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    AuthorTitleYearJournal/ProceedingsReftypeDOI/URL
    Crandall, A.S. Survive Catastrophic Internet Loss (Hack 45) 2004 BSD Hacks  incollection  
    BibTeX:
    @incollection{Crandall2004,
      author = {Crandall, Aaron S.},
      title = {Survive Catastrophic Internet Loss (Hack 45)},
      booktitle = {BSD Hacks},
      publisher = {O'Reilly Media},
      year = {2004}
    }
    
    Jakkula, V.R., Crandall, A.S. & Cook, D.J. Knowledge Discovery in Entity Based Smart Environment Resident Data Using Temporal Relation Based Data Mining 2007 IEEE Workshop on Temporal-Spatial Data MiningProceedings of the IEEE International Conference on Data Mining Workshops  inproceedings DOI  
    Abstract: Time is an important aspect of all real world phenomena. In this paper, we present a temporal relations-based framework for discovering interesting patterns in smart environment datasets, and test this framework in the context of the CASAS smart environments project. Our use of temporal relations in the context of smart environment tasks is described and our methodology for mining such relations from raw sensor data is introduced. We demonstrate how the results are enhanced by identifying the number of individuals in an environment, and apply the resulting technologies to look for interesting patterns which play a vital role to predict activities and identify anomalies in a physical smart environment.
    BibTeX:
    @inproceedings{Jakkula2007a,
      author = {Jakkula, Vikramaditya R. and Crandall, Aaron S. and Cook, Diane J.},
      title = {Knowledge Discovery in Entity Based Smart Environment Resident Data Using Temporal Relation Based Data Mining},
      booktitle = {Proceedings of the IEEE International Conference on Data Mining Workshops},
      journal = {IEEE Workshop on Temporal-Spatial Data Mining},
      publisher = {IEEE Computer Society},
      year = {2007},
      doi = {http://dx.doi.org/10.1109/ICDMW.2007.60}
    }
    
    Jakkula, V., Cook, D.J. & Crandall, A.S. Temporal pattern discovery for anomaly detection in a smart home 2007 The IET International Conference on Intelligent Environments, pp. 339-345  inproceedings  
    Abstract: The temporal nature of data collected in a smart environment provides us with a better understanding of patterns over time. Detecting anomalies in such datasets is a complex and challenging task. To solve this problem, we suggest a solution using temporal relations. Temporal pattern discovery based on modified Allen's temporal relations [5] has helped discover interesting patterns and relations on smart home datasets [10]. This paper describes a method of discovering temporal relations in smart home datasets and applying them to perform anomaly detection process on the frequently-occurring events. We also include experimental results, performed on real and synthetic datasets.
    BibTeX:
    @inproceedings{Jakkula2007d,
      author = {Jakkula, Vikramaditya and Cook, Diane J. and Crandall, Aaron S.},
      title = {Temporal pattern discovery for anomaly detection in a smart home},
      booktitle = {The IET International Conference on Intelligent Environments},
      year = {2007},
      pages = {339--345}
    }
    
    Crandall, A.S., Cook, D.J., Kusznir, J. & Thomas, B. CASAS Project: A Comprehensive Smart Home Research Testbed 2008 Washington State University Academic Showcase  conference  
    Abstract: The concept of a “Smart Home” is any kind of living space
    with sensors and controllers run by a computer. At WSU,
    our CASAS testbed has two operational spaces, with one
    more being constructed, that are true and comprehensive
    smart homes. These spaces are designed to use simple
    sensors to detect activity and behavior within the space,
    with the goal of controlling devices that surround the
    inhabitants.
    The events sensed are interpreted by artificial intelligence
    techniques towards building better models for medical
    monitoring, energy efficiency, security and general comfort.
    Our research is targeted towards improving the
    capabilities of smart homes with these areas in mind.
    BibTeX:
    @conference{Crandall2008,
      author = {Crandall, Aaron S. and Cook, Diane J. and Kusznir, Jim and Thomas, Brian},
      title = {CASAS Project: A Comprehensive Smart Home Research Testbed},
      booktitle = {Washington State University Academic Showcase},
      year = {2008}
    }
    
