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					  Fabio Pianesi
Massimo Zancanaro
          FBK-irst
 Alessandro Cappelletti, Bruno
      Lepri, Nadia Mana
Research questions

   Recognition of is happening at a given time
    slice (mainly) from audio-visual signals




Fabio Pianesi & Massimo Zancanaro FBK
Data sharing requirements
   Indipendent modules for
       Visual recognition robust for lighting for
           Detecting parts of the body
           Detecting and recognizing objects

   Attentional module to focus cameras, mics and other sensors
    where “the action is”
       Avoids continuous streams of data from the environment

   Standards
       Annotation of activities
       Meta-data descriptions
           Type of sensors, their relative position in the environment
           Environment description (relative to sensors: riverberation,
            …)
       Standard for data storing

Fabio Pianesi & Massimo Zancanaro FBK
Gregory D.
  Abowd
 Georgia Tech
Research questions

   How to address questions of health and (to
    a lesser degree) sustainability through
    instrumentation and augmentation of the
    home. How to enable others to collect data
    in real homes.

   Chronic care management
       Early detection and monitoring of
        interventions for autism
       Video data and sensor data                 Room #1   Room #2
                                                                          Bus
                                                                          Monitoring

   Sensing for the masses
                                                                          Sensors


           Infrastructure mediated sensing data   Room #n     Electrical Machine
                                                               Outlets and Learning
            from real homes                                    Appliances System
                                                               Air Ducts
           Energy awareness, location-tracking                            Inferred
                                                               Plumbing Human
                                                               Fixtures    Activity


Gregory D. Abowd, Georgia Tech
Data sharing requirements

   I want annotated home movies of young child behavior, or at
    least movies that I can annotate and make available as a shared
    data set for the vision community to work on.

   I want to provide (through commercial efforts) the ability to
    collect low-level sensor data of home activity so that you can
    collect data in real homes.




Gregory D. Abowd, Georgia Tech
   Aaron Crandall
  Washington State University

   D.J. Cook, M. Schmitter-
Edgecombe, Chad Sanders, Brian
           Thomas
Collecting and Disseminating Smart Home
     Sensor Data in the CASAS Project
D.J Cook, M. Schmitter-Edgecombe, Aaron Crandall, Chad
               Sanders and Brian Thomas
                  cook@eecs.wsu.edu
                   CASAS Testbed
• Comprehensive
• Agent-oriented
• Both office and living spaces
• Scripted and unscripted data
• Focused on ADL detection
                     The Space & Sensors
• Describing the physical space:
  • Implications on resident behavior
  • Issues with changes
• The sensors:
  • Location
  • Relationships
  • Implications
  • Configurations
  • Versions
The Data Fields and Format
     When collected, the CASAS data is very simple:
                   Annotation & ADLs
• Correct annotation is still a limiting factor
• Detail of annotation drives cost of effort and
 accuracy

• Proper notation of both correct activity
 completion and activity errors
                  Final Core Issues
• Ensuring clean data
• Annotation accuracy & length
• Generating sufficiently varied data
• Properly describing test bed configurations
WSU Smart Home Dataset
Available Now
Thank you

Shared Datasets:
http://www.ailab.wsu.edu/casas/datasets.html


Contact info:
Aaron S. Crandall
acrandal@wsu.edu

Diane J. Cook
cook@eecs.wsu.edu
Lorcan Coyle
     Lorcan.coyle@lero.ie
   Lero – The Irish Software
  Engineering Research Centre
     University of Limerick

Juan Ye, Susan McKeever, Stephen
  Knox, Matthew Stabeler, Simon
     Dobson, and Paddy Nixon
     University College Dublin
Research questions
                                                       McKeever et al.,
   we are interested in activity recognition           Pervasive LBR
       Bayesian networks & lattice theory,             2008
        Dempster Shafer evidence theory, case-
        based reasoning                                Stabeler et al.,
                                                        Pervasive LBR
       more realistic and/or more crisp datasets       2008
        for evaluations
                                                       Knox et al., RIA
   we are also gathering our own datasets              2008
       based on best principles? - CASL
       (also we have some “toy datasets”)             Ye et al., RIA 2008

                                                       Ye et al., ICPS 2008

                                                       Ye et al., Percom
                                                        2009



Lorcan Coyle, Lero@UL
Data sharing requirements

   there should be a web-based repository like the UCI ML
    repository

   we need a common language for datasets
       and parsers to allow interoperability

   algorithms should be released!
       like Weka or in Weka?

