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Real-Time Action Tracking System (RATS). Ryan Frazier and Brett Newlin . Brief Review. What is RATS? RATS is a system of sensors used to record statistical data and transmit this data wirelessly. How can it be used?

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Real-Time Action Tracking System(RATS)

Ryan Frazier and Brett Newlin

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Brief Review

  • What is RATS?

    • RATS is a system of sensors used to record statistical data and transmit this data wirelessly.

  • How can it be used?

    • By attaching our sensor board to any object you will be able to record the acceleration that object is feeling and you’ll be able to determine the rotation of that object.

  • Who is interested?

    • Extreme Sports athletes (skateboarders, snowboarders, etc…)

    • Extreme Sports Judges

    • Extreme Sports fans

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Related Work

  • RATS (Beta)– by Matt N. Nevitt, Kabir K. Shahani, Brandon S. Tengan, and Geoffrey R. Velasco from the Information School.

    • Origin of our design

    • Break-beam system used to determine height

    • iPaq and an accelerometer attached to underside of skateboard.

  • Killer App – Edward Chi from the Palo Alto Research Center

    • Sensor system used to determine clean hits in taekwondo.

    • Use of piezoelectric sensors instead of accelerometers

    • Wireless transmission of data to judges

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Demo – Play with the Sensor board

  • June 11th, 1:00 pm in CSE 003

    • The user will be able to move the senor board freely and watch the data packets appear on the screen. If Gaetano wants to he can skate around the lab in a chair and spin around to generate some interesting data.

    • After enough data has been completed we’ll be able to open up the data file in excel and view the data graphically and show you can determine what movements were made.

  • June 17th, Renton

    • Matt Nevitt from the I-School has rented an indoor skateboard facility to test his break-beam system on a professional sized halfpipe.

    • Our RATS sensor board will also be there to test and acquire data for Matt’s continued research.

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Implementation – Data Flow

  • Atmel Controller

    • Programmed Via JTAG

    • Gathers data from the Accelerometer (digital transfer)

    • Performs analog to digital conversion of gyroscope data.

    • Sends data to Mote via UART

  • Accelerometer

    • Generates values at 280Hz

    • ADC is on the chip so data being sent to Atmel is digital.

  • Gyroscope

    • Creates an analog out signal

    • Self Test pins used for calibration

  • Motes

    • Sender – gather data via UART and Send over Radio

    • Receiver – gather data via Radio and Send over UART

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  • Completed Tasks

    • PCBs designed and manufactured

    • Prototype board soldered for testing

    • TinyOS code completed

  • Still to come

    • Atmega16L programming to gather data

    • Front end

      • Data parser

      • Mathematical computations

      • Visualizer

  • Remaining Issues

    • Getting the accelerometers in time for the demo.

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  • Metrics

    • Throughput – how much data can we send in real time?

    • Power consumption – how long will our batteries last?

    • Ruggedness – is this going to break once we put it on a skateboard?

    • Precision and Accuracy – is the data useful?

    • Usability – does the sensor inhibit the athlete’s performance?

  • Data Collected so far?

    • Basic rotation and acceleration data at once every half second.

  • Conclusions

    • It works and has the capability to gather data but the maximum amount of data is unknown.

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Future Work

  • Software Capstone

    • There is an entire Embedded software capstone project available on our system

      • Try to maximize the amount of data sent in real time.

      • Increase functionality of Motes, Atmel, accelerometer and gyroscope.

    • Front End software

      • Mathematical and Statistical computation

      • Data visualizer

  • Hardware Evolution

    • By using smaller IC packaging (ball grid array parts) we could make the board even smaller.

    • Better gyroscope (current one is limited at 300 degrees per second which is too slow for snowboarding).

  • Matt Nevitt from the I-School will most likely be using our sensor board to continue his research.