    Crandall, A.S. & Cook, D.J. Smart Home Resident Detection and Identification Using Simple Sensors 2008 Washington State University Academic Showcase  conference  
    Abstract: The concept of a “Smart Home” is any kind of living space with
    sensors and controllers run by a computer. At WSU our CASAS
    testbed has two running spaces with one more being constructed
    that are true and comprehensive smart homes. These spaces
    are designed to use simple sensors to detect activity and
    behavior within the space with the goal of controlling devices that
    surround the inhabitants.
    The events sensed are interpreted by artificial intelligence
    techniques towards building better models for medical monitoring,
    energy efficiency, security and general comfort. Our research is
    targeted towards improving the capabilities of smart homes with
    these areas in mind.
    BibTeX:
    @conference{Crandall2008a,
      author = {Crandall, Aaron S. and Cook, Diane J.},
      title = {Smart Home Resident Detection and Identification Using Simple Sensors},
      booktitle = {Washington State University Academic Showcase},
      year = {2008}
    }
    
    Crandall, A.S. & Cook, D.J. Resident and Caregiver: Handling Multiple People in a Smart Care Facility 2008 AI in Eldercare: New Solutions to Old Problems, pp. 39-47  inproceedings  
    Abstract: Intelligent environment research has benefited medical care in a number of ways, including emergency detection, comfort and accessibility. However, most of these techniques have been applied in the context of a single resident, leaving out situations where there is more than one person in the living space. A current looming issue for intelligent environment systems is performing these same techniques when multiple residents or care providers are present in the environment. In this paper we investigate the problem of attributing sensor events to individuals in a multi-resident intelligent environment. Specifically, explore and contrast using two different classification techniques. The naive Bayesian and Markov Model classifiers present different capabilities and features for identifying the resident responsible for a unique sensor event. We present results of experimental validation in an intelligent workplace testbed and discuss the unique issues that arise in addressing this challenging problem.
    BibTeX:
    @inproceedings{Crandall2008b,
      author = {Crandall, Aaron S. and Cook, Diane J.},
      title = {Resident and Caregiver: Handling Multiple People in a Smart Care Facility},
      booktitle = {AI in Eldercare: New Solutions to Old Problems},
      publisher = {AAAI Press},
      year = {2008},
      pages = {39-47}
    }
    
    Crandall, A.S. & Cook, D.J. Attributing Events to Individuals in Multi-Inhabitant Environments 2008 IET International Conference on Intelligent Environments, pp. 1-8  inproceedings  
    Abstract: Intelligent environment research has resulted in many useful tools such as activity recognition, prediction, and automation. However, most of these techniques have been applied in the context of a single resident. A current looming issue for intelligent environment systems is performing these same techniques when multiple residents are present in the environment. In this paper we investigate the problem of attributing sensor events to individuals in a multi-resident intelligent environment. Specifically, we use a naive Bayesian classifier to identify the resident responsible for a unique sensor event. We present results of experimental validation in a real intelligent workplace testbed and discuss the unique issues that arise in addressing this challenging problem.
    BibTeX:
    @inproceedings{Crandall2008c,
      author = {Crandall, Aaron S. and Cook, Diane J.},
      title = {Attributing Events to Individuals in Multi-Inhabitant Environments},
      booktitle = {IET International Conference on Intelligent Environments},
      publisher = {IOS Press},
      year = {2008},
      pages = {1--8}
    }
    
    Cook, D., Schmitter-Edgecombe, M., Crandall, A., Sanders, C. & Thomas, B. Collecting and disseminating smart home sensor data in the CASAS project, 2009 CHI Workshop on Developing Shared Home Behavior Datasets to Advance HCI and Ubiquitous Computing Research  inproceedings  
    Abstract: Although smart environment technology has rapidly been maturing, the performance of these
    technologies is still difficult to assess because of the limited evaluation that has been conducted. A
    primary limitation in evaluating technologies is the lack of rich physical datasets on which the
    algorithms can be tested. In this position paper we describe a publicly-available dataset that was
    created as part of the CASAS project and discuss challenges that are faced when creating and
    disseminating such data.
    BibTeX:
    @inproceedings{Cook2009,
      author = {Diane Cook and Maureen Schmitter-Edgecombe and Aaron Crandall and Chad Sanders and Brian Thomas},
      title = {Collecting and disseminating smart home sensor data in the CASAS project,},
      booktitle = {CHI Workshop on Developing Shared Home Behavior Datasets to Advance HCI and Ubiquitous Computing Research},
      year = {2009}
    }
    
