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
High Impact Blow Inspection over a Reactive Mobile-Cloud Framework
Eric L. Luster
 A. Camp, A. Boeckmann, M. Olson, K. Hughes, ECE 477 Final Report − Fall 2008 Team 2 − PHI-Master
By Riddell on Tuesday, August 10, 2010
Ruling Finds Schutt Infringed Riddell’s Concussion
Reduction Technology Patents
Paper # 1P. Viola and M. Jones, “Rapid Object Detection using a Boosted Cascade of Simple Features,”
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
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- General feature- Adaboost
Online VS Offline Adaboost
- Get the sample one by one
- Not accurate in all cases
- Get all the samples
at one time
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