Robotic hand design with linear actuators based on Toronto development
Bibliographic record
Abstract
In this work, the design of a robotic hand with 7 degrees of freedom is presented that allows greater flexibility, achieving the usual actions performed by a normal hand. The work consists of a prototype designed with linear actuators and myoelectric sensor, following the mechanism of the University of Toronto for the management of functional phalanges. The design, construction description, components and recommendations for the elaboration of a flexible and useful robotic hand for amputee patients with a residual limb for the socket are presented. Keywords: Robotic hand, Degree of freedom, Toronto´s Mechanism, lineal actuator. References [1]W. Diane, J. Braza and M. Yacub, Essentials of Physical Medicine and Rehabilitation, 4th ed. Philadelphia: Walter R. Frontera and Julie K. Silver and Thomas D. Rizzo, 2020, pp. 651 - 657. [2]A. Heerschop, C. Van Der Sluis, E. Otten, & R.M. Bongers, Looking beyond proportional control: The relevance of mode switching in learning to operate multi-articulating myoelectric upper-limb prostheses, . Biomedical Signal Processing and Control, 2020, doi:10.1016/j.bspc.2019.101647. [3]L. Heisnam, B. Suthar, 20 DOF robotic hand for tele-operation: — Design, simulation, control and accuracy test with leap motion. 2016 International Conference on Robotics and Automation for Humanitarian Applications (RAHA), 2016, doi:10.1109/raha.2016.7931886. [4]Y. Mishima, R. Ozawa, Design of a robotic finger using series gear chain mechanisms. 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014, doi:10.1109/iros.2014.6942961. [5]N. Dechev, W. Cleghorn, S. Naumann, Multi-segmented finger design of an experimental prosthetic hand,Proceedings of the Sixth National Applied Mechanisms & Robotics Conference, december 1999. [6]O. Flor, “Building a mobile robot,” Education for the future. Accessed on: December 29, 2019. [Online] Available: https://omarflor2014.wixsite.com/misitio. [7]Vargas, O., Flor,O., Suarez, F., Design of a robotic prototype of the hand and right forearm for prostheses, Universidad, Ciencia y Tecnología, 2019. [8]O. Vargas, O. Flor, F. Suarez, C. Chimbo, Construction and functional tests of a robotic prototype for human prostheses, Revista espirales, 2020. [9]P. PonPriya, E. Priya, Design and control of prosthetic hand using myoelectric signal. International Conference on Computing and Communications Technologies (ICCCT), 2017, doi:10.1109/iccct2.2017.7972314. [10]N. Bajaj, A. Spiers, A. Dollar, State of the Art in Artificial Wrists: A Review of Prosthetic and Robotic Wrist Design. IEEE Transactions on Robotics, 2019, doi:10.1109/tro.2018.2865890.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".