   results need to be published beyond the paper!
       put results up with the datasets
       tag datasets with 3rd party opinions and cite the paper where
        the results are presented

 ultimately we need to make it transparent for
    reviewers/scientists to understand a “good result”

Lorcan Coyle, Lero@UL
Fernando De la Torre
  Jessica Hodgins
   Javier Montano
  Sergio Valverde

  Carnegie Mellon
    University
http://kitchen.cs.cmu.edu/
Research questions
   How to build good computation models to characterize subtle human
    motion?
       Develop machine learning algorithms for activity recognition and
        temporal segmentation (supervised/unsupervised) of human motion
       Judgments about the quality of motion

   How to select or fuse multimodal data for activity recognition?

   What should be a good protocol for multimodal data capturing?




Fernando, Carnegie Mellon University
Data sharing requirements

   Shared datasets:
     45 people cooking 5 different recipes (brownies, salad, pizza,
      sandwich, eggs)
     Each recipe is about 22 minutes and 5 synchronized modalities are
      recorded (audio, video, motion capture, inertial measurement
      units)
     Anomalous situations (falling, fire, mistaken putting soap rather
      than salt, …)
     Camera calibration parameters, time stamps for each modality
     Shared labels for object recognition, temporal segmentation and
      activity recognition

   Shared code:
     Multimodal data visualization toolbox (Matlab).
     Baseline experiments for activity recognition and temporal
      segmentation.
     Aligned Cluster Analysis: Clustering of time series.

Fernando, Carnegie Mellon University
      James
     Fogarty
      Assistant Professor
Computer Science & Engineering
Research questions

   Attacking human-computer interaction
    problems using statistical machine learning

   Previously with a significant focus on sensing
     Sensor-based human interruptibility models
     Privacy-sensitive approach to collecting
      sensed data in location-based applications
     Unobtrusive home activity sensing
      (collaborations pulling me back into this)

   More recently focused on domains where it is
    actually possible to attack the entire problem
     End-user interactive concept learning
      (with application in Web image search)
     Mixed-initiative information extraction
      (with application to semantifying Wikipedia)



James Fogarty, University of Washington
Data sharing requirements

   Convincingly answering compelling HCI questions typically requires
    some custom data collection (either formative or summative data)
       Those datasets are expensive and difficult to collect
       We therefore look for the minimal collection to answer our question
       Rendering the collected data largely useless for other questions

   Data sharing can have important value, but we also need to examine
    other approaches to achieving the same intended benefits
       Work on different problems (like the Web, where there’s lots of data!)
       Improved coordination of collection (work with others to reduce costs)
       Improved standardization of collection (agree what’s important to
        collect)
       Improved collection tools (lower barrier to getting it in the first place)
       Improved annotation tools (lower barrier to coding it later)



James Fogarty, University of Washington
  Stephen
   Intille
Massachusetts Institute of
      Technology
Research questions

   How can just-in-time information presented by context-aware
    technology in the home and worn on the body help people stay
    healthy as they age?

   How do we make activity detection algorithms that work for
    non-techies in real life in complex situations using practical and
    affordable sensor infrastructures?

    End-user concerns/challenges that have not been adequately
    addressed…
    * Practical sensor installation
    * Maintaining sensors
    * Fixing mistakes
    * Adding activities

    Toothbrushing


Stephen Intille (MIT)
Data sharing requirements

   What shared datasets or tools, if any, would best advance your
    work (on automatic detection of activity for health systems) ?

   Datasets of 10-100 families in their homes doing everyday
    activities for months with accurate labeling of activity, postures,
    and audio transcription and synchronized with data from 3-axis
    accelerometers on each limb, object usage data on as many
    objects as possible, current flow sensing on electrical devices,
    and indoor position information on each occupant (1m accuracy).

   Datasets of 10-100 people doing everyday activities in natural
    settings for weeks or months with accurate labeling of type and
    intensity (energy expenditure) of physical activity while wearing
    3-axis accelerometers on each limb.




