; Vitzthum, V. J.; and Elhadad, N.\n\n\n \n\n\n\n Nature Partner Journal Digital Medicine, 3(79). 2020.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{j-Li2020,\n  author  = {Kathy Li and I{\\~n}igo Urteaga and Chris H.~Wiggins and Anna Druet and Amanda Shea and Virginia J. Vitzthum and No\\'{e}mie Elhadad},\n  title   = {{Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile health data}},\n  journal = {Nature Partner Journal Digital Medicine},\n  year    = {2020},\n  volume  = {3},\n  number  = {79},\n  doi     = {10.1038/s41746-020-0269-8},\n}\n\n
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\n \n\n \n \n \n \n \n Learning endometriosis phenotypes from patient-generated data.\n \n \n \n\n\n \n Urteaga, I.; McKillop, M.; and Elhadad, N.\n\n\n \n\n\n\n Nature Partner Journal Digital Medicine, 3(88). 2020.\n \n\n\n\n
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@Article{j-Urteaga2020a,\n  author  = {I{\\~n}igo Urteaga and Mollie McKillop and No\\'{e}mie Elhadad},\n  title   = {{Learning endometriosis phenotypes from patient-generated data}},\n  journal = {Nature Partner Journal Digital Medicine},\n  year    = {2020},\n  volume  = {3},\n  number  = {88},\n  doi     = {10.1038/s41746-020-0292-9},\n}\n\n
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\n  \n 2018\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n (Sequential) Importance Sampling Bandits.\n \n \n \n\n\n \n Urteaga, I.; and Wiggins, C. H.\n\n\n \n\n\n\n arXiv e-print:1808.02933. August 2018.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@Article{j-Urteaga2018,\n  author        = {I{\\~n}igo Urteaga and Chris H. Wiggins},\n  journal       = {arXiv e-print:1808.02933},\n  title         = {{(Sequential) Importance Sampling Bandits}},\n  year          = {2018},\n  month         = aug,\n  abstract      = {This work extends existing multi-armed bandit (MAB) algorithms beyond their original settings by leveraging advances in sequential Monte Carlo (SMC) methods from the approximate inference community. We leverage Monte Carlo estimation and, in particular, the flexibility of (sequential) importance sampling to allow for accurate estimation of the statistics of interest within the MAB problem. The MAB is a sequential allocation task where the goal is to learn a policy that maximizes long term payoff, where only the reward of the executed action is observed; i.e., sequential optimal decisions are made, while simultaneously learning how the world operates. In the stochastic setting, the reward for each action is generated from an unknown distribution. To decide the next optimal action to take, one must compute sufficient statistics of this unknown reward distribution, e.g., upper-confidence bounds (UCB), or expectations in Thompson sampling. Closed-form expressions for these statistics of interest are analytically intractable except for simple cases. By combining SMC methods --- which estimate posterior densities and expectations in probabilistic models that are analytically intractable --- with Bayesian state-of-the-art MAB algorithms, we extend their applicability to complex models: those for which sampling may be performed even if analytic computation of summary statistics is infeasible --- nonlinear reward functions and dynamic bandits. We combine SMC both for Thompson sampling and upper confident bound-based (Bayes-UCB) policies, and study different bandit models: classic Bernoulli and Gaussian distributed cases, as well as dynamic and context dependent linear-Gaussian, logistic and categorical-softmax rewards.},\n  archiveprefix = {arXiv},\n  eprint        = {1808.02933},\n  file          = {:http\\://arxiv.org/pdf/1808.02933v3:PDF},\n  keywords      = {stat.ML, cs.LG, stat.CO, I.2.6},\n  primaryclass  = {stat.ML},\n}\n\n
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\n This work extends existing multi-armed bandit (MAB) algorithms beyond their original settings by leveraging advances in sequential Monte Carlo (SMC) methods from the approximate inference community. We leverage Monte Carlo estimation and, in particular, the flexibility of (sequential) importance sampling to allow for accurate estimation of the statistics of interest within the MAB problem. The MAB is a sequential allocation task where the goal is to learn a policy that maximizes long term payoff, where only the reward of the executed action is observed; i.e., sequential optimal decisions are made, while simultaneously learning how the world operates. In the stochastic setting, the reward for each action is generated from an unknown distribution. To decide the next optimal action to take, one must compute sufficient statistics of this unknown reward distribution, e.g., upper-confidence bounds (UCB), or expectations in Thompson sampling. Closed-form expressions for these statistics of interest are analytically intractable except for simple cases. By combining SMC methods — which estimate posterior densities and expectations in probabilistic models that are analytically intractable — with Bayesian state-of-the-art MAB algorithms, we extend their applicability to complex models: those for which sampling may be performed even if analytic computation of summary statistics is infeasible — nonlinear reward functions and dynamic bandits. We combine SMC both for Thompson sampling and upper confident bound-based (Bayes-UCB) policies, and study different bandit models: classic Bernoulli and Gaussian distributed cases, as well as dynamic and context dependent linear-Gaussian, logistic and categorical-softmax rewards.\n
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\n  \n 2017\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n Sequential Monte Carlo for inference of latent ARMA time-series with innovations correlated in time.\n \n \n \n \n\n\n \n Urteaga, I.; Bugallo, M. F.; and Djurić, P. M.\n\n\n \n\n\n\n EURASIP Journal on Advances in Signal Processing, 2017(1). Dec 2017.\n \n\n\n\n
\n\n\n\n \n \n \"SequentialPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{j-Urteaga2017,\n  Title                    = {{Sequential Monte Carlo for inference of latent ARMA time-series with innovations correlated in time}},\n  Author                   = {I{\\~n}igo Urteaga and M\\'{o}nica F. Bugallo and Petar M. Djuri\\'{c}},\n  Journal                  = {EURASIP Journal on Advances in Signal Processing},\n  Year                     = {2017},\n\n  Month                    = {Dec},\n  Number                   = {1},\n  Volume                   = {2017},\n\n  Doi                      = {10.1186/s13634-017-0518-4},\n  Owner                    = {iurteaga},\n  Timestamp                = {2016-10-25},\n  Url                      = {https://doi.org/10.1186/s13634-017-0518-4}\n}\n\n
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\n \n\n \n \n \n \n \n Bayesian bandits: balancing the exploration-exploitation tradeoff via double sampling.\n \n \n \n\n\n \n Urteaga, I.; and Wiggins, C. H.\n\n\n \n\n\n\n arXiv eprint:1709.03162. September 2017.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@Article{j-Urteaga2017a,\n  author        = {I{\\~n}igo Urteaga and Chris H. Wiggins},\n  journal       = {arXiv eprint:1709.03162},\n  title         = {{Bayesian bandits: balancing the exploration-exploitation tradeoff via double sampling}},\n  year          = {2017},\n  month         = sep,\n  abstract      = {Reinforcement learning studies how to balance exploration and exploitation in real-world systems, optimizing interactions with the world while simultaneously learning how the world operates. One general class of algorithms for such learning is the multi-armed bandit setting. Randomized probability matching, based upon the Thompson sampling approach introduced in the 1930s, has recently been shown to perform well and to enjoy provable optimality properties. It permits generative, interpretable modeling in a Bayesian setting, where prior knowledge is incorporated, and the computed posteriors naturally capture the full state of knowledge. In this work, we harness the information contained in the Bayesian posterior and estimate its sufficient statistics via sampling. In several application domains, for example in health and medicine, each interaction with the world can be expensive and invasive, whereas drawing samples from the model is relatively inexpensive. Exploiting this viewpoint, we develop a double sampling technique driven by the uncertainty in the learning process: it favors exploitation when certain about the properties of each arm, exploring otherwise. The proposed algorithm does not make any distributional assumption and it is applicable to complex reward distributions, as long as Bayesian posterior updates are computable. Utilizing the estimated posterior sufficient statistics, double sampling autonomously balances the exploration-exploitation tradeoff to make better informed decisions. We empirically show its reduced cumulative regret when compared to state-of-the-art alternatives in representative bandit settings.},\n  archiveprefix = {arXiv},\n  eprint        = {1709.03162},\n  file          = {:http\\://arxiv.org/pdf/1709.03162v2:PDF},\n  keywords      = {stat.ML, cs.LG, stat.CO, I.2.6},\n  primaryclass  = {stat.ML},\n}\n\n
