This paper introduces a novel approach for prosthetic wrist control, addressing limitations of traditional electromyography-based methods. While previous research has primarily focused on hand and gripper development, our study emphasizes the importance of wrist mobility for enhancing dexterity and manipulation skills. Leveraging a combination of visual data and inertial sensors, we proposed a system capable of estimating object orientation in real-time, enabling automatic and natural control of a prosthetic wrist. Our deep learning-based model can accurately interpret object posture from the user’s perspective, facilitating seamless wrist movement based on object inclination. In addition, Gaussian filtering was employed to mitigate noise in image-based posture estimation while preserving essential trends. Through this approach, users can achieve natural positioning without needing additional muscle movements, thus significantly improving prosthetic usability and user experience.
Hands perform various functions. There are many inconveniences in life without the use of hands. People without the use of hands wear prostheses. Recently, there have been many developments and studies about robotic prosthetic hands performing hand functions. Grasping motions of robotic prosthetic hands are integral in performing various functions. Grasping motions of robotic prosthetic hands are required recognition of grasping targets. A path toward using images to recognize grasping targets exists. In this study, object recognition in images for grasping motions are performed by using object detection based on deep-learning. A suitable model for the grasping motion was examined through three object detection models. Also, we present a method for selecting a grasping target when several objects are recognized. Additionally, it will be used for grasping control of robotic prosthetic hands in the future and possibly enable automatic control robotic prosthetic hands.
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A Study on Defect Detection Model of Bone Plates Using Multiple Filter CNN of Parallel Structure Song Yeon Lee, Yong Jeong Huh Journal of the Korean Society for Precision Engineering.2023; 40(9): 677. CrossRef
In this paper, a prosthetic robot hand was designed and fabricated and experimental evaluation of the realization of basic gripping motions was performed. As a first step, a robot finger was designed with same structural configuration of the human hand and the movement of the finger was evaluated via kinematic analysis. Electromyogram (EMG) signals for hand motions were measured using commercial wearable EMG sensors and classification of hand motions was achieved by applying the artificial neural network (ANN) algorithm. After training and testing for three kinds of gripping motions via ANN, it was observed that high classification accuracy can be obtained. A prototype of the proposed robot hand is manufactured through 3D printing and servomotors are included for position control of fingers. It was demonstrated that effective realization of gripping motions of the proposed prosthetic robot hand can be achieved by using EMG measurement and machine learning-based classification under a real-time environment.
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Development of a Caterpillar-Type Walker for the Elderly People Yeon-Kyun Lee, Chang-Min Yang, Sol Kim, Ji-Yong Jung, Jung-Ja Kim Applied Sciences.2021; 12(1): 383. CrossRef
Remote Control of Mobile Robot Using Electromyogram-based Hand Gesture Recognition Daun Lee, Jung Woo Sohn Transactions of the Korean Society for Noise and Vibration Engineering.2020; 30(5): 497. CrossRef
This paper proposes a myoelectric hand prosthesis with an easy control strategy to apply more conveniently with just two EMG sensors. The myoelectric hand prosthesis is composed of a multi-DOF finger mechanism, a controller, and an intuitive control algorithm. The developed hand prosthesis has 6-DOFs and can perform eight hand motions using the intuitive control algorithm. The proposed intuitive control algorithm classifies four grip motions and four gesture motions; we used the thumb position of the hand prosthesis and three EMG signals (Co-contraction, flexion, and extension) generated from the two EMG sensors. From the experimental results, we demonstrated that the proposed myoelectric hand prosthesis is applicable to amputees as a hand prosthesis.
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Development of Multifunctional Myoelectric Hand Prosthesis System with Easy and Effective Mode Change Control Method Based on the Thumb Position and State Sung-Yoon Jung, Seung-Gi Kim, Joo-Hyung Kim, Se-Hoon Park Applied Sciences.2021; 11(16): 7295. CrossRef
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Conventional prosthetic hands require users to activate designated muscles or press buttons to select among predefined grasping patterns. These methods are time-consuming and increase muscle fatigue. This study proposes a regression model that differentiates multiple muscle activation patterns allowing the user to select a desired grasping pattern. We classified four hand primitives and three force intensities, which can reflect the intention of prosthetic hand users. An 8-channel band-type sEMG sensor was used to measure myoelectric signals from an amputated upper-arm. To acquire the sEMG data, the amputee was instructed to imagine four hand primitives (fist, open hand, flexion, and extension) with three levels of force intensity (low, medium, and high). Time-domain features (mean average value, variance, waveform length, and root mean square) were extracted from the sEMG signal and classified using a Support Vector Machine. The hand primitives and force intensities had accuracies of 95% and 90%, respectively. Results indicate the regression model reflected the user’s intention to select different grasping patterns, and is thus expected to improve the quality of life of amputees.
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Continuous grip force estimation from surface electromyography using generalized regression neural network He Mao, Peng Fang, Yue Zheng, Lan Tian, Xiangxin Li, Pu Wang, Liang Peng, Guanglin Li Technology and Health Care.2023; 31(2): 675. CrossRef
Design of Prosthetic Robot Hand and Electromyography-Based Hand Motion Recognition Ho Myoung Jang, Jung Woo Sohn Journal of the Korean Society for Precision Engineering.2020; 37(5): 339. CrossRef
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