Stephen Intille (MIT)
                                                     Taketoshi
                                                       MORI
                                                     Mechano-Informatics,
                                                    The University of Tokyo

                                                     Masamichi Shimosaka,
                                                         Akinori Fujii,
                                                        Kana Oshima,
                                                       Ryo Urushibata,
                                                       Tomomasa Sato,
                                                        Hajime Kubo,
                                                       Hiroshi Noguchi


                 Sensing Room and Its Resident Behavior Mining
                                   CHI 2009 Workshop
Developing Shared Home Behavior Datasets to Advance HCI and Ubiquitous Computing Research
Research questions
Sensing Room and Its Resident Behavior Mining

   We have constructed several room-type
    human behavior sensing environments.
    These used many distributed sensors. The
    key was location sense. The problems
    were
       A long-term recording is difficult,
       Time synchronization is difficult,
       Annotating is such a bother!

   Based on the collected behavior data, we
    have been constructing services such as
    action anticipation, beat-one information display
    and robotic support.
   We introduced for these problems,
       Multi-layered network system,
       Distributed object software scheme,
       RDF/OWL knowledge representations.

Taketoshi Mori, the University of Tokyo
Data sharing requirements
Sensing Room and Its Resident Behavior Mining

   Developing algorithms to detect unusual behavioral phenomenon
    or to foresee stereotyped frequently occurring behaviors for
    supporting human, it is necessary to obtain human’s position in
    the space with timestamp. Distributed sensors should supply
    sufficient information to estimated the human position. It may
    help if the timestamp is marked both at the sensed time by
    sensors and the recorded time by the home server.

   The datasets with many additional data,
    such as the resident’s profile, 3D room
    models, the wall and floor textures,
    the weather and temperature help to
    construct an appropriate behavior
    estimation method.
   The datasets should be constructed based on
    some tagged formats as XML or YAML, and
    preferably the tags are added following RDF.

Taketoshi Mori, the University of Tokyo
Tim van Kasteren
Intelligent Systems Lab Amsterdam
      University of Amsterdam
        Co-author: Ben Kröse
Research questions

   Which probabilistic model is best for modeling human behavior?

         How to deal with unsegmented data?

         How to capture long term dependencies?

         How to deal with the large number of ways in which activities can be
          performed?


   How can we apply these models on a large scale, without the
    necessity of training data from each house they are applied?

         How to deal with different layout of houses?

         How to deal with different behavior of people?




Tim van Kasteren (University of Amsterdam)
Data sharing requirements

   To validate the effectiveness of our models, we need:
         Datasets consisting of several days (weeks) of data recorded in a
          real world setting.
         We have mainly used wireless sensor networks, but we are
          interested in validating our models on other sensing modalities as
          well.

   To validate the application of our models on a large scale, we
    need:
         Datasets from multiple houses.
         Ideally consisting of a fixed set of sensors and labeled activities.

   We offer:
         Several real world datasets consisting of at least two weeks of fully
          labeled data each.

Tim van Kasteren (University of Amsterdam)
   Sumi Helal
  University of Florida, Andres
Mendes-Vazquez, Diane Cook and
       Shantonu Hussein

       www.icta.ufl.edu
Research questions

   How can we synthesize sensory datasets either from scratch or by
    “stem-celling” existing actual datasets?

   Synthesis is necessary to enable researchers with limited
    resources but with great ideas and algorithms that need to be
    thoroughly tested.

   Synthesis could also be needed by the owner of an actual dataset,
    to enable/him/her to go back in time and explore additional
    concerns/goals not thought of during data collection.

   What are the synthesis strategies/algorithms?

   How good are synthesized datasets? How can we assess our
    success or failure in this direction.

    What does “Sensory Dataset Description Language” standard has
    to do with data synthesis?


Sumi Helal, University of Florida
Data sharing requirements

   Simply, access to a database of well documented datasets will
    advance our research and tool development in sensory data
    synthesis.
       What is of great importance to us is documentation of the
        “protocol” used to collect the data, not just the data itself.

   To be able to utilize other people datasets, and to foster a
    greater level of interoperability and cross use of data sets, we
    have been working on defining a standard to propose to the
    community. We call the standard: “Sensory Dataset Description
    Language” or SDDL. The SDDL specification proposal can be
    downloaded from:        http://www.icta.ufl.edu/persim/sddl/

   We have utilized 4 datasets in defining this standard proposal.
    We wish to consider many more datasets in refining this
    proposal. Your comments AND contributions to SDDL are sought.


