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Hassle Free Fitness Monitoring

Hassle Free Fitness Monitoring. David Jea, Jason Liu, Thomas Schmid, Mani Srivastava. Pervasive Health Care Systems. Fitness Monitoring is the most Fundamental Functionality of Pervasive Health Care Systems Provides 24X7 Fitness Monitoring Sensor devices are clipped on the body

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Hassle Free Fitness Monitoring

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  1. Hassle Free Fitness Monitoring David Jea, Jason Liu, Thomas Schmid, Mani Srivastava

  2. Pervasive Health Care Systems • Fitness Monitoring is the most Fundamental Functionality of Pervasive Health Care Systems • Provides 24X7 Fitness Monitoring • Sensor devices are clipped on the body • Proactively Record changes in vital signs such as weight and blood pressure • Appropriate Medical Services provided on the basis of recorded data

  3. Challenges • Privacy • Security • Finding a perfect balance between usability, privacy and security

  4. Problems • Large number of Devices hooked on the body • Multiple type of sensors • Privacy concerns at workplaces

  5. Security Issues • Network Security Issues • User authentication issues • Security problems related to stolen palmtops or PDA’s

  6. The Idea • Build Fitness monitoring system for healthy individuals in a workplace • Identification of the individual by only utilizing imprecise biometrics and existing information • Maintaining the device’s original user interface • No additional sensors incorporated in the system

  7. Design Guidelines • Privacy • Recorded data cannot be used as hard evidence (in court) to pinpoint exactly who the user is • Feasibility • The system is allowed to use existing information • Usability • Restoring the original interface of the device so that people of all age groups know how to use it

  8. The Design Possible Candidates Imprecise Physiological Info Biometric Matcher Uncertainty Reduction Context Reasoning User Identity Activity Information

  9. Implementation • The system consists of a weight scale and a blood pressure monitor • Both devices communicate with the laptop • Software program installed on laptop continuously record data and attach a timestamp to weight and blood pressure readings • Facility for a user to input his/her name is also provided • This step is to establish ground truth for the experiment

  10. Inference Engine Components • Biometric Matcher • It implements a Bayes classifier that combines multiple sensor observations • It assumes that each observation is unique • This results in the identity of the subject • Context Reasoning • Itis based on Reified Temporal Logic • It provides with the user’s context • It uses two meta-Predicates to express when things are true

  11. Analysis

  12. Results for one Physiological Information

  13. Results based on Multiple Sources

  14. The accuracy of the context reasoning component

  15. Combining the biometric matcher with the context reasoning.

  16. Conclusion • Built a health monitoring system which is hassle free • Less privacy concerns • No extra sensors hooked on the body • Easy to Use • Widely used by population • How to handle uncertain usage?

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