 |a i | is the measured acceleration for sensor i, |H| is the linear acceleration of the head’s center of mass due to impact, α H and θ H, are the spherical angles of the impact’s location on the helmet: Department of Electrical and Computer Engineering ECE 415/ECE 416 SENIOR DESIGN PROJECT 2013 College of Engineering - University of Massachusetts Amherst Helmet RCA: Real-Time Concussion Analyzer Timothy Coyle, Justin Kober, Scott Rosa, Kenneth Van Tassell Faculty Advisor: Prof. Christopher Hollot Abstract We introduce RCA (Real-Time Concussion Analyzer), a real- time system that will allow a football coach to remotely monitor the impacts a player experiences during a game. This system will provide the likelihood that a player has experienced a concussion, allowing coaches to make more informed decisions pertaining to player safety. RCA incorporates an array of accelerometers inside each player’s helmet. The sensor data from each helmet is wirelessly transmitted to an Android device, where an application will query a player database on a server, and determine the likelihood of concussion. System Block Diagram  Helmet: Six single-axis accelerometers, microcontroller, Bluetooth module, battery.  Android Device: Communication to helmet and server, data processing, alerts & user interface.  Server: Store impact data and player history. Sensors Processor & Battery Acknowledgements Special thanks to Professor Christopher Hollot. Thank you to professors Christopher Salthouse, Dennis Goeckel, Marco Duarte, and William Leonard. Thank you to both Holyoke Catholic High school and the University of Massachusetts Amherst Athletics Department. Also thanks to Fran Caron, and Professor Steven Rowson of Virginia Tech. Data Analysis & Risk  Real-time algorithm expresses the above as CX-A=0, where:  From the least-squares solution of the above; i.e., X=(C’C) -1 C’A, we obtain: Results Typical sensor graph. Impact at sensor 0, transmitted and interpolated data. Impact Acceptability test confirms battery life > 5 hrs. Statistical analysis of system conducted from collection of 120 impacts. 95% of all errors in identifying the impact’s position are less than 30.4° and 33.3° in θ H and α H respectively. Analysis conducted for mapping of impact location only, since risk is dependent on α H and θ H. Six single-axis accelerometers measure the forces acting on the helmet. A microcontroller samples sensors at 1.5 kHz, and records data after 10 g threshold trigger. Data is transmitted to the Android device, via Bluetooth, for processing.  Alert message shows magnitude, risk and location of impact.  Six individual acceleration vs. time graphs.  Impact histogram showing frequency and intensity.  Data stored to the server for later use.  Bluetooth communication with helmet, WiFi/3G/LTE communication with server. Android Device Vibrational response of helmet shell and skull Data Processing Server Player DB Impact DB GUI TX/RX Settings History Server Interface Impact Data Collection Data Analysis User Interface Power Supply Sensors Processing TX/RX

 RCA samples at 1.5 kHz, almost double the Nyquist rate of the accelerometer’s bandwidth.  Accuracy verified with function generator.  Quantization error of 0.7 g. Cost System SpecificationsMicrocontroller Testing Risk Impact Location Mapping SpecificationGoalActual Weight< 5% increase (102 g)6.2% (120 g) Range25 m30 m Response Time< 2 s< 5 s Battery Life> 5 hr. Cost< $5000 per team$5040 Power Consumption< 2 W1.39 W Acceleration Range+/- 70 g+/- 84 g SensitivityDetect only collisionsTriggers at 10 g Durable PackagingStable & WaterproofStable & Water Resistant PartDevelopmentProduction (1000) 6x ADXL193 (Accelerometers)Free Sample$6.21 ATMEGA32U4 (Processor)Free Sample$6.04 BlueSMiRF (Bluetooth)$64.95$51.96 PCB$33$5.50 Capacitors$4.40$1.46 Resistors$0.20$ MHz Clock$0.91$0.53 Battery$19.95$15.96 Total$123.41$87.68  Risk equation derived by Rowson et al  RCA uses shifted version for a scaled prototype. SensorΘ i (deg)α i (deg)Sensitivity (mV/g) Sensor Placement Impact locations for measurements Sensor 0 (θ=0, α=20) Sensor 1 (θ=-90, α=15) Sensor 2 (θ=180, α=20) Sensor 3 (θ=90, α=15) Sensor 4 (θ=75, α=50) Sensor 5 (θ=-69, α=50) Ave H (g) H StDev Ave θ (deg) θ StDev Ave α (deg) α StDev Ave θ Difference (deg) Ave α Difference (deg)  160 impacts, with small standard deviation, show consistency in experiments.  10 impacts along each sensor’s axis, normal to the surface of the skull.  Errors in sensor placement, estimated at +/- 5, and variance of impact location during testing.  Experiments proved vital to testing location mapping algorithm.  Individual sensitivity found experimentally.  Sensor locations modeled around Virginia Tech research.  Android application interprets incoming Bluetooth data.  A detected impact prompts a dialog to alert the user with risk of injury and location of impact.  The Player Details activity returns information for each player and shows cumulative risk.  Histogram activity displays risk over time sorting them by risk percentage.  Settings allow user to pick between coach and trainer views and the histogram’s time frame to graph.  MySQL database controlled by PHP scripts.  Database stores raw accelerometer data, resultant hit vectors and player information.  Data accessible, via internet, for all who need data. Application Server