Transcript RCA: Real-Time Concussion Analyzer Timothy Coyle, Justin Kober, Scott Rosa, Kenneth Van Tassell Faculty Advisor: Prof.

RCA: Real-Time Concussion Analyzer
Timothy Coyle, Justin Kober, Scott Rosa, Kenneth Van Tassell
Faculty Advisor: Prof. Christopher Hollot
Abstract
Data Analysis & Risk
We introduce RCA (Real-Time Concussion Analyzer), a realtime 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
Data Analysis
Impact Data Collection
Data Processing
Power Supply
Processing
Player
DB
TX/RX
Impact
DB
Server
Sensors
User Interface
Settings
 |ai| is the measured acceleration for sensori, |H| is the
linear acceleration of the head’s center of mass due to
impact, αH and θH, are the spherical angles of the
impact’s location on the helmet:
 Real-time algorithm expresses the above as CX-A=0,
where:
TX/RX
Server
Interface
History
 From the least-squares solution of the above; i.e.,
X=(C’C)-1C’A, we obtain:
GUI
 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.
Results
Typical sensor graph. Impact at sensor 0, transmitted and interpolated data.
Helmet
Sensors
Processor & Battery
Impact
Vibrational response of helmet shell and skull
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
Acceptability
test
confirms
battery
life
>
5
hrs.
Statistical
processing.
analysis of system conducted from collection of 120
impacts. 95% of all errors in identifying the impact’s position
Android Device
are less than 30.4° and 33.3° in θH and αH respectively.
Analysis conducted for mapping of impact location only,
 Alert message shows magnitude, risk
since
risk
is
dependent
on
α
and
θ
.
H
H
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.
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.
Department of Electrical and Computer Engineering
ECE 415/ECE 416
SENIOR DESIGN PROJECT 2013
College of Engineering - University of Massachusetts Amherst
System Specifications
Specification
Goal
Actual
Weight
< 5% increase (102 g)
6.2% (120 g)
Range
25 m
30 m
Response Time
<2s
<5s
Battery Life
> 5 hr.
> 5 hr.
Cost
< $5000 per team
$5040
Power Consumption
<2W
1.39 W
Acceleration Range
+/- 70 g
+/- 84 g
Sensitivity
Detect only collisions
Triggers at 10 g
Durable Packaging
Stable & Waterproof
Stable & Water Resistant
Microcontroller Testing
Risk
 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.
Sensor Placement
 Risk equation derived by Rowson et al. 2012.
 RCA uses shifted version for a scaled prototype.
Sensor
Θi (deg)
αi (deg)
Sensitivity (mV/g)
0
0
20
27.6
1
-90
15
27.5
2
180
20
27.6
3
90
15
27.6
4
75
50
27.8
5
-69
50
27.6
 Individual sensitivity found experimentally.
 Sensor locations modeled around Virginia Tech
research.
Application
Impact Location
Mapping
 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.
Impact locations for
measurements
Ave H (g)
H StDev
Server
 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.
Cost
Part
Development
Production (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
$0.02
16 MHz Clock
$0.91
$0.53
Battery
$19.95
$15.96
Total
$123.41
$87.68
Sensor 0 Sensor 1 Sensor 2 Sensor 3 Sensor 4 Sensor 5
(θ=0,
(θ=-90, (θ=180, (θ=90, (θ=75, (θ=-69,
α=20)
α=15)
α=20)
α=15)
α=50)
α=50)
37.16
44.04
59.30
30.92
41.99
43.95
1.52
9.35
1.56
5.96
8.66
4.97
Ave θ (deg)
θ StDev
0.50
0.29
-103.84
4.56
179.84
0.15
107.49
2.28
104.48
1.33
-94.73
4.57
Ave α (deg)
α StDev
8.40
1.78
41.31
5.92
30.47
0.36
39.74
10.25
57.66
2.31
53.41
4.73
Ave θ Difference (deg)
Ave α Difference (deg)
0.5
11.6
13.8
26.3
0.2
10.8
17.5
24.7
29.5
7.7
25.7
3.4
 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.