A Design of Biomimetic Prosthetic Hand

Narumi, Sakura; Huang, Xiansong; Lee, Jong-Ho; Kambara, Hiroyuki; Kang, Yousun; Shin, Duk

doi:10.3390/act11060167

Cited by 9 publications

(5 citation statements)

References 28 publications

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“…When the tendon is in a relaxed state, tendon BE in the coupled linkage mechanism c 1-2-3-5 will be ineffective, and only the 1-2-3-4 four-bar linkage continues to function. Therefore, the finger will transform into the shape-adaptive grasping mode [ 8 ], as shown in Figure 2 a. In this mode, the proximal phalanx and distal phalanx will bend or extend simultaneously under the driving of the linkage driving motor, without relative motion before contacting objects.…”

Section: Design Of the Multi-mode Mechanical Fingermentioning

confidence: 99%

“…In recent years, to achieve a better imitation of human hands, scholars have developed numerous dexterous humanoid robotic hands [ 8 ]. For example, Cheng et al [ 1 ] proposed an adaptive humanoid finger with a nine-bar mechanism; the I-limb robotic hand [ 9 ] by Touch Bionics; the Hannes hand [ 10 ] by M. Laffranchi et al; the BeBionic Hand by Ottobock, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, tendon transmission mechanisms have been adopted by the Huazhong University of Science and Technology’s X-hand [ 15 ], the UK’s Shadow Hand, the I-Limb Hand, and the American Washington Hand. However, the tendon transmission mechanism has a defect of insufficient rigidity, as its tendons are easy to break and require the application of pre-tightening force [ 8 ]. Some researchers [ 16 ] have tried to integrate the linkage and tendon mechanisms in recent years.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design of a Multi-Mode Mechanical Finger Based on Linkage and Tendon Fusion Transmission

et al. 2023

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Today, most humanoid mechanical fingers use an underactuated mechanism driven by linkages or tendons, with only a single and fixed grasping trajectory. This paper proposes a new multi-mode humanoid finger mechanism based on linkage and tendon fusion transmission, which is embedded with an adjustable-length tendon mechanism to achieve three types of grasping mode. The structural parameters of the mechanism are optimized according to the kinematic and static models. Furthermore, a discussion was conducted on how to set the speed ratio of the linkage driving motor and the tendon driving motor to adjust the length and tension of the tendon, in order to achieve the switching of the shape-adaptive, coupled-adaptive, and variable coupling-adaptive grasping modes. Finally, the multi-mode functionality of the proposed finger mechanism was verified through multiple grasping experiments.

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Section: Design Of the Multi-mode Mechanical Fingermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design of a Multi-Mode Mechanical Finger Based on Linkage and Tendon Fusion Transmission

et al. 2023

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“…Prosthetic organs, e.g., hands [1,2], are a robotic system that has a power generator, actuator, body, sensor, and controller [3,4]. One of the important challenges with prosthetic organs, e.g., hands, is to control the prosthetic hands from the patient's brain.…”

Section: Introductionmentioning

confidence: 99%

On Automated Object Grasping for Intelligent Prosthetic Hands Using Machine Learning

Odeyemi,

Ogbeyemi,

Wong

et al. 2024

Bioengineering

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Prosthetic technology has witnessed remarkable advancements, yet challenges persist in achieving autonomous grasping control while ensuring the user’s experience is not compromised. Current electronic prosthetics often require extensive training for users to gain fine motor control over the prosthetic fingers, hindering their usability and acceptance. To address this challenge and improve the autonomy of prosthetics, this paper proposes an automated method that leverages computer vision-based techniques and machine learning algorithms. In this study, three reinforcement learning algorithms, namely Soft Actor-Critic (SAC), Deep Q-Network (DQN), and Proximal Policy Optimization (PPO), are employed to train agents for automated grasping tasks. The results indicate that the SAC algorithm achieves the highest success rate of 99% among the three algorithms at just under 200,000 timesteps. This research also shows that an object’s physical characteristics can affect the agent’s ability to learn an optimal policy. Moreover, the findings highlight the potential of the SAC algorithm in developing intelligent prosthetic hands with automatic object-gripping capabilities.

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“…Biomimetic tendon-driven prosthetic hands are also designed to imitate human hand movement. Narumi proposed a biomimetic prosthetic hand with bones, ligaments, tendons, and multiple muscles based on the human musculoskeletal system [ 15 ]. Liu designed a biomimetic finger based on anatomical structures actuated by SMA with hard and soft modes [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Design and Control of a Tendon-Driven Robotic Finger Based on Grasping Task Analysis

Zhou,

Fu,

Shentu

et al. 2024

Biomimetics

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To analyze the structural characteristics of a human hand, data collection gloves were worn for typical grasping tasks. The hand manipulation characteristics, finger end pressure, and finger joint bending angle were obtained via an experiment based on the Feix grasping spectrum. Twelve types of tendon rope transmission paths were designed under the N + 1 type tendon drive mode, and the motion performance of these 12 types of paths applied to tendon-driven fingers was evaluated based on the evaluation metric. The experiment shows that the designed tendon path (d) has a good control effect on the fluctuations of tendon tension (within 0.25 N), the tendon path (e) has the best control effect on the joint angle of the tendon-driven finger, and the tendon path (l) has the best effect on reducing the friction between the tendon and the pulley. The obtained tendon-driven finger motion performance model based on 12 types of tendon paths is a good reference value for subsequent tendon-driven finger structure design and control strategies.

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A Design of Biomimetic Prosthetic Hand

Cited by 9 publications

References 28 publications

Design of a Multi-Mode Mechanical Finger Based on Linkage and Tendon Fusion Transmission

Design of a Multi-Mode Mechanical Finger Based on Linkage and Tendon Fusion Transmission

On Automated Object Grasping for Intelligent Prosthetic Hands Using Machine Learning

Design and Control of a Tendon-Driven Robotic Finger Based on Grasping Task Analysis

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