    Crandall, A.S. & Cook, D.J. Coping with multiple residents in a smart environment 2009 Journal of Ambient Intelligence and Smart Environments
    Vol. 1(4), pp. 323-334 
    article DOI  
    Abstract: Smart environment research has resulted in many useful tools for modeling, monitoring, and adapting to a single resident. However, many of these tools are not equipped for coping with multiple residents in the same environment simultaneously. In this paper we investigate a first step in coping with multiple residents, that of attributing sensor events to individuals in a multi-resident environment. We discuss approaches that can be used to achieve this goal and we evaluate our implementations in the context of two physical smart environment testbeds. We also explore how learning resident identifiers can aid in performing other analyses on smart environment sensor data such as activity recognition.
    BibTeX:
    @article{Crandall2009,
      author = {Crandall, Aaron S. and Cook, Diane J.},
      title = {Coping with multiple residents in a smart environment},
      journal = {Journal of Ambient Intelligence and Smart Environments},
      publisher = {IOS Press},
      year = {2009},
      volume = {1},
      number = {4},
      pages = {323--334},
      doi = {http://dx.doi.org/10.3233/AIS-2009-0041}
    }
    
    Jakkula, V.R., Crandall, A.S. & Cook, D.J. Enhancing Anomaly Detection Using Temporal Pattern Discovery 2009 Advanced Intelligent Environments, pp. 175-194  incollection DOI  
    Abstract: Technological enhancements aid development and research in smart homes and . The temporal nature of data collected in a smart environment provides us with a better understanding of patterns that occur over time. Predicting events and detecting anomalies in such data sets is a complex and challenging task. To solve this problem, we suggest a solution using temporal relations . Our temporal pattern discovery algorithm, based on Allen’s temporal relations , has helped discover interesting patterns and relations from data sets. We hypothesize that machine learning algorithms can be designed to automatically learn models of resident behavior in a and, when these are incorporated with temporal information , the results can be used to detect anomalies. We describe a method of discovering temporal relations in data sets and applying them to perform anomaly detection on the frequently occurring events by incorporating information shared by the activity. We validate our hypothesis using empirical studies based on the data collected from real resident and virtual resident (synthetic) data.
    BibTeX:
    @incollection{Jakkula2009,
      author = {Jakkula, Vikramaditya R. and Crandall, Aaron S. and Cook, Diane J.},
      title = {Enhancing Anomaly Detection Using Temporal Pattern Discovery},
      booktitle = {Advanced Intelligent Environments},
      publisher = {Springer US},
      year = {2009},
      pages = {175-194},
      doi = {http://dx.doi.org/10.1007/978-0-387-76485-6_8}
    }
    
    Cook, D.J., Crandall, A.S., Singla, G. & Thomas, B. Detection of Social Interaction in Smart Spaces 2010 Cybernetics and Systems: An International Journal
    Vol. 41(2), pp. 90-104 
    article DOI  
    Abstract: The pervasive sensing technologies found in smart environments offer unprecedented opportunities for monitoring and assisting the individuals who live and work in these spaces. An aspect of daily life that is important for one's emotional and physical health is social interaction. In this paper we investigate the use of smart environment technologies to detect and analyze interactions in smart spaces. We introduce techniques for collecting and analyzing sensor information in smart environments to help in interpreting resident behavior patterns and determining when multiple residents are interacting. The effectiveness of our techniques is evaluated using two physical smart environment test beds.
    BibTeX:
    @article{Cook2010,
      author = {Cook, Diane J. and Crandall, Aaron S. and Singla, Geetika and Thomas, Brian},
      title = {Detection of Social Interaction in Smart Spaces},
      journal = {Cybernetics and Systems: An International Journal},
      year = {2010},
      volume = {41},
      number = {2},
      pages = {90--104},
      doi = {http://dx.doi.org/10.1080/01969720903584183}
    }
    