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\n Reinforcement learning studies how to balance exploration and exploitation in real-world systems, optimizing interactions with the world while simultaneously learning how the world operates. One general class of algorithms for such learning is the multi-armed bandit setting. Randomized probability matching, based upon the Thompson sampling approach introduced in the 1930s, has recently been shown to perform well and to enjoy provable optimality properties. It permits generative, interpretable modeling in a Bayesian setting, where prior knowledge is incorporated, and the computed posteriors naturally capture the full state of knowledge. In this work, we harness the information contained in the Bayesian posterior and estimate its sufficient statistics via sampling. In several application domains, for example in health and medicine, each interaction with the world can be expensive and invasive, whereas drawing samples from the model is relatively inexpensive. Exploiting this viewpoint, we develop a double sampling technique driven by the uncertainty in the learning process: it favors exploitation when certain about the properties of each arm, exploring otherwise. The proposed algorithm does not make any distributional assumption and it is applicable to complex reward distributions, as long as Bayesian posterior updates are computable. Utilizing the estimated posterior sufficient statistics, double sampling autonomously balances the exploration-exploitation tradeoff to make better informed decisions. We empirically show its reduced cumulative regret when compared to state-of-the-art alternatives in representative bandit settings.\n
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\n \n\n \n \n \n \n \n Sequential Estimation of Hidden ARMA Processes by Particle Filtering - Part I.\n \n \n \n\n\n \n Urteaga, I.; and Djurić, P. M.\n\n\n \n\n\n\n IEEE Transactions on Signal Processing, 65(2): 482–493. 2016.\n \n\n\n\n
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@Article{j-Urteaga2016,\n  author    = {I{\\~n}igo Urteaga and Petar M. Djuri\\'{c}},\n  title     = {{Sequential Estimation of Hidden {ARMA} Processes by Particle Filtering - {P}art {I}}},\n  journal   = {IEEE Transactions on Signal Processing},\n  year      = {2016},\n  volume    = {65},\n  number    = {2},\n  pages     = {482--493},\n  issn      = {1053-587X},\n  abstract  = {This paper is Part I of a series of two papers where we address sequential estimation of wide-sense stationary autoregressive moving average (ARMA) state processes by particle filtering. In Part I, we present estimation methods for ARMA processes of known model order, where the parameters are first known and then unknown. The driving noise of the ARMA process is Gaussian with unknown variance. We derive the transition density of the ARMA state for settings that correspond to different assumptions of a priori knowledge. Instead of estimating all the unknown parameters of the model, we treat them by Rao-Blackwellization. We propose a particle filtering method, with appropriate variations according to available information, for sequential estimation of the unknown state as it evolves with time. We demonstrate the performance of the proposed methods by extensive computer simulations.},\n  doi       = {10.1109/TSP.2016.2598309},\n  keywords  = {Atmospheric measurements;Autoregressive processes;Computational modeling;Estimation;Geophysical measurements;Particle measurements;Signal processing;ARMA processes;Rao-Blackwellization;known model order;nonlinear models;particle filtering},\n  owner     = {iurteaga},\n  timestamp = {2015-10-05},\n}\n\n
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\n This paper is Part I of a series of two papers where we address sequential estimation of wide-sense stationary autoregressive moving average (ARMA) state processes by particle filtering. In Part I, we present estimation methods for ARMA processes of known model order, where the parameters are first known and then unknown. The driving noise of the ARMA process is Gaussian with unknown variance. We derive the transition density of the ARMA state for settings that correspond to different assumptions of a priori knowledge. Instead of estimating all the unknown parameters of the model, we treat them by Rao-Blackwellization. We propose a particle filtering method, with appropriate variations according to available information, for sequential estimation of the unknown state as it evolves with time. We demonstrate the performance of the proposed methods by extensive computer simulations.\n
