Taimur's Dissertation Presentation

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    Taimur's Dissertation Presentation - Presentation Transcript

    1. A Mobile Context-Aware Behavior Modification System for Healthy Lifestyle Management October 17, 2007 Taimur Hassan
    2. Agenda
      • Introduction
      • Problem Statement
      • Research Questions
      • Background
      • HMSS Details
      • Evaluations
      • Research Answers
      • Contribution
      • Future Goals
      • Questions
    3. Introduction
      • This dissertation emerged from collaboration with Kaiser Permanente’s preventive medicine division
      • The division conducts classes that aim at educating participants about weight management through:
        • Adopting a balanced diet in their daily life
        • Counting calories
        • Exercise
        • Reducing visits to fast-food restaurants
    4. Problem Statement
      • Problems with the preventive medicine approach:
        • Maintaining motivation outside classroom is difficult because of the environment participants often face
        • Keeping track of activity and related parameters is tedious
      • Proposed idea: A mobile system that sends motivational messages based on analyses of a participant’s activity and location data. It has been named the Health Management and Support System (HMSS)
    5. Research Questions
      • Before such a system could be designed, questions to be answered included:
        • How could activity and location information be captured?
        • Can these sources be feasibly used for choosing feedback related to the health choices of a user?
        • Could such feedback be effective in changing the behavior of a user?
    6. Background
      • To understand how HMSS works, a brief introduction to pervasive and context-aware computing is needed
      • Pervasive computing refers to an arrangement where information processing has been integrated into everyday objects that surround a user
        • A common example given is a refrigerator that can sense what is placed inside and can warn the owner if something has expired or finished and put the item on an electronic shopping list
    7. Background (cont.)
      • What is Context?
        • The set of facts or circumstances that surround a situation or event (Wordnet)
      • Context-aware computing is a sub-discipline of pervasive computing
      • Describes the idea of computer systems reacting to what is sensed in the user’s environment
      • An example is a room’s light dimming when the system senses the subject wants to lie down
      • Context can be sensed using information such as activity, location, time of day and schedule using a variety of sources
      • Why is context so important for HMSS?
        • Studies conducted suggest that contextualization of motivational messages reduces the chances of the message being ignored
        • HMSS uses context to select the most appropriate message
    8. Background (cont.)
      • Some examples of context-aware applications:
        • A context-aware remote control developed at MIT which combines behavior modification theory and daily activity records to persuade users to increase their physical activity and reduce television viewing
        • Mitchell et al. describe an application which is wired with sensors to track users and allow them to summon each other without worrying about location
        • SenSay phone developed by MIT can provide remote callers with the ability to communicate the urgency of their calls, make call suggestions to users when they are idle, and provide the caller with feedback on the current status of another SenSay user based on sensor data
    9. HMSS Overview
      • HMSS is based on knowledge from these main domains, as well as related fields such as mobile and sensor based computing
      • It is a prototype that sends just-in-time motivational messages based on a user’s context
      • Software packages include:
        • .NET Windows Mobile SDK
        • Java, Visual C#, PHP
        • MySQL database
        • Google Maps API
        • GT2K gesture recognition library
      • Hardware used:
        • Windows Mobile Verizon XV6700 smartphone
        • iTrek M5 Bluetooth GPS Receiver
        • Sparkfun Bluetooth Accelerometers
      • HMSS consists of two artifacts:
        • A mobile client application
        • A server
    10. Mobile Client Application
      • Connected to server via a subscribed data service
      • Has two components:
        • Sensor Manager
          • Establishes connection with accelerometer and GPS receiver
          • Responsible for upload, compression and transmission of raw data to server
        • GUI
          • Controls sessions
          • Controls sensor data retrieval process
          • Contains menus for setting session properties
      • Feedback from the server is displayed in the GUI
    11. Server
      • Has four components:
        • Session Manager
          • Handles data storage and processing for a session
          • Contains GT2k and other functions for handling raw data
        • A database
          • Stores data related to all aspects of HMSS
        • Message Selector
          • Selecting feedback based on context
        • Message Handler
          • Customizes feedback and responsible for dispatch to mobile client
    12. Data Architecture
      • HMSS data architecture represents the steps taken from raw data to user context
      • Adopted from previous work by Dey et al. and Gellersen
      • Used for simplifying feedback selection
      • All data sources expressed as sensor types
    13. Sensor Layer
      • Virtual Sensor
        • Source: User data (weight, gender, address etc)
      • Location Sensor
        • Source: A GPS receiver
      • External Physical State Sensor
        • Source: An accelerometer
    14. Cue Layer
      • Abstracts raw sensor data into cues, or abstractions
        • Example can be the output of a light bulb which is expressed as a number from 0-255 into high, medium and low
      • Five cues derived from source layer:
          • Speed (outdoor walking pace)
          • Closest building (closest building to user)
          • Indoor/outdoor (is user inside the building)
          • Activity, as determined by GT2k
          • Calories burnt
    15. Context Layer
      • Responsible for determining a users context by using cues to help minimize misidentification
      • Algorithms convert multiple cues into context at this layer, which is then used for feedback selection
      • Two contexts defined, with ability to add more
    16. Evaluation
      • Developed using all domains as a reference
      • Eight unescorted subjects participating in mobile field experiments
      • The first evaluation was a pilot, followed by further development and seven subsequent evaluations
      • Each evaluation held in three phases
        • Pre-experiment training
        • Experiment
        • Post-experiment interview
      • Subjects were associated with SISAT
      • Three questions asked in all evaluations:
        • What features you would like to see?
