1. High Impact Blow Inspection over a Reactive Mobile-Cloud Framework
Presentation by:
Eric L. Luster
Hong Wu

2. Project Introduction The state-of-the-art of this Project
Various systems that are designed to evaluate correlations between head acceleration measurements and concussions are in the early stages of research and development.
Automatically detect the impact between the athletes by using online machine learning method.
Design a more effective solution for delivering textual and imagery to mobile devices

3. Project Introduction
The Instrumented Football Helmet (IFH) is a standard regulation football helmet that is equipped with sensors that measure, record, and analyze impacts. Upon examining similar products and existing patents, there are two areas of potential infringement.
US Patent number , filed in June 11, 1997, which outlines a system designed to measure and record in real time data relating to translational and angular acceleration of an individual’s head during normal sporting activity.
[1] A. Camp, A. Boeckmann, M. Olson, K. Hughes, ECE 477 Final Report − Fall 2008 Team 2 − PHI-Master

4. Related Research
A team from Arizona State University work on state of the art wireless delivery methods for reporting in real-time head impacts and concussions
Simbex receives an NIH SBIR Phase II award to continue development of HitAlert™ technology to expand to enhance Simbex's product offerings in head impact biomechanics.
Simbex receives an NIH SBIR Phase I award for develop HitAlert™ - high schools and youth football programs

5. News By Riddell on Tuesday, August 10, 2010
Ruling Finds Schutt Infringed Riddell’s Concussion
Reduction Technology Patents
(CHICAGO, August 10, 2010) – A federal court jury in Madison, Wis., has found that Schutt Sports Inc.’s DNA and ION football helmets infringed the concussion reduction technology features of the Riddell Revolution family of football helmets. The jury awarded Riddell just under $30 million in damages for Schutt’s infringing activities.

7. Paper # 1 P. Viola and M. Jones, “Rapid Object Detection using a Boosted Cascade of Simple Features,”
Hong Wu

8. Background Motivation
P. Viola and M. Jones, “Rapid Object Detection using a Boosted Cascade of Simple Features,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Los Alamitos, CA, USA: IEEE Computer Society, 2001, p. 511.
Classical method in computer vision, cited over 4000 times
Motivation
Relation between computer vision, machine learning and mobile computing
Reduce the labor work of marking the samples.
Reduce the time used in training.

9. Method. - Data-driven training VS Intelligence-driven training
Method - Data-driven training VS Intelligence-driven training - General feature - Adaboost

10. Online VS Offline Adaboost
Online Training:
- Get the sample one by one
- Adaptive
- Not accurate in all cases
Offline Training:
- Get all the samples
at one time
Demo

11. Problem
Failure Case

12. Relation with the project
Automatically detect the impact with less supervision.
Assume that the athlete was tracked by a camera and an impact is a true alarm if the athlete is running and then fall down.
The concept of online machine learning can be used in other applications such as training an accelerator sensor to detect the gesture of a person by using heart rate sensor.
Training: Accelerator sensor + heart rate sensor
Testing: Accelerator sensor

13. Paper #2 Eric L. Luster Support for Mobile Access to DICOM Images Over Heterogeneous Radio Networks
I.Maglogiannis, G. Kormentzas, and T. Pliakas, Wavelet-Based Compression With ROI Coding Support for Mobile Access to DICOM Images Over Heterogeneous Radio Networks, IEEE Transaction on Information Technology in Biomedicine, vol. 13, no., 4 July 2009

14. Paper Background
The visual quality of the medical images/scans is required to be high, in order to ensure correct and efficient assessment resulting in correct diagnosis.
In this context, a mobile device has to handle medical images of significant sizes, while also taking into account its own limitations concerning memory and processing resources.

16. DLWIC
Useful when a user browses medical images using slow-bandwidth connections,
DLWIC uses the progressivism by stopping the coding when the quality of the reconstruction exceeds a threshold given as an input parameter to the algorithm.

17. Relevance to our Project
Application enhances the viewing of the following types of images on a mobile device:
Computed Tomography (CT) scans
Computed Radiography (CR) scans
Magnetic Resonance (MR) images
Stored in picture archiving and communication systems (PACS)
Hospital Information Systems (HIS)
Furthermore, the current medical image viewers do not take into consideration the special requirements and needs of an heterogeneous radio access environment composed of different radio access technologies [e.g., GPRS/UMTS, WLAN, and DVB-H).

18. Back-up Slides

19. Original Master Schedule
Old Schedule

20. Master Schedule

21. Tables & Figures