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\n \n\n \n \n \n \n \n Sequential Estimation of Hidden ARMA Processes by Particle Filtering - Part II.\n \n \n \n\n\n \n Urteaga, I.; and Djurić, P. M.\n\n\n \n\n\n\n IEEE Transactions on Signal Processing, 65(2): 494–504.. 2016.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@Article{j-Urteaga2016a,\n  author    = {I{\\~n}igo Urteaga and Petar M. Djuri\\'{c}},\n  title     = {{Sequential Estimation of Hidden {ARMA} Processes by Particle Filtering - {P}art {II}}},\n  journal   = {IEEE Transactions on Signal Processing},\n  year      = {2016},\n  volume    = {65},\n  number    = {2},\n  pages     = {494--504.},\n  issn      = {1053-587X},\n  abstract  = {This is Part II of a series of two papers where we address sequential estimation of wide-sense stationary autoregressive moving average (ARMA) state processes by particle filtering. In Part I, we considered a state-space model where the state was an ARMA process of known order and where the parameters of the process could be known or unknown. In this paper, we extend our work from Part I by considering the same type of models, with the added complexity that the ARMA processes are now of unknown order. Instead of working on a scheme that first tracks the state by operating with different assumed models, and then selects the best model by using a predefined criterion, we present a method that directly estimates the state without the need of knowing the model order.We derive the transition density of the state for unknown ARMA model order, and propose a particle filter based on that density and the empirical Bayesian methodology. We demonstrate the performance of the proposed method with computer simulations and compare it with the methods from Part I.},\n  doi       = {10.1109/TSP.2016.2598324},\n  keywords  = {Autoregressive processes;Bayes methods;Computational modeling;Covariance matrices;Estimation;Signal processing;State-space methods;ARMA processes;Rao-Blackwellization;empirical Bayes;nonlinear models;particle filtering;unknown model order},\n  owner     = {iurteaga},\n  timestamp = {2015-10-05},\n}\n\n
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\n This is Part II of a series of two papers where we address sequential estimation of wide-sense stationary autoregressive moving average (ARMA) state processes by particle filtering. In Part I, we considered a state-space model where the state was an ARMA process of known order and where the parameters of the process could be known or unknown. In this paper, we extend our work from Part I by considering the same type of models, with the added complexity that the ARMA processes are now of unknown order. Instead of working on a scheme that first tracks the state by operating with different assumed models, and then selects the best model by using a predefined criterion, we present a method that directly estimates the state without the need of knowing the model order.We derive the transition density of the state for unknown ARMA model order, and propose a particle filter based on that density and the empirical Bayesian methodology. We demonstrate the performance of the proposed method with computer simulations and compare it with the methods from Part I.\n
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\n  \n 2015\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n Reliability of Bluetooth-based connectivity traces for the characterization of human interaction.\n \n \n \n\n\n \n María Cabero, J.; Urteaga, I.; Molina, V.; Liberal, F.; and Martín, J. L.\n\n\n \n\n\n\n Ad Hoc Networks, 24, Part A(0): 135 - 146. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@Article{j-Cabero2015,\n  Title                    = {{Reliability of Bluetooth-based connectivity traces for the characterization of human interaction}},\n  Author                   = {Jos{\\'e} Mar{\\'i}a Cabero and I{\\~n}igo Urteaga and Virginia Molina and Fidel Liberal and Jos{\\'e} Luis Mart{\\'i}n},\n  Journal                  = {Ad Hoc Networks},\n  Year                     = {2015},\n  Number                   = {0},\n  Pages                    = {135 - 146},\n  Volume                   = {24, Part A},\n\n  Abstract                 = {Abstract The characterization of human interaction at different levels has been a matter of interest in many disciplines. So far, social networking through the Internet has been the main source to infer human beings’ relations. Nevertheless, due to the irruption of wearable devices with wireless communication capabilities, initiatives that use them to measure physical proximity are increasingly popular. Built-in wireless communication technologies allow these devices to detect each other and to infer their owners’ interaction, based on their proximity measurements. This approach, which is followed by most proximity initiatives in the research community, poses three main challenges that usually limit the quality of collected data and consequently, the reliability of human behavior characterization: the person-device uncertainty, the sample period and the bias caused by the particularities of the underlying wireless technology. The work presented here analyzes empirically the impact of these three limitations when Bluetooth is the communication technology used to detect proximity. It also presents the expansion of the results when additional mechanisms to counteract the impediments are applied, and it states their necessity for the reliability of the results. They show relevant differences with previous initiatives that open a discussion on the proper use of wireless wearable devices as a tool for the characterization of human interactions.