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| Content Provider | ACM Digital Library |
|---|---|
| Author | Menychtas, Dimitrios Dubey, Rajiv Carey, Stephanie L. |
| Abstract | Prosthesis prescription and training for the upper limb is based on the experience of the prosthetist and user feedback. As a result, many users may receive a less than optimal prosthetic device. Even though the need for a prosthesis does not change, failure of the prosthetic device to meet expectations can cause users to abandon their prosthesis. Further optimization of a prosthesis is often completed using trial and error. Currently, there is no universally accepted way to quantify the fitting and performance of the prosthesis. In order to address that issue a simulation algorithm is being developed at the University of South Florida (USF) in an effort to quantify and evaluate the performance of both the user and the prosthesis. The algorithm uses motion capture data to generate a statistical prediction of the expected performance of the user. Currently, the algorithm can be used for able-bodied subjects and is being optimized for left side transradial amputees. In this paper an update on the state of the algorithm is presented. |
| Starting Page | 1 |
| Ending Page | 6 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781450334525 |
| DOI | 10.1145/2769493.2769594 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-07-01 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Human model Upper body movement Simulation Prediction algorithm Weighted least norm |
| Content Type | Text |
| Resource Type | Article |
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