        • Would you like to use such a device all day?
        • Did you alter your behavior during the course based on feedback?
    17. Pilot
      • Held in March 2007
      • Instructions between three campus buildings, ACB, Sprague library and Hagelbarger’s lounge
      • Two activities were included for messages, walking and resting
      • Aimed at gathering data on technical and usability issues
      • Subject followed an instruction sheet, while carrying a smartphone, a GPS receiver, and wearing three accelerometers
      • Subject was female, in her twenties
      • Two sessions were held:
        • In first session, application crashed and session was reset
        • The second session proceeded normally
      • Subject was able to indicate via the smartphone whether the messages received were accurate or not
    18. Course
      • Go downstairs and exit the building from the southern exit
      • Once outside, walk towards the Sprague library
      • Enter the library from the southern entrance and climb up the stairs to the fifth floor
      • Sit on the chairs available on the fifth floor for 2-4 minutes
      • Go downstairs and leave the building and proceed to Hagelbarger’s restaurant at CGU.
      • Once inside the restaurant, sit inside for one
      • Leave the restaurant through the southern exit, and walk to the ACB building
      • Sit on a bench outside the northern entrance of ACB for 2-4 minutes
      • Enter ACB from the northern entrance, go up the stairs and sit on the couch for 2 minutes
    19. Pilot Results
      • Location did not update properly because the subject had put the GPS receiver in pant pocket
      • Subject received six messages
      • Subject found five of the messages inaccurate because of the GPS error
      • Subject indicated surprise that stair climbing was not recognized
      • Subject indicated less sensors were desirable
      • Resulted in several updates to system
    20. Second Evaluation Phase
      • Seven sessions held in June 2007
      • Reliability and stability
        • Session restart
        • Improved sensor data handling
      • Accelerometers
        • Reduced to one
      • Subject training
        • Error recovery
        • Each evaluation issue led to iterative improvements
      • Instructions
        • Automatically received on smartphone based on subject location and specified time elapse period
        • Goals were attached to activity choices; outdoor walking pace and stairs vs. elevator choice
        • Inclusion of the Olin Science Building at Harvey Mudd
        • Subject were asked to acknowledge the receipt of an instruction via a smartphone button, otherwise session was suspended
      • Packaging
        • Easier to replace batteries
        • Improvement in wiring and the addition of an external power switch
    21. Evaluation Overview
      • Seven subjects:
        • Five males, two females
        • Five in their twenties, one in forties, one in fifties
      • Experiment sessions lasted from 30 minutes to 1 hour
      • Each evaluation was remotely monitored by using the Google Maps API
        • A web page was created that had access to entire session data
        • Web page displayed subject location as well as feedback on a map in realtime
        • Allowed signaling of potential problems and correction notification to subject
    22. Course
    23. Evaluation #1
      • Subject was male in his twenties
      • Subject did not use stairs at Olin and Sprague, which was correctly identified by system
      • On the way back, subject used stairs at ACB, but only a small percentage of the sequence was correctly identified. Data delay transmission also played a factor in incorrect recognition
      • Interview revealed subject got lost inside Olin and had also thought Sprague Library stairs were off-limits
      • Subject training modified to emphasize architecture inside Olin and Sprague
    24. Evaluation #2
      • Subject was female in her twenties
      • Subject was briefed in more details about Olin and Sprague, but lost way inside Olin
      • Extra walking inside Olin caused incorrect feedback
      • Subject had to wait a long time outside Sprague as she was standing closer to Olin
      • Inside Sprague, subject used elevator to go upstairs, but used the stairs to go down when she received her feedback
      • At instruction #9, the session had to be aborted as incorrect GPS data caused the system to not send the next instruction
      • Course was modified slightly to make it easier to trigger instruction #9
    25. Evaluation #3
      • Subject was male in his twenties
      • Subject lost his way to the Olin building
      • Subject was guided back on course using text messaging to smartphone
      • Subject used stairs inside Olin and got correct feedback
      • Inside Sprague, subject was going to use stairs, but decided on sharing an elevator ride up
      • The subject received correct feedback at Sprague
      • At ACB, the subject’s stair walking sequence was correctly recognized
      • Exterior pictures of Olin were taken and shown to subjects to reduce chances of getting lost
    26. Evaluation #4
      • Subject was male in his twenties
      • Subject could not find stairs inside Olin, extra activity caused incorrect feedback
      • Subject had to wait a long time for instruction #6 as he was not close enough to Sprague
      • Climbed stairs very quickly at both Sprague and ACB, which caused incorrect feedback
      • Took a route for Sprague-Hagelbarger’s that was unanticipated, which caused delay in next instruction dispatch
      • The instructions were modified to require the subject to wait outside Hagelbarger’s for the next instruction before proceeding inside
      • Video clips were incorporated into training
    27. Evaluation #5
      • Subject was male in his twenties
      • This was the second session with this subject, the first one was aborted due to priority conflict
      • Subject used stairs in Olin, got correct feedback, but activity analysis showed low recognition of sequence
