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

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

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 5978972, 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

Related Research 2010 - A team from Arizona State University work on state of the art wireless delivery methods for reporting in real-time head impacts and concussions 2010 - 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. 2009 - Simbex receives an NIH SBIR Phase I award for develop HitAlert™ - high schools and youth football programs

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.

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

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.

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

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 http://www.youtube.com/watch?v=0tSxMmAngs8&feature=player_embedded

Problem Failure Case http://www.youtube.com/watch?v=3AnWc5J9968&NR=1

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

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

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.

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.

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).

Back-up Slides

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