AefaPersonal 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• Solutions• Architecture• Design• Hardware• Motion Analysis• Social Networking• Database Abstraction

• Video Demonstration• Conclusion• Questions

Background

Project Motivation:

• Promote healthy lifestyle

• Simplify exercise tracking

• Join growing market of exercise software

• Save users’ money and time 

Background

Importance of Project

• Promote weight loss  • Introduce exercise

software on PC

• Provide an inexpensive option for exercise management

 

Relation to Coursework

• Signal processing & Filtering

• Hardware Interfacing

• GUI building

• Software Development

• Database Design

Discussion

Problem Statement & SolutionProblem

• Track user exercise

• Exercise analysis

• Motivate user

• Performance graphing

• Usable with a variety of sensors

• Sharing recorded data

Problem Statement & SolutionSolution• Design a Multi-Platform

Applicationo Intuitive User Interface

 • Wii Remote

o Accelerometer • Motion Detection Algorithms 

o  Peak Counting • Storage Of Exercise Data

o SQL Database • Plugino Java Simple Plugin

Framework

 

Solution•  Social Networking

o Twitter

• Result & User Feedback o JfreeChart

 

Discussion - Architecture

Discussion - Design

• Plugin Management

• Event Driven Design

• Interface-based Design 

• Dependency Injection

Discussion - Hardware

Wii Remote:

• ADXL330 accelerometer

• Broadcom bluetooth device

•  +/- 5g with 10% accuracy

• Acceleration axes are relative to the device

• Earth gravity is added to the measurements

• Motion Plus to improve acceleration reading

Discussion - Acceleration Data

• Acceleration measured in units of g

• Exercise patterns are preserved in the acceleration data

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

Discussion - Social Networking

• Share performance with friends

• Motivate users by showing friends' performance

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

Demonstration - Running

 

Demonstration – Squats

Demonstration – Jumping Jacks

Conclusion

Prospective Users:• Home Users• Retirement Homes• Exercise Gyms

 

 Future Possibilities:• More plugins• Compatibility with more devices• Better social networking connectivity

o Facebooko Foursquare

Q&A

You've got questions...We've got blank stares.