      • Subject had to wait a long time for instruction #6 because he was standing under a shade
      • Subject climbed stairs very quickly at Sprague which caused incorrect feedback
      • On way back, subject forgot to acknowledge instructions which caused the session to end
      • Subjects would now be escorted for a short while during training
      • Subjects would now be instructed to stand under open sky while waiting for a GPS fix
    28. Evaluation #6
      • Subject was male in his fifties
      • There was high data transmission delay, causing several disconnections
      • One disconnection caused by subject forgetting to acknowledge instruction
      • Subject became conscientious inside Olin, tried to find stairs, but turned around and used elevator The extra activity caused an incorrect feedback
      • Subject used elevator at Sprague, and got correct feedback
      • The subject used stairs at ACB, but did not get correct feedback because of physical parameters
    29. Evaluation #7
      • Subject was female in her forties
      • Session had no problems with getting lost, or losing server connection
      • Subject used elevator at Olin and got correct feedback
      • Outside Sprague, subject started conversing with someone, which caused some delay
      • Subject used elevator inside Sprague and got correct feedback
      • Subject used stairs inside ACB, which was correctly recognized
    30. Location Recognition Summary
      • Eight instances of some delay in all sessions because of:
        • Subject near structures e.g. sunshade
        • Subject not close enough to building to trigger next instruction
        • Subject placing GPS receiver in pockets
      • One session had to be aborted because location misrecognition prevented new instruction from triggering
    31. Activity Recognition Summary
      • Nine instances of stair climbing at different buildings
      • Out of nine instances of stair climbing, six were categorized as walking
      • Major reasons:
        • Subject running on stairs
        • Activity model based on one person
        • Single stair case used
      • More time needed to modify activity recognition capabilities
    32. Subject Interview Summary
      • What features you would like to see?
        • Comparison, gaming, privacy, discrete packaging, a larger pool of messages
      • Would you like to use such a device all day?
        • Both older subjects expressed worry about using device at work
        • Younger group enthusiastic about using the device in their daily activities
      • Did you alter your behavior during the course based on feedback?
        • Two subjects attributed their changed pace on way back from course because of the feedback they had received
        • One subject consciously tried to look for the stairs for way down when feedback was received
    33. Connectivity and Stability Summary
      • Eight instances of disconnection from server
        • Data transmission delay to server
        • Recoverable with session restart
        • Two instances where subject did not acknowledge new instruction. One was not recovered as he was not aware that session had closed
      • One instance of smartphone freezing
        • Session restarted
      • Connection stability affected by experiment time and location
        • Sessions taking place late afternoon had more disconnections than other timings
    34. Research Answers
      • How could activity and location information be captured?
        • Accelerometers and a GPS receiver placed on the user were adequate sources to capture this information
      • Can these sources be feasibly used for choosing feedback related to the health choices of a user?
        • The development and evaluation of HMSS has shown that raw data from these sources can be used to determine a user’s context, which in turn can help the system select an appropriate feedback
      • Could feedback from the system be effective in changing behavior?
        • The results showed mixed result in changing behavior. Also the results were demonstrated for a short term. The study did uncover a clue to demographic factors in acceptance of such a feedback mechanism. Further study is required
    35. Contributions
      • An understanding of user attitude towards real time health feedback devices
      • Knowledge gained about the subject and technical factors that can affect the outcome and results of mobile field experiments
      • An exploration of how customized real time reference to physical parameters such as activity and walking pace could affect short-term behavior
    36. Future Goals
      • Context-awareness
        • Increase activity and location recognition accuracy
        • Use more data for context recognition e.g. weather
        • Implement user definable feedback parameters
      • Increase feedback message variety and quantity
      • Reduce data transmission to server
      • Enable customized activity modeling
      • Reduce power consumption
      • Publication: Article submitted to IEEE Pervasive Computing
    37. Questions
    38. References
      • S. Mitchell, M. D. Spiteri, J. Bates, and G. Coulouris, "Context-aware multimedia computing in the intelligent hospital," Proceedings of the 9th workshop on ACM SIGOPS European workshop: beyond the PC: new challenges for the operating system , pp. 13-18,2000.
      • D. Siewiorek, A. Smailagic, J. Furukawa, A. Krause, N. Moraveji, K. Reiger, and J. Shaffer, "SenSay: a context-aware mobile phone," Wearable Computers, 2003.
      • Proceedings. Seventh IEEE International Symposium on , pp. 248-249, 2003.
      • A. K. Dey, "Understanding and Using Context," Personal and Ubiquitous Computing , vol. 5, no. 1, pp. 4-7, 2001.
      • H. W. Gellersen, A. Schmidt, and M. Beigl, "Adding Some Smartness to Devices and Everyday Things," Proceedings of IEEE Workshop on Mobile Computing Systems and Applications 2000 (WMCSAA 2000) , 2000.
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