    Crandall, A.S. & Cook, D.J. Tracking systems for multiple smart home residents 2010 Human Behavior Recognition Technologies  incollection  
    Abstract: Once a smart home system moves to a multi-resident situation, it be-
    comes significantly more important that individuals are tracked in some
    manner. By tracking individuals the events received from the sensor plat-
    form can then be separated into different streams and acted on inde-
    pendently by other tools within the smart home system. This process
    improves activity detection, history building and personalized interaction
    with the intelligent space.
    Historically, tracking has been primarily approached through a carried
    wireless device or an imaging system, such as video cameras. These are
    complicated approaches and still does not always effectively address the
    problem. Additionally, both of these solutions pose social problems to
    implement in private homes over long periods of time. This paper intro-
    duces and explores a Bayesian Updating method of tracking individuals
    through the space that leverages the CASAS platform of pervasive and
    passive sensors. This approach does not require the residents to main-
    tain a wireless device, nor does it incorporate rich sensors with the social
    privacy issues.
    BibTeX:
    @incollection{Crandall2010,
      author = {Crandall, Aaron S. and Cook, Diane J.},
      title = {Tracking systems for multiple smart home residents},
      booktitle = {Human Behavior Recognition Technologies},
      publisher = {IGI Global},
      year = {2010}
    }
    
    Crandall, A.S. & Cook, D.J. Using a Hidden Markov Model for resident identification 2010 Proceedings of the International Conference on Intelligent Environments  inproceedings  
    Abstract: In smart home environments, it is highly desirable
    to know who is performing what actions. This knowledge allows
    the system to accurately build individuals’ histories and to take
    personalized action based on the current resident. Without a good
    handle on identity, multi-resident smart homes are less effective
    when used for medical and assistive applications.
    Most smart home systems either have a single occupancy
    requirement, or rely on a wireless or video device to identify
    individuals. These requirements are too burdensome in some
    situations, which can limit the deployment of smart home
    technologies in environments that would derive benefits from
    them. This research work introduces the use of passive sensors
    and a Hidden Markov Model as a means to identify individuals.
    The result is a passive, low profile means to attribute individual
    events to unique residents.
    For this work, two different pairs of individuals living in a
    smart home testbed are used to evaluate the tools. The data used
    is from unscripted, full time occupancy and annotated by the
    residents themselves for accuracy. Lastly, the Hidden Markov
    Model approach is compared and contrasted against a prior
    Naive Bayes solution on the same data sets.
    BibTeX:
    @inproceedings{Crandall2010a,
      author = {Crandall, Aaron S. and Cook, Diane J.},
      title = {Using a Hidden Markov Model for resident identification},
      booktitle = {Proceedings of the International Conference on Intelligent Environments},
      year = {2010}
    }
    
    Crandall, A.S. & Cook, D.J. Learning Activity Models for Multiple Agents in a Smart Space 2010 Handbook of Ambient Intelligence and Smart Environments, pp. 751-769  incollection DOI  
    Abstract: With the introduction of more complex intelligent environment systems, the possibilities for customizing system behavior have increased dramatically. Significant headway has been made in tracking individuals through spaces using wireless devices [1, 18, 26] and in recognizing activities within the space based on video data (see chapter by Brubaker et al. and [6, 8, 23]), motion sensor data [9, 25], wearable sensors [13] or other sources of information [14, 15, 22]. However, much of the theory and most of the algorithms are designed to handle one individual in the space at a time. Resident tracking, activity recognition, event prediction, and behavior automation becomes significantly more difficult for multi-agent situations, when there are multiple residents in the environment.
    BibTeX:
    @incollection{Crandall2010b,
      author = {Crandall, Aaron S. and Cook, Diane J.},
      title = {Learning Activity Models for Multiple Agents in a Smart Space},
      booktitle = {Handbook of Ambient Intelligence and Smart Environments},
      publisher = {Springer US},
      year = {2010},
      pages = {751--769},
      doi = {http://dx.doi.org/10.1007/978-0-387-93808-0_28}
    }
    
    Crandall, A.S. & Cook, D.J. Bayesian Updating for Individual Tracking in Smart Homes 2010 Washington State University Academic Showcase  conference  
    Abstract: The concept of a “Smart Home” is any kind of living space
    with sensors and controllers run by a computer. At WSU
    our CASAS testbed has several operational smart homes.
    These include on campus labs, apartment living spaces
    and private homes. These are true full-time and
    comprehensive smart homes. The testbeds are designed
    to use simple and robust sensors to detect activity and
    behavior within the space. The goal is to build algorithmic
    models of resident behavior to facilitate uses such as
    energy efficiency, aging in place and other
    gerontechnology systems.
    BibTeX:
    @conference{Crandall2010c,
      author = {Crandall, Aaron S. and Cook, Diane J.},
      title = {Bayesian Updating for Individual Tracking in Smart Homes},
      booktitle = {Washington State University Academic Showcase},
      year = {2010}
    }
    