Sumi Helal, University of Florida
 Allen Yang
with Phil Kuryloski and Ruzena
            Bajcsy

        UC Berkeley
DexterNet: A Wearable Body Sensor
System

   Primary Goals
    1.   Real-time control & sampling of
         heterogeneous body sensors
    2.   Secured surveillance in indoors and
         outdoors
    3.   Provides geographical and social data

   System Architecture
    1.   Body Sensor Layer (BSL)
    2.   Personal Network Layer (PNL)
    3.   Global Network Layer (GNL)

   Prototype Systems
    1.   Human action recognition
    2.   State-of-the-art security features
    3.   Real-time communication between
                                                   Reference: BSN Workshop, 2009.
         Berkeley and Vanderbilt Hospital tested



Allen Yang, UC Berkeley
Wearable Action Recognition
Database (WARD), version 1

   Free for noncommercial users

   5 motion sensors, each carries an
    accelerometer and gyroscope sampled at 30
    Hz

   20 test subjects (13 male & 7 female) ages 19-
    75

   13 action categories collected in an indoor lab
        standing          sitting          sleeping

        walking           running            jumping

        turning      upstairs/downstair   pushing objects
                              s




   Data processed in Matlab. Visualization tool is
    included


Allen Yang, UC Berkeley
Workshop schedule

    9:00 Overview and goals
    9:15 Introductions by attendees
    10:30 Break
    10:45 Targeted questions and answers
    12:00 State-of-the-art in data
     collection
    12:30 Lunch
    14:00 Discussion: What's possible?
    14:20 Group exercise
    15:20 Group presentations
    16:00 Break
    16:15 Next steps
    17:30 End of workshop
Gregory D.
  Abowd
 Georgia Tech
Question & Answers #1

   Why do you want family home movies?
       Sufficient retrospective research in the autism domain has
        shown that there is evidence of developmental delay in home
        movies. This has value for early detection and early
        intervention.
       We have shown that you can encourage the collection of
        relevant developmental milestone behavior from parents, but
        not of rich evidence like video.
       We are working on filtering techniques to pull out the relevant
        snippets of social interaction.
       Ultimately, I envision a way to upload home movies to a
        secured service that can then extract relevant portions to
        share with a pediatrician or other professional for screening
        purposes.



Gregory D. Abowd, Georgia Tech
Question & Answers #2

   What is the value of infrastructure mediated sensing to other
    researchers?


       This is a way to gather low-level sensing data from real
        homes.
       There is both commercial and research opportunities here and
        I think the commercial opportunities in demand-side energy
        management may be able to drive the ability to provide
        valuable resources for researchers to leverage.




Gregory D. Abowd, Georgia Tech
  Fabio Pianesi
Massimo Zancanaro
          FBK-irst
 Alessandro Cappelletti, Bruno
      Lepri, Nadia Mana
Question & Answers #1

   How would low-bandwidth sensing (e.g. passive infrared motion
    detection, object movement sensors, RFID) complement the
    methods used in the NETCARITY project

   Attention mechanism to that activates/deactivates camera/mikes
    when someone enters a room

   Fusion of multiple modalities
       Recognition of objects (manipulation)
       Information about body (and body segments) activity levels,
        posture changes, etc.




Fabio Pianesi & Massimo Zancanaro FBK
        Question & Answers #2

   How could the data on target behaviors in NETCARITY be used to
    improve segmentation of activities in recordings of ongoing natural
    behavior?

   Segmentation is a ill-posed problem because it confuses two level: the
    description of an activity and the intention of the performer

   Example:
       While I cook spaghetti, I go to the restroom. A friend call and I say
        “I’m cooking” (still in the restroom!)
       I grab an hammer and my wife asks me about what I’m doing: “I’m
        hanging the painting” but I have not yet started (or not?)

   Telic events have a clear end but still lack a clear start
       If the apple is finished, you have ate an apple
       But it’s hard to agree on the start (or if you leave the apple on the
        table)
        Fabio Pianesi & Massimo Zancanaro FBK
Question & Answers #3

   How might recordings from the high density microphone arrays
    used in this project provide value to other researchers? Would
    this justify the cost?

