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Actuators
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Intelligent Robotic and Prosthetic Hands: Design, Control and Application
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Intelligent Robotic and Prosthetic Hands: Design, Control and Application

A special issue of Actuators (ISSN 2076-0825). This special issue belongs to the section "Actuators for Robotics".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 14953

Special Issue Editors

Dr. Duk Shin

E-Mail Website
Guest Editor
Tokyo Polytechnic University, Kanagawa, Japan
Interests: brain-machine interface; brain-computer interface; human computer interface
Special Issues, Collections and Topics in MDPI journals
Dr. Chao Chen

E-Mail Website
Guest Editor
Tianjin University of Technology, Tianjin, China
Interests: brain-computer interface; machine learning; neurofeedback system

Special Issue Information

Dear Colleagues,

Prosthetic hands are artificial extensions that help people who have lost their hands or arms to regain normal activity. Intelligent robotic and prosthetic hands aim to imitate the various human-like operations of moving, grasping, lifting, and so on. Over the last several decades, attempts have been made to build intelligent robotic and prosthetic hands. However, even though prosthetic hands are pivotal to mitigating the effects of disability, including activity-related difficulties and health-related quality of life, many arm amputees rely on old, less capable devices. Arm amputees have several expectations when it comes to prosthetic arms, the main being that their design should be as close as possible to that of a natural hand. Other requirements include alternative control methods with muscle-like flexibility for the acquisition and processing of biological signals, and innovative applications with new sensing technology such as IoT/5G.

Replicating the human hand in all its various functions is still a challenging task because of its extreme complexity. Therefore, this calls for more methods based on the acquisition and processing of biological signals. Also relevant are advances in neural signal acquisition, computational decoding and encoding methods of neural/biological signals, and computer and robotic technologies for robot hands. All these areas have the potential to provide a standard for supporting robotic/prosthetic hand strategies.

We invite investigators to contribute original research articles and review articles addressing robotic/prosthetic hands that facilitate advances in rehabilitation, such as brain–machine interfaces, neuroprosthetics, rehabilitation robots, and human support robots.

Potential topics include but are not limited to:

  • New design as close to the natural hand as possible;
  • Control methods for motor or sensory function;
  • Neuroprosthetics and rehabilitation systems;
  • Assistive technologies for stroke/old patients;
  • Personalized rehabilitation interfaces for adapted physical activity;
  • New sensors and actuator techniques.

Dr. Duk Shin
Dr. Chao Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Actuators is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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Research