    Crandall, A.S. & Cook, D.J. Tracking systems for multiple smart home residents 2011
    Vol. 9Behaviour Monitoring and Interpretation 
    incollection DOI  
    Abstract: Once a smart home system moves to a multi-resident situation, it becomes significantly more important that individuals are tracked in some manner. By tracking individuals the events received from the sensor platform can then be separated into different streams and acted on independently by other tools within the smart home system. This process improves activity detection, history building and personalized interaction with the intelligent space. Historically, tracking has been primarily approached through a carried wireless device or an imaging system, such as video cameras. These are complicated approaches and still does not always effectively address the problem. Additionally, both of these solutions pose social problems to implement in private homes over long periods of time. This paper introduces and explores a Bayesian Updating method of tracking individuals through the space that leverages the CASAS platform of pervasive and passive sensors. This approach does not require the residents to maintain a wireless device, nor does it incorporate rich sensors with the social privacy issues.
    BibTeX:
    @incollection{Crandall2011,
      author = {Crandall, Aaron S. and Cook, Diane J.},
      title = {Tracking systems for multiple smart home residents},
      booktitle = {Behaviour Monitoring and Interpretation},
      publisher = {IOS Press},
      year = {2011},
      volume = {9},
      doi = {http://dx.doi.org/10.3233/978-1-60750-731-4-65}
    }
    
    Crandall, A.S. Behaviometrics for Multiple Residents in a Smart Environment 2011 School: Washington State University  phdthesis  
    Abstract: Smart homes and ambient intelligence show great promise in the fields of medical monitoring, energy efficiency and ubiquitous computing applications. Their ability to adapt and react to the people relying on them positions these systems to be invaluable tools for our aging populations. This work introduces and explores solutions for issues surrounding real world multiple inhabitant smart home situations. Dealing with multiple residents without requiring wireless tracking devices, while paying heed to privacy concerns, is a difficult proposition at best.

    The Center for Advanced Studies in Adaptive Systems research group has developed and tested a number of novel technologies to address the issues of multiple inhabitants within a smart home context using inexpensive, low profile, privacy sensitive sensors. These smart home implementations, when combined with artificial intelligence tools, are designed to provide localization, tracking, and identification through behaviometric approaches that are useful and deployable in real world situations. They have been evaluated using unscripted living spaces with multiple residents, and their capabilities explored as a means of benefiting other modeling tools, such as detecting the Activities of Daily Living.

    Given the complex nature and diverse needs of smart home technologies, the tools presented here are by no means definitive solutions to handling multiple resident smart environment situations. However, they do provide a strong working base for the continued development of smart environments with demonstrable benefits on real world implementations.

    BibTeX:
    @phdthesis{CrandallDissertation,
      author = {Aaron S. Crandall},
      title = {Behaviometrics for Multiple Residents in a Smart Environment},
      school = {Washington State University},
      year = {2011}
    }
    
    Thomas, B.L. & Crandall, A.S. A Demonstration of PyViz, a Flexible Smart Home Visualization Tool 2011 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 304-306  inproceedings DOI  
    Abstract: As smart home technologies are deployed in re-
    search and real world environments, there is continuing need
    for quality visualization. These data come from a variety of
    sensor sources, artificial intelligence algorithms and human
    feedback. Building tools that are easily configured, rapidly
    developed and capable of communicating with both real time
    and historical data is ever challenging.
    This work introduces PyViz, a smart home-focused, middle-
    ware enabled, interactive interface. PyViz is the result of several
    years of testing and development in a large smart home re-
    search project. During its development, issues of configuration,
    ease of use and adaptability have been explored and overcome.
    The demonstration of this tool hopes to enlighten the audience
    about the facets of user interfaces, network agents and smart
    home complexity.
    BibTeX:
    @inproceedings{Thomas2011,
      author = {Thomas, Brian L. and Crandall, Aaron S.},
      title = {A Demonstration of PyViz, a Flexible Smart Home Visualization Tool},
      booktitle = {IEEE International Conference on Pervasive Computing and Communications Workshops},
      year = {2011},
      pages = {304--306},
      doi = {http://dx.doi.org/10.1109/PERCOMW.2011.5766889}
    }
    
