• Fashion & Textile Research Journal
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• v.24 no.3
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• pp.333-345
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• 2022

This study developed and evaluated the motion control of 3D printed fingers applied to smart gloves. Four motions were programmed by assembling the module using the Arduino program: cylindrical grasping, spherical grasping, tip-to-tip pinch gripping, and three-jaw pinch gripping. Cap and re-entrant (RE) strip types were designed to model the finger. Two types of modeling were printed using filaments of thermoplastic elastomer (TPE) and thermoplastic polyurethane (TPU). The prepared samples were evaluated using three types of pens for cylidrical grasping, three types of balls for spherical grasping, and two types of cards for tip-to-tip pinch gripping and three-jaw pinch gripping. The motion control of fingers was connected using five servo motors to the number of each control board. Cylindrical and spherical grasping were moved by controlling the fingers at 180° and 150°, respectively. Pinch gripping was controlled using a tip-to-tip pinch motion controlled by the thumb at 30° and index-middle at 0° besides a three-jaw pinch motion controlled by the thumb-index finger-middle at 30°, 0°, and 0°, respectively. As a result of the functional evaluation, the TPE of 3D-printed fingers was more flexible than those of TPU. RE strip type of 3D-printed fingers was more suitable for the motion control of fingers than the 3D-printed finger.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.19-23
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• 2024

Patients with amputations of both hands need prosthetic hands that serve both cosmetic and functional purposes, and research on prosthetic hands using electromyography of remaining muscles is active, but there is still the problem of high cost. In this study, an artificial prosthetic hand was manufactured and its performance was evaluated using low-cost parts and software such as a surface electromyography sensor, machine learning software Edge Impulse, Arduino Nano 33 BLE, and 3D printing. Using signals acquired with surface electromyography sensors and subjected to digital signal processing through Edge Impulse, the flexing movement signals of each finger were transmitted to the fingers of the prosthetic hand model through training to determine the type of finger movement using machine learning. When the digital signal processing conditions were set to a notch filter of 60 Hz, a bandpass filter of 10-300 Hz, and a sampling frequency of 1,000 Hz, the accuracy of machine learning was the highest at 82.1%. The possibility of being confused between each finger flexion movement was highest for the ring finger, with a 44.7% chance of being confused with the movement of the index finger. More research is needed to successfully develop a low-cost prosthetic hand.