},\n  Doi                      = {http://dx.doi.org/10.1016/j.adhoc.2014.08.010},\n  ISSN                     = {1570-8705},\n  Keywords                 = {Bluetooth; Connectivity Traces; Human interaction; Contact times; Inter-contact times; Real database},\n  Owner                    = {iurteaga},\n  Timestamp                = {2014-02-10}\n}\n\n
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\n Abstract The characterization of human interaction at different levels has been a matter of interest in many disciplines. So far, social networking through the Internet has been the main source to infer human beings’ relations. Nevertheless, due to the irruption of wearable devices with wireless communication capabilities, initiatives that use them to measure physical proximity are increasingly popular. Built-in wireless communication technologies allow these devices to detect each other and to infer their owners’ interaction, based on their proximity measurements. This approach, which is followed by most proximity initiatives in the research community, poses three main challenges that usually limit the quality of collected data and consequently, the reliability of human behavior characterization: the person-device uncertainty, the sample period and the bias caused by the particularities of the underlying wireless technology. The work presented here analyzes empirically the impact of these three limitations when Bluetooth is the communication technology used to detect proximity. It also presents the expansion of the results when additional mechanisms to counteract the impediments are applied, and it states their necessity for the reliability of the results. They show relevant differences with previous initiatives that open a discussion on the proper use of wireless wearable devices as a tool for the characterization of human interactions.\n
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\n  \n 2014\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n Acquisition of human traces with Bluetooth technology: Challenges and proposals.\n \n \n \n\n\n \n María Cabero, J.; Molina, V.; Urteaga, I.; Liberal, F.; and Martín, J. L.\n\n\n \n\n\n\n Ad Hoc Networks, 12(0): 2-16. 2014.\n \n\n\n\n
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@Article{j-Cabero2014,\n  Title                    = {{Acquisition of human traces with Bluetooth technology: Challenges and proposals}},\n  Author                   = {Jos{\\'e} Mar{\\'i}a Cabero and Virginia Molina and I{\\~n}igo Urteaga and Fidel Liberal and Jos{\\'e} Luis Mart{\\'i}n},\n  Journal                  = {Ad Hoc Networks},\n  Year                     = {2014},\n  Number                   = {0},\n  Pages                    = {2-16},\n  Volume                   = {12},\n\n  Abstract                 = {This paper highlights the challenges to be taken into consideration when Bluetooth is used as a radio technology to capture proximity traces between people. Our study analyzes the limitations of Bluetooth-based trace acquisition initiatives carried out until now in terms of granularity and reliability. We then propose an optimal configuration for the acquisition of proximity traces and movement information using a fine-tuned Bluetooth system based on custom hardware. With this system and based on such a configuration, we have carried out an intensive human trace acquisition experiment resulting in a proximity and mobility database of more than 5 million traces with a minimum granularity of 5 s.},\n  Doi                      = {http://dx.doi.org/10.1016/j.adhoc.2012.05.007},\n  ISSN                     = {1570-8705},\n  Keywords                 = {Trace acquisition system, Proximity traces, Movement traces, Bluetooth, Real experiment, Real database},\n  Owner                    = {iurteaga},\n  Timestamp                = {2013-09-30}\n}\n\n