    Chen, C., Cook, D.J. & Crandall, A.S. The User Side of Sustainability: Modeling Behavior and Energy Usage in the Home 2012 Pervasive and Mobile Computing  article  
    Abstract: Society is becoming increasingly aware of the impact that our lifestyle choices make on energy usage and the environment. As a result, research attention is being directed toward green technology, environmentally-friendly building designs, and smart grids. This paper looks at the user side of sustainability. In particular, it looks at energy consumption in everyday home environments to examine the relationship between behavioral patterns and energy consumption. It first demonstrates how data mining techniques may be used to find patterns and anomalies in smart home-based energy data. Next, it describes a method to correlate homebased activities with electricity usage. Finally, it describes how this information could inform users about their personal energy consumption and to support activities in a more energy-efficient manner. These approaches are validated by using real energy data collected in a set of smart home testbeds.
    BibTeX:
    @article{Chen2012,
      author = {Chao Chen and Diane J. Cook and Aaron S. Crandall},
      title = {The User Side of Sustainability: Modeling Behavior and Energy Usage in the Home},
      journal = {Pervasive and Mobile Computing},
      year = {2012},
      note = {[To Appear]}
    }
    
    Cook, D.J., Crandall, A.S., Thomas, B.L. & Krishnan, N.C. CASAS: A Smart Home in a Box 2012 Computer  incollection  
    Abstract: While the potential benefits of smart home technology are widely recognized, a lightweight design is needed for the benefits to be realized at a large scale. We introduce the CASAS “smart home in a box”, a lightweight smart home design that is easy to install and provides smart home capabilities out of the box with no customization or training. We discuss types of data analysis that have been performed by the CASAS group and can be pursued in the future by using this approach to designing and implementing smart home technologies.
    BibTeX:
    @incollection{Cook2012a,
      author = {Diane J. Cook and Aaron S. Crandall and Brian L. Thomas and Narayanan C. Krishnan},
      title = {CASAS: A Smart Home in a Box},
      booktitle = {Computer},
      publisher = {IEEE},
      year = {2012},
      note = {[to appear]}
    }
    
    Crandall, A.S. & Cook, D.J. Smart Home in a Box: A Large Scale Smart Home Deployment 2012 Workshop on Large Scale Intelligent Environments  conference  
    Abstract: Smart home technologies hold promise for many aspects of daily life.
    Research and development of these systems has matured for elder care, energy efficiency, and home safety applications.
    The focus of most implementations has been on single living spaces for a small number of individuals.
    New low-power wireless systems, inexpensive computing power, and widely available network access has reached the point where large scale ubiquitous computing technologies have become much more feasible.

    The smart environments research community has few projects to explore issues and techniques for deploying large scale ubiquitous systems.
    This work summarizes some of the existing works and introduces the Smart Home in a Box (SHiB) Project.
    The upcoming SHiB Project targets building 100 smart homes in several kinds of living spaces for gathering longitudinal data from a significant number of residents.
    The resulting data set will provide opportunities to answer open questions in the areas of transfer learning, active learning, digital asset migration, middleware architectures, activity detection and discovery, human factors, smart home installation, and others.

    BibTeX:
    @conference{Crandall2012,
      author = {Aaron S. Crandall and Diane J. Cook},
      title = {Smart Home in a Box: A Large Scale Smart Home Deployment},
      booktitle = {Workshop on Large Scale Intelligent Environments},
      year = {2012}
    }
    
    Crandall, A.S., Zulas, L., Feuz, K., Krishnan, N.C. & Cook, D.J. Visualizing Your Ward: Bringing Smart Home Data to Caregivers 2012 Emerging Technologies for Healthcare and Aging Workshop in the Proceedings of Computer Human Interaction at CHI  conference  
    Abstract: Health care visualization is a quickly evolving field.
    With a significant increase in the data available, care providers are continually being bombarded with increasingly complex information about their patients.

    Smart home technologies used to monitor older adults are no exception to this proliferation of information.
    Little work has been done to discern what pieces of data are most important to care providers and how to best deliver it.
    The Center for Advanced Studies in Adaptive Systems (CASAS) at Washington State University has launched a series of evaluations to derive a concrete understanding of which interfaces and data are most useful to nurses in a continuing care retirement center environment.

    This work will be used to guide how future smart environments are designed and presented to users.
    Research will also address how these same systems should be shown to residents and various kinds of care givers.
    The outstanding questions surrounding acceptance, usability and privacy are all open issues to be addressed.

