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Related Concept Videos

Muscles that Move the Arm01:31

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Nine muscles are involved in arm movements. Two of these, the pectoralis major and latissimus dorsi, originate from the axial skeleton and are called axial muscles. The other seven originate from the scapula and are called the scapular muscles.
The pectoralis major has two origins. Its clavicular head originates on the medial half of the clavicle. In contrast, the sternocostal head originates on the costal cartilages of ribs 1-6, the sternum, and the aponeurosis of the external oblique of the...
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Machine-Learning-Based Muscle Control of a 3D-Printed Bionic Arm.

Sherif Said1,2, Ilyas Boulkaibet1, Murtaza Sheikh1

  • 1College of Engineering and Technology, American University of the Middle East, Al-Eqaila 54200, Kuwait.

Sensors (Basel, Switzerland)
|June 6, 2020
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Summary
This summary is machine-generated.

This study presents a low-cost, 3D-printed bionic arm controlled by surface electromyography (sEMG) signals. A support vector machine classifier achieved an 89.93% success rate for gesture recognition in real-time testing.

Keywords:
Myo armbandbionic armgesturemachine learningprostheticrecognitionrobotics

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Area of Science:

  • Biomedical Engineering
  • Robotics
  • Rehabilitation Technology

Background:

  • Developing advanced prosthetic limbs is crucial for improving the quality of life for amputees.
  • Surface electromyography (sEMG) offers a non-invasive method for controlling prosthetic devices.
  • Customizable and affordable bionic solutions are needed to increase accessibility.

Purpose of the Study:

  • To design, fabricate, and optimize a customizable, 3D-printed bionic arm for a right arm amputee.
  • To evaluate the effectiveness of sEMG signals for controlling the bionic hand.
  • To compare the performance of different machine learning classifiers for gesture recognition.

Main Methods:

  • A wearable 3D-printed bionic arm was designed and fabricated at a low cost ($295 USD) and weight (428 g).
  • Multi-channel sEMG signals were acquired from participants performing various gestures (fist, spread fingers, wave-in, wave-out).
  • Feature extraction and statistical comparison of classifiers including neural networks, support vector machine (SVM), and decision trees were performed.

Main Results:

  • The developed bionic arm successfully demonstrated real-time control using sEMG signals.
  • The support vector machine classifier achieved the highest success rate of 89.93% in recognizing gestures.
  • The system was optimized for a specific user, showcasing its customizable nature.

Conclusions:

  • A cost-effective and lightweight 3D-printed bionic arm can be effectively controlled using sEMG signals.
  • Machine learning, particularly SVM, provides a robust method for interpreting sEMG data for prosthetic control.
  • This technology holds promise for enhancing the functionality and accessibility of upper-limb prosthetics.