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\n This paper highlights the challenges to be taken into consideration when Bluetooth is used as a radio technology to capture proximity traces between people. Our study analyzes the limitations of Bluetooth-based trace acquisition initiatives carried out until now in terms of granularity and reliability. We then propose an optimal configuration for the acquisition of proximity traces and movement information using a fine-tuned Bluetooth system based on custom hardware. With this system and based on such a configuration, we have carried out an intensive human trace acquisition experiment resulting in a proximity and mobility database of more than 5 million traces with a minimum granularity of 5 s.\n
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\n \n\n \n \n \n \n \n AWARE: Activity aware maintenance of communication structures for wireless sensor networks.\n \n \n \n\n\n \n Urteaga, I.; Yu, N.; Hubbell, N.; and Han, Q.\n\n\n \n\n\n\n Pervasive and Mobile Computing, 13: 111–124. 2014.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@Article{j-Urteaga2014,\n  author    = {I{\\~n}igo Urteaga and Na Yu and Nicholas Hubbell and Qi Han},\n  title     = {{AWARE: Activity aware maintenance of communication structures for wireless sensor networks}},\n  journal   = {Pervasive and Mobile Computing},\n  year      = {2014},\n  volume    = {13},\n  pages     = {111--124},\n  doi       = {http://dx.doi.org/10.1016/j.pmcj.2013.10.011},\n  keywords  = {Wireless sensor network, Activity-driven, Network clustering; Energy efficiency},\n  owner     = {iurteaga},\n  timestamp = {2014-02-10},\n}\n\n
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\n  \n 2011\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n .\n \n \n \n\n\n \n Unanue, I.; Urteaga, I.; Husemann, R.; Ser, J. D.; Roesler, V.; Rodriguez, A.; and Sanchez, P.\n\n\n \n\n\n\n of Recent Advances on Video Coding. A Tutorial on H.264/SVC Scalable Video Coding and its Tradeoff between Quality, Coding Efficiency and Performance, pages 9 – 15.. Lorente, J. D. S., editor(s). InTech, 2011.\n \n\n\n\n
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@InBook{ib-Unanue2011,\n  pages     = {9 -- 15.},\n  title     = {{A Tutorial on H.264/SVC Scalable Video Coding and its Tradeoff between Quality, Coding Efficiency and Performance}},\n  publisher = {InTech},\n  year      = {2011},\n  author    = {Iraide Unanue and I{\\~n}igo Urteaga and Ronaldo Husemann and Javier Del Ser and Valter Roesler and Aitor Rodriguez and Pedro Sanchez},\n  editor    = {Javier Del Ser Lorente},\n  series    = {{Recent Advances on Video Coding}},\n  doi       = {http://dx.doi.org/10.5772/19227},\n  owner     = {iurteaga},\n  timestamp = {2014-02-11},\n}\n\n
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\n  \n 2010\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n On Integrating Groundwater Transport Models with Wireless Sensor Networks.\n \n \n \n\n\n \n Barnhart, K.; Urteaga, I.; Han, Q.; P.Jayasumana, A.; and Illangasekare, T.\n\n\n \n\n\n\n Journal of Ground Water, 48(5). October 2010.\n \n\n\n\n
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@Article{j-Barnhart2010,\n  Title                    = {{On Integrating Groundwater Transport Models with Wireless Sensor Networks}},\n  Author                   = {Kevin Barnhart and I{\\~n}igo Urteaga and Qi Han and Anura P.Jayasumana and Tissa Illangasekare},\n  Journal                  = {Journal of Ground Water},\n  Year                     = {2010},\n\n  Month                    = {October},\n  Number                   = {5},\n  Volume                   = {48},\n\n  Doi                      = {http://dx.doi.org/10.1111/j.1745-6584.2010.00684.x},\n  Owner                    = {iurteaga},\n  Timestamp                = {2014-02-11}\n}\n\n
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\n  \n 2009\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n REDFLAG: A REal-time, Distributed, Flexible, Lightweight, And Generous Fault Detection Service for Data-driven Sensor Applications.\n \n \n \n\n\n \n Urteaga, I.; Barnhart, K.; and Han, Q.\n\n\n \n\n\n\n Pervasive and Mobile Computing (PMC) Journal, 5(5). October 2009.\n \n\n\n\n
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@Article{j-Urteaga2009,\n  Title                    = {{REDFLAG: A REal-time, Distributed, Flexible, Lightweight, And Generous Fault Detection Service for Data-driven Sensor Applications}},\n  Author                   = {I{\\~n}igo Urteaga and Kevin Barnhart and Qi Han},\n  Journal                  = {Pervasive and Mobile Computing (PMC) Journal},\n  Year                     = {2009},\n\n  Month                    = {October},\n  Number                   = {5},\n  Volume                   = {5},\n\n  Doi                      = {http://dx.doi.org/10.1016/j.pmcj.2009.08.001},\n  Owner                    = {iurteaga},\n  Timestamp                = {2014-02-11}\n}\n\n
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