    BibTeX:
    @conference{Crandall2012a,
      author = {Aaron S. Crandall and Leah Zulas and Kyle Feuz and Narayanan Chatapuram Krishnan and Diane J. Cook},
      title = {Visualizing Your Ward: Bringing Smart Home Data to Caregivers},
      booktitle = {Emerging Technologies for Healthcare and Aging Workshop in the Proceedings of Computer Human Interaction at CHI},
      year = {2012},
      note = {[to appear]}
    }
    
    Crandall, A.S., Thomas, B.L. & Cook, D.J. Exploring Smart Home Sensor Placement Algorithms 2012 Washington State University Academic Showcase  conference  
    Abstract: With the continued success of research into using smart homes for elder care applications, there is a commiserate drive to build tools to assist in the deployment of these smart technologies in the real world. This deployment process has numerous open issues, including knowing where to place the sensors for effective monitoring in the home. Until the smart home engineering community has a strong grasp on installing sensors in any home, it will have trouble providing successful commercial offerings based upon smart home technologies.
    BibTeX:
    @conference{Crandall2012b,
      author = {Aaron S. Crandall and Brian L. Thomas and Diane J. Cook},
      title = {Exploring Smart Home Sensor Placement Algorithms},
      booktitle = {Washington State University Academic Showcase},
      year = {2012}
    }
    
    Zulas, L., Crandall, A.S., Schmitter-Edgecombe, M. & Cook, D.J. Caregiver Needs from Elder Care Assistive Smart Homes: Nursing Assessment 2012 Human Factors and Ergonomics Society  conference  
    Abstract: With the elder population on the rise, assistive smart technology is positioned to help the assisted living
    community take on the upcoming age wave. Even with the notable volume of data collected from these
    systems, there are few researchers formally evaluating the needs of caregivers. This study attempts to
    inform researchers and engineers building smart technologies to better understand the needs of nurses as
    elder caregivers. Interviews suggest that nutrition, sleep length and quality, cleanliness of the individual,
    safety, and elopement by cognitively impaired individuals are of central concern. It is also important for
    programmers to make graphs with axis that have real world reliability. Sensor events are not relevant to
    nursing staff, and should not be presented in their raw form. Time increments are more appropriate for this
    population than number of sensor evens. With a little extra care during the design of user-facing tools, to
    the needs of caregivers, assistive smart homes can truly become helpful to keep our aging population
    independent for longer.
    BibTeX:
    @conference{Zulas2012,
      author = {Leah Zulas and Aaron S. Crandall and Maureen Schmitter-Edgecombe and Diane J. Cook},
      title = {Caregiver Needs from Elder Care Assistive Smart Homes: Nursing Assessment},
      booktitle = {Human Factors and Ergonomics Society},
      year = {2012},
      note = {[to appear]}
    }
    
    Crandall, A.S. & Cook, D.J. Behaviometrics for Identifying Smart Home Residents 2013   inbook  
    Abstract: Smart homes and ambient intelligence show great promise in the fields of medical monitoring, energy efficiency and ubiquitous computing applications.
    Their ability to adapt and react to the people relying on them positions these systems to be invaluable tools for our aging populations.
    The most privacy protecting and easy to use smart home technologies often lack any kind of unique tracking technologies for individuals.
    Without a built-in mechanism to identify which resident is currently triggering events, new tools need to be developed to help determine the identity of the resident(s) in situ.

    This work proposes and discusses the use of behaviometrics as a strategy for identifying people through behavior.
    By using behaviometrics-based approaches, the smart home may identify residents without requiring them to carry a tracking device, nor use privacy insensitive recording systems such as cameras and microphones.
    With the ability to identify the residents through behavior, the smart home may better react to the multitude of inhabitants in the space.

    BibTeX:
    @inbook{Crandall2013,
      author = {Aaron S. Crandall and Diane J. Cook},
      title = {Behaviometrics for Identifying Smart Home Residents},
      publisher = {Atlantis Press},
      year = {2013},
      note = {[Under Review]}
    }
    
    Crandall, A.S., Zulas, A.L. & Cook, D.J. Smart Home in a Box: Deploying Large-Scale In-Home Smart Environments 2013 Intelligent Buildings International Journal  article  
    Abstract: Smart home technologies hold promise for many aspects of daily life.
    Research and development of these systems has matured for elder care, energy efficiency, and home safety applications.
    The focus of most implementations has been on single living spaces for a small number of individuals.
    New low-power wireless systems, inexpensive computing power, and widely available network access has reached the point where large scale ubiquitous computing technologies have become much more feasible.