   For what concerns event detection, we had disappointing results
    from microphone

   They can be useful to monitor verbal and para-verbal activities
    to estimates:
       personality traits (Pianesi et al. 2008; Lepri et al. 2009)
       mood




Fabio Pianesi & Massimo Zancanaro FBK
   Aaron Crandall
  Washington State University

   D.J. Cook, M. Schmitter-
Edgecombe, Chad Sanders, Brian
           Thomas
Lorcan Coyle
     Lorcan.coyle@lero.ie
   Lero – The Irish Software
  Engineering Research Centre
     University of Limerick

Juan Ye, Susan McKeever, Stephen
  Knox, Matthew Stabeler, Simon
     Dobson, and Paddy Nixon
     University College Dublin
Combining Redundant Data

“In the CASL Dataset, how might overlapping data from Ubisense
  locator, pressure mats, and Bluetooth spotters be used to good
  advantage?”

   tells us a lot about the data quality
       reveals when sensors are not operating optimally

   allows us to make more educated guesses

   we can test with subsets

   the sensors aren’t where the cost is (imho)

   without redundant data streams there are certain algorithms we
    cannot test
       voting/weighting strategies?
       it’s easier to play with DS evidence theory

Lorcan Coyle, Lero@UL
Bootstrapping Users to a
Dataset

“Describe the concept of “bootstrapping” datasets for new users
  and discuss how this might be done efficiently for large long-
  term datasets”

   release parsers/interfaces to deal with your dataset

   how about sample experiments?

   really simple worked-through tutorial examples subsets of the
    dataset

   e.g., using only RFID and object sensors:
       10:12pm: Prof. Plum enters kitchen
       10:14pm: candlestick sensor active
       10:16pm: Prof Plum enters hallway

 reducing the learning curve
Lorcan Coyle, Lero@UL
Fernando De la Torre
  Jessica Hodgins
   Javier Montano
  Sergio Valverde

  Carnegie Mellon
    University
Question & Answers #1

   How might body motion capture be most practically implemented
    in a natural home environment?

   Wearable:
        Small wireless Inertial Measurement Units distributed
         through the body.

   Instrumented environment:
        Sparse information: motion sensors around the house.
        Rich information: multiple cameras (at least 3)




Fernando, Carnegie Mellon University
Question & Answers #2

   Given the choice between high resolution (1024x768, 30fps) or
    high frame rate (640x480, 60fps) video, which do you think
    would be more beneficial to the greatest number of researchers?

   It depends on the task
       Subtle activity recognition (e.g. grasping a fork) or object
        recognition probably is better higher resolution
       Egomotion computation from wearable camera or fast
        activities such as cutting a cucumber, probably better higher
        frame-rate




Fernando, Carnegie Mellon University
      James
     Fogarty
      Assistant Professor
Computer Science & Engineering
Question & Answers #1

 Discuss why you believe it is or is not possible to collect
  general-purpose shared datasets on home behavior.
     Very simple to collect shared datasets on home behavior
      (see the website for this workshop, we’ve already succeeded!)

     The notion that a dataset is general purpose is what makes it
      difficult

         Data collection is hard enough and expensive enough
          when focused on answering your own research questions

     Asking the question also implies that recognition is the goal

     Is that what we’re doing here in the HCI community?

     Maybe we should be looking for the HCI contributions we can
      make without solving the hard general activity recognition
      problem


James Fogarty, University of Washington
Question & Answers #2

 Describe one way researchers might solicit and distill community
  input before undertaking a data collection project.

     Focus on researcher awareness of the benefits they personally
      might obtain from collecting data, then encourage them to share

     Tag existing datasets by the types of data they contain
         When designing a new data collection, a researcher can easily
          see what kinds of data other people have previously collected in
          tandem with what you are already planning to collect
         Can also see why they collected it, imagine what additional
          benefit its collection would have to your current research

     Try to identify a way to solicit wishlists or shortcomings of
      datasets

     Make it really easy to search, link to related tools, etc.


James Fogarty, University of Washington
  Stephen
   Intille
Massachusetts Institute of
      Technology
Question & Answers #1

   Do you think low cost, off-body sensors be used to detect
    postural transitions or physical activity with any useful degree of
    accuracy?


    Best hope: computer vision (technically and socially tough)

    Interesting question: how close can you get when other sensors
    are ubiquitous?




Stephen Intille (MIT)
Question & Answers #2

   How are you addressing the logistical challenges associated with
    mobile computing research (e.g., user compliance, comfort,
    battery life)?


