Machine-Learning-Based Muscle Control of a 3D-Printed Bionic Arm
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Implementation assessment
Scoring by criterion
Scores describe accessibility and implementation characteristics reported in the reviewed source. They are not a measure of scientific quality.
Resolution
N/APaper is outside the rubric's bioprinting/liquid-handling/microfabrication types.
Scalability/Throughput
N/APaper is outside the rubric's bioprinting/liquid-handling/microfabrication types.
Build and Part Sourcing Complexity
N/APaper is outside the rubric's bioprinting/liquid-handling/microfabrication types.
Skill Complexity
N/APaper is outside the rubric's bioprinting/liquid-handling/microfabrication types.
Equipment/Cosumable/Facility Requirement Accessibility
N/APaper is outside the rubric's bioprinting/liquid-handling/microfabrication types.
Application Level
N/APaper is outside the rubric's bioprinting/liquid-handling/microfabrication types.
Accessibility to documentation
N/APaper is outside the rubric's bioprinting/liquid-handling/microfabrication types.
Validation/Troubleshooting Complexity
N/APaper is outside the rubric's bioprinting/liquid-handling/microfabrication types.
Speed/Cycle Time
N/APaper is outside the rubric's bioprinting/liquid-handling/microfabrication types.
Build Time
N/APaper is outside the rubric's bioprinting/liquid-handling/microfabrication types.
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Abstract
In this paper, a customizable wearable 3D-printed bionic arm is designed, fabricated, and optimized for a right arm amputee. An experimental test has been conducted for the user, where control of the artificial bionic hand is accomplished successfully using surface electromyography (sEMG) signals acquired by a multi-channel wearable armband. The 3D-printed bionic arm was designed for the low cost of 295 USD, and was lightweight at 428 g. To facilitate a generic control of the bionic arm, sEMG data were collected for a set of gestures (fist, spread fingers, wave-in, wave-out) from a wide range of participants. The collected data were processed and features related to the gestures were extracted for the purpose of training a classifier. In this study, several classifiers based on neural networks, support vector machine, and decision trees were constructed, trained, and statistically compared. The support vector machine classifier was found to exhibit an 89.93% success rate. Real-time testing of the bionic arm with the optimum classifier is demonstrated.
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