    The smart environments research community has few projects to explore issues and techniques for deploying large scale ubiquitous systems.
    This work summarizes some of the existing works and introduces the Smart Home in a Box (SHiB) Project.
    The upcoming SHiB Project targets building 100 smart homes in several kinds of living spaces for gathering longitudinal data from a significant number of residents.
    The resulting data set will provide opportunities to answer open questions in the areas of transfer learning, active learning, digital asset migration, middleware architectures, activity detection and discovery, human factors, smart home installation, and others.

    BibTeX:
    @article{Crandall2013a,
      author = {Aaron S. Crandall and A. Leah Zulas and Diane J. Cook},
      title = {Smart Home in a Box: Deploying Large-Scale In-Home Smart Environments},
      journal = {Intelligent Buildings International Journal},
      year = {2013},
      note = {[Under Review]}
    }
    
    Seelye, A.M., Schmitter-Edgecombe, M., Cook, D.J. & Crandall, A. Smart environment prompting technologies for everyday activities in mild cognitive impairment 2013 Journal of the International Neuropsychological Society  article  
    Abstract: Older adults with mild cognitive impairment (MCI) often have difficulty performing complex
    instrumental activities of daily living (IADLs), which are critical to independent living. In this
    study, amnestic multi-domain MCI (N = 29), amnestic single-domain MCI (N = 18), and healthy
    older participants (N = 47) completed eight scripted IADLs (e.g., cook oatmeal on the stove) in a
    smart apartment test bed. We developed and experimented with a graded hierarchy of
    technology-based prompts to investigate both the amount of prompting and type of prompts
    required to assist individuals with MCI in completing the activities. When task errors occurred,
    progressive levels of assistance were provided, starting with the lowest level needed to adjust
    performance. Results showed that the multi-domain MCI group made more errors and required
    more prompts than the single-domain MCI and healthy older adult groups. Similar to the other
    two groups, the multi-domain MCI group responded well to the indirect prompts and did not
    need a higher level of prompting to get back on track successfully with the tasks. Need for
    prompting assistance was best predicted by verbal memory abilities in multi-domain amnestic
    MCI. Participants across groups indicated that they perceived the prompting technology to be
    very helpful.
    BibTeX:
    @article{Seelye2013,
      author = {Adriana M. Seelye and Maureen Schmitter-Edgecombe and Diane J. Cook and Aaron Crandall},
      title = {Smart environment prompting technologies for everyday activities in mild cognitive impairment},
      journal = {Journal of the International Neuropsychological Society},
      year = {2013},
      note = {[To appear]}
    }
    
    Thomas, B.L., Crandall, A.S. & Cook, D.J. An Exploration of Motion Detector Sensor Placement in Smart Environments 2013 Journal of Ambient Intelligence and Smart Environments
    Vol. 1, pp. 1-5 
    article  
    Abstract: Smart homes and ubiquitous computing technologies hold great promise for a wide range of real world applications.
    The Center of Advanced Studies in Adaptive Systems (CASAS) uses these sensor-rich environments to gather information about people as they go about their daily lives.
    The information gathered supports tools targeted towards energy efficiency, health care, and others.
    One aspect of effectively deploying these technologies is determining where the sensors should go in the home to effectively support these end goals.

    This work introduces and evaluates a set of approaches for algorithmically generating successful sensor layouts in the home.
    These approaches range from the gold standard of human intuition-based placement to more advanced search algorithms, including Hill Climbing and Genetic Algorithms.
    The ultimate goal of these tools is to be run with little to no user input and output a reasonable layout of sensors.
    The final layout created should be effective at detecting activities, while also being sensitive to total monetary cost of the smart home deployment.

    BibTeX:
    @article{Thomas2013,
      author = {Brian L. Thomas and Aaron S. Crandall and Diane J. Cook},
      title = {An Exploration of Motion Detector Sensor Placement in Smart Environments},
      journal = {Journal of Ambient Intelligence and Smart Environments},
      year = {2013},
      volume = {1},
      pages = {1--5},
      note = {[under review]}
    }
    

    Created by JabRef on 18/01/2013.