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Aefa

Aefa. Personal Exercise Assistant. Introduction. Team members: Justin Bumpus -Barnett Dmitri Musatkin Cilranus Thompson Sean Cline. Course Instructor:   Dr. Gursel Serpen Faculty Advisor:   Dr. Henry Ledgard. Presentation Contents. Background Discussion Problem Statement

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Aefa

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  1. Aefa Personal Exercise Assistant

  2. Introduction Team members: • Justin Bumpus-Barnett • Dmitri Musatkin • Cilranus Thompson • Sean Cline Course Instructor:   Dr. GurselSerpen Faculty Advisor:   Dr. Henry Ledgard

  3. Presentation Contents • Background • Discussion • Problem Statement • Solutions • Architecture • Design • Hardware • Motion Analysis • Social Networking • Database Abstraction • Video Demonstration • Conclusion • Questions

  4. Background Project Motivation: • Promote healthy lifestyle • Simplify exercise tracking • Join growing market of exercise software • Save users’ money and time

  5. Background • Relation to Coursework • Signal processing & Filtering • Hardware Interfacing • GUI building • Software Development • Database Design • Importance of Project • Promote weight loss  • Introduce exercise software on PC • Provide an inexpensive option for exercise management

  6. Discussion

  7. Problem Statement & Solution Problem • Track user exercise • Exercise analysis • Motivate user • Performance graphing • Usable with a variety of sensors • Sharing recorded data

  8. Problem Statement & Solution • Solution •  Social Networking • Twitter • Result & User Feedback • JfreeChart Solution • Design a Multi-Platform Application • Intuitive User Interface • Wii Remote • Accelerometer • Motion Detection Algorithms • Peak Counting • Storage Of Exercise Data • SQL Database • Plugin • Java Simple Plugin Framework

  9. Discussion - Architecture

  10. Discussion - Design • Plugin Management • Event Driven Design • Interface-based Design • Dependency Injection

  11. Discussion - Hardware WiiRemote: • ADXL330 accelerometer • Broadcom bluetoothdevice •  +/- 5g with 10% accuracy • Acceleration axes are relative to the device • Earth gravity is added to the measurements • Motion Plus to improve acceleration reading

  12. Discussion - Acceleration Data • Acceleration measuredin units of g • Exercise patterns are preserved in the acceleration data

  13. Discussion - Motion analysis • Algorithm based on published technical articles • Mean filter to smooth out the data • Adaptive thresholding • Dynamic precision • Time framing • Calories burned calculation

  14. Discussion - Social Networking • Share performance with friends • Motivate users by showing friends' performance

  15. Discussion - DAL • Database Abstraction Layer • Persistence of data between exercise sessions • Implementation independent method to store data • Separates code from data • Implemented using SQLite database

  16. Demonstration - Running

  17. Demonstration – Squats

  18. Demonstration – Jumping Jacks

  19. Conclusion Prospective Users: • Home Users • Retirement Homes • Exercise Gyms Future Possibilities: • More plugins • Compatibility with more devices • Better social networking connectivity • Facebook • Foursquare

  20. Q&A You've got questions... We've got blank stares.

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