    -   One night, one recharge
    -   Same thing every day
    -   Phone prompting if no compliance
    -   Looking for apps to inspire compliance
    -   Leave stuff by the door (retrain user)
    -   Sensors: small enough to wear under clothing


    - Tricky IRB/social issue: what to do outside home




Stephen Intille (MIT)
                                                     Taketoshi
                                                       MORI
                                                     Mechano-Informatics,
                                                    The University of Tokyo

                                                     Masamichi Shimosaka,
                                                         Akinori Fujii,
                                                        Kana Oshima,
                                                       Ryo Urushibata,
                                                       Tomomasa Sato,
                                                        Hajime Kubo,
                                                       Hiroshi Noguchi


                 Sensing Room and Its Resident Behavior Mining
                                   CHI 2009 Workshop
Developing Shared Home Behavior Datasets to Advance HCI and Ubiquitous Computing Research
Question & Answers #1
Sensing Room and Its Resident Behavior Mining

   What challenges would you anticipate for installing magnetic
    motion capture in natural homes? What alternative strategies for
    capturing bodily motion would you consider?
   Magnetic motion capture systems work poorly
    when there exist many metallic things. Also, there
    may be troublesome cables between magnetic
    sensors distributed on human body and the
    controller. We do not expect the magnetic capture
    systems as the usual behavior collecting way, but
    it may be used to prepare ground truth
    motion/posi-tion since other mo-caps such as
    optical/super-sonic-based are greatly influenced
    by occlusions.
   2D/3D stationary laser range sensors may be used
    to measure human position and pose. Appliances
    usage tells a lot about home behaviors. Some people
    may wear wrist-watch type accelerometer with gyros.

Taketoshi Mori, the University of Tokyo
Question & Answers #2
Sensing Room and Its Resident Behavior Mining

   Describe your schema for annotating behaviors in Sensing Room.
    What were strengths and weaknesses of the annotation
    procedure?

   Sensing Room accumulates the resident’s place by its floor
    distributed pressure sensors, object carrying actions by RFID
    readers, and many other acts by electric switches or electric
    current sensors. All the data are put together and can be
    displayed as 3D-CG images. Watching the CG video, several
    researchers write down the behavior annotation by hand.

   Our procedure has advantage that it can
    be done offline. No camera surveillance
    is required. But, of course, it has the
    weakness that the correctness depends
    both on the lucidness of the graphics
    and the interpretation of the annotators.

Taketoshi Mori, the University of Tokyo
Tim van Kasteren
Intelligent Systems Lab Amsterdam
      University of Amsterdam
        Co-author: Ben Kröse
Question & Answers #1

   Q: How might you change the real-time voice-activated
    annotation procedure to reduce the burden on the user?

   A: The current system requires the user to constantly be aware
    of the activity he/she is involved in and report this.

         If the system would ask the user what activity is being
          performed this would reduce the burden.

         The system could ask the user at times when sensor
          patterns are most ambiguous with respect to the activities
          annotated.

   However, the question remains how this effects the reliability of
    the annotation method.


Tim van Kasteren (University of Amsterdam)
Question & Answers #2

   Q: Describe how the activities to be annotated were selected.
    How and why would you change this list in future data
    collection?

   A: Activities were selected based on previous work, literature on
    activities of daily living (ADLs) and based on what would seem
    challenging yet feasible using the sensor platform used.
         In future data collection more detailed activities would be
          annotated. For example, annotating getting tea and getting
          juice, instead of getting a drink.
         More detailed activities can always be grouped into a
          collective activity afterwards, but extend the lifetime of a
          dataset.

   However, there is always a trade-off between cost and gain.

Tim van Kasteren (University of Amsterdam)
 Allen Yang
with Phil Kuryloski and Ruzena
            Bajcsy

        UC Berkeley
Question & Answers #1

   Advantages of wearable sensors over environmental sensors?
    1. Cost less to instrument, especially in outdoors
    2. Richer interaction with subjects (physiological sensors, message feedback)

   Integration of wearable and environmental sensors?
    1. Certain environmental sensors are not portable (size and battery)
    2. Wearable sensors can provide localization services, which then correlate with
       environmental information.




                                          Airborne Particulate Matter Concentrations

Allen Yang, UC Berkeley
Question & Answers #2

   Plan for future databases?
    1. Human Motion Interaction: WARD version 2.
    2. Integrating Geographic Data (with Oakland Children’s Hospital): Long-term
         monitoring of 160 obese patients correlated with environmental factors.
    3.   Integrating Social Interaction: Port DexterNet platform to consumer-ready
         smart phones (iPhone and gPhone).




Allen Yang, UC Berkeley

				
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