17 pages, 22513 KiB  
Article
Design of a Movable Tensegrity Arm with Springs Modeling an Upper and Lower Arm
by Kihiro Kawahara, Duk Shin and Yuta Ogai
Actuators 2023, 12(1), 18; https://doi.org/10.3390/act12010018 - 31 Dec 2022
Cited by 1 | Viewed by 2093
Abstract
Tensegrity is a structure consisting of rigid bodies and internal tensile members, with no contact between the rigid bodies. The model of an arm with a tensegrity structure is not movable as it is, but we believe that it can be made movable [...] Read more.
Tensegrity is a structure consisting of rigid bodies and internal tensile members, with no contact between the rigid bodies. The model of an arm with a tensegrity structure is not movable as it is, but we believe that it can be made movable and flexible by incorporating springs. We developed an arm that incorporates springs in the arm’s tensile members by extending the model of an arm with a tensegrity structure. Then, as an evaluation of the developed arm, we measured the ranges of motions and the forces required for that motion. We also developed a mechanism that allows the arm to bend and extend. We believe that this method of making the tensegrity arm controllable by incorporating springs will be useful in the development of flexible robotic arms for caregiving using robots and other applications. Full article
(This article belongs to the Special Issue Intelligent Robotic and Prosthetic Hands: Design, Control and Application)
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14 pages, 5924 KiB  
Article
A Design of Biomimetic Prosthetic Hand
by Sakura Narumi, Xiansong Huang, Jongho Lee, Hiroyuki Kambara, Yousun Kang and Duk Shin
Actuators 2022, 11(6), 167; https://doi.org/10.3390/act11060167 - 16 Jun 2022
Cited by 7 | Viewed by 3997
Abstract
Many patients with upper limb defects desire myoelectric prosthetic hands, but they are still not used for some reasons. One of the most significant reasons is its external appearance, which has the discomfort caused by the structural difference between a human hand and [...] Read more.
Many patients with upper limb defects desire myoelectric prosthetic hands, but they are still not used for some reasons. One of the most significant reasons is its external appearance, which has the discomfort caused by the structural difference between a human hand and a robotic link. The structure must be based on human anatomy to create a more natural-looking prosthesis. This study designed a biomimetic prosthetic hand with bones, ligaments, tendons, and multiple muscles based on the human musculoskeletal system. We verified the proposed prosthetic hand using the viscoelastic angle sensor to determine whether it works like a human hand. We also compared the finger force of the prosthetic hand with that of a human finger. It could be capable of controlling the angle and the stiffness of the joint by multiple extensor and flexor muscles, like humans. Full article
(This article belongs to the Special Issue Intelligent Robotic and Prosthetic Hands: Design, Control and Application)
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17 pages, 2911 KiB  
Article
Body Calibration: Automatic Inter-Task Mapping between Multi-Legged Robots with Different Embodiments in Transfer Reinforcement Learning
by Satoru Ikeda, Hitoshi Kono, Kaori Watanabe and Hidekazu Suzuki
Actuators 2022, 11(5), 140; https://doi.org/10.3390/act11050140 - 21 May 2022
Cited by 1 | Viewed by 1994
Abstract
Machine learning algorithms are effective in realizing the programming of robots that behave autonomously for various tasks. For example, reinforcement learning (RL) does not require supervision or data sets; the RL agent explores solutions by itself. However, RL requires a long learning time, [...] Read more.
Machine learning algorithms are effective in realizing the programming of robots that behave autonomously for various tasks. For example, reinforcement learning (RL) does not require supervision or data sets; the RL agent explores solutions by itself. However, RL requires a long learning time, particularly for actual robot learning situations. Transfer learning (TL) in RL has been proposed to address this limitation. TL realizes fast adaptation and decreases the problem-solving time by utilizing the knowledge of the policy, value function, and Q-function from RL. Taylor proposed TL using inter-task mapping that defines the correspondence between the state and action between the source and target domains. Inter-task mapping is defined based on human intuition and experience; therefore, the effect of TL may not be obtained. The difference in robot shapes for TL is similar to the cognition in the modification of human body composition, and automatic inter-task mapping can be performed by referring to the body representation that is assumed to be stored in the human brain. In this paper, body calibration is proposed, which refers to the physical expression in the human brain. It realizes automatic inter-task mapping by acquiring data modeled on a body diagram that illustrates human body composition and posture. The proposed method is evaluated in a TL situation from a computer simulation of RL to actual robot control with a multi-legged robot. Full article
(This article belongs to the Special Issue Intelligent Robotic and Prosthetic Hands: Design, Control and Application)
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13 pages, 3139 KiB  
Article
A Portable Non-Contact Tremor Vibration Measurement and Classification Apparatus
by Mohd Zarhamdy Md Zain, Ali Zolfagharian, Moslem Mohammadi, Mahdi Bodaghi, Abd Rahim Abu Bakar and Abbas Z. Kouzani
Actuators 2022, 11(1), 26; https://doi.org/10.3390/act11010026 - 17 Jan 2022
Cited by 1 | Viewed by 3067
Abstract
Tremors are the most common type of movement disorder and affect the lives of those experiencing them. The efficacy of tremor therapies varies according to the aetiology of the tremor and its correct diagnosis. This study develops a portable measurement device capable of [...] Read more.
Tremors are the most common type of movement disorder and affect the lives of those experiencing them. The efficacy of tremor therapies varies according to the aetiology of the tremor and its correct diagnosis. This study develops a portable measurement device capable of non-contact measurement of the tremor, which could assist in tremor diagnosis and classification. The performance of this device was assessed through a validation process using a shaker at a controlled frequency to measure human tremors, and the device was able to measure vibrations of 50 Hz accurately, which is more than twice the frequency of tremors produced by humans. Then, the device is tested to measure the tremors for two different activation conditions: rest and postural, for both hand and leg. The measured non-contact tremor vibration data successfully led to tremor classification in the subjects already diagnosed using a contact accelerometer. Full article
(This article belongs to the Special Issue Intelligent Robotic and Prosthetic Hands: Design, Control and Application)
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14 pages, 7502 KiB  
Article
Event-Based, Intermittent, Discrete Adaptive Control for Speed Regulation of Artificial Legs
by Salvador Echeveste, Ernesto Hernandez-Hinojosa and Pranav A. Bhounsule
Actuators 2021, 10(10), 264; https://doi.org/10.3390/act10100264 - 12 Oct 2021
Viewed by 1528
Abstract
For artificial legs that are used in legged robots, exoskeletons, and prostheses, it suffices to achieve velocity regulation at a few key instants of swing rather than tight trajectory tracking. Here, we advertise an event-based, intermittent, discrete controller to enable set-point regulation for [...] Read more.
For artificial legs that are used in legged robots, exoskeletons, and prostheses, it suffices to achieve velocity regulation at a few key instants of swing rather than tight trajectory tracking. Here, we advertise an event-based, intermittent, discrete controller to enable set-point regulation for problems that are traditionally posed as trajectory following. We measure the system state at prior-chosen instants known as events (e.g., vertically downward position), and we turn on the controller intermittently based on the regulation errors at the set point. The controller is truly discrete, as these measurements and controls occur at the time scale of the system to be controlled. To enable set-point regulation in the presence of uncertainty, we use the errors to tune the model parameters. We demonstrate the method in the velocity control of an artificial leg, a simple pendulum, with up to 50% mass uncertainty. Starting with a 100% regulation error, we achieve velocity regulation of up to 10% in about five swings with only one measurement per swing. Full article
(This article belongs to the Special Issue Intelligent Robotic and Prosthetic Hands: Design, Control and Application)
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