Force Sensor-Controlled Prosthetics Research – FINAL RESULTS AND DOCUMENTS
It’s been quite a while since I finished up with my Prosthetics Research and the Intel Science Talent Search, so I decided it was time for me to get all of my final materials online. Below I’ve included my final research paper, two posters, a powerpoint, and a short slideshow of the prototype progression that I played at my booth at Intel STS. Just for fun, I’ve put up some select pictures from Intel as well. Please feel free to comment and ask any questions you like.
I am issuing my work under a cc license which allows you to view and share it freely under the conditions outlined by Creative Commons:
Using Force Sensors to Effectively Control a Below-Elbow Intelligent Prosthetic Device by Jeremy E. Blum is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Download the 20 page Final Research Paper (PDF)
Download the 20×30 Summary Poster (PDF)
Download the 48×48 Presentation Poster (PDF)
Download the 30 slide PowerPoint (PDF)
Watch the “Prototype Progression” video that played at my Intel Booth:
View some of the pictures from Intel STS:
Research Poster
2008 Intel Science Talent Search Poster
Click the thumbnail to view a full-size PDF
Research Abstract
There are multiple problems associated with myoelectric control, currently the most popular form of prosthetic control. Myoelectrodes are expensive, require extensive processing to remove noise, must sometimes be implanted to receive the best signal, and often receive a noisy signal when used externally. One out of every twenty times, myoelectrodes inaccurately predict muscle bulge. Force sensors, a new control method being tested in this project, measure muscle bulge directly, rather than the electricity produced by the muscle. Force sensors are inexpensive, require little or no signal processing, and are used externally. To test this control method, an operational prosthetic hand prototype was built. MATLAB programming language was employed to write a program that could take readings through the computer, from both myoelectrodes and force sensors, and compare their accuracy. The program used Linear Discriminant Analysis to analyze the input voltages and convert them into a signal that would be capable of commanding movement for a given degree of freedom in a prosthetic device. Results show that force sensors can accurately differentiate between different forearm muscles with little training, indicating that in the future they could provide a low-cost, low-maintenance control method for amputees. Research was supported by Mu Alpha Theta.
Comments are off for this postFinalist in the Intel Science Talent Search
I was notified today that the work I submitted to the Intel Science Talent Search earned me a position as one of 40 finalists. Over 1,600 students entered their research into the competition from 45 states. I will attend a finalist week in Washington DC in march, and be given the opportunity to present my research to scientists and the public. You can learn more about the competition and read the press release HERE.
Comments are off for this postMu Alpha Theta Honorary Student Speech
The speech I gave at this years induction ceremony for Mu Alpha Theta Members:
Prosthetic Prototype #7
Complete Force Activation, LED Readout, Slip Detection/Slip Arrest, and Amplification for Cast Control
Prosthetic Prototype #6
Complete Force Activation, LED Readout, and Slip Detection/Slip Arrest