ArticleBiomimetics (Basel, Switzerland)2023
A Proposal of Bioinspired Soft Active Hand Prosthesis.
Article in Biomimetics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed, 21 citations in OpenAlex.
- Hand Prosthesis with Soft Robotics Technology and Artificial Intelligence for Fine Motor Control.Sensors (Basel, Switzerland) · 2026Article
- Anthropomorphic Robotic Hand Prosthesis Developed for Children.Biomimetics (Basel, Switzerland) · 2024Article
- Recent Advances in Biomimetics for the Development of Bio-Inspired Prosthetic Limbs.Biomimetics (Basel, Switzerland) · 2024Review
- The LIBRA NeuroLimb: Hybrid Real-Time Control and Mechatronic Design for Affordable Prosthetics in Developing Regions.Sensors (Basel, Switzerland) · 2023Article
- Design of a Multi-Mode Mechanical Finger Based on Linkage and Tendon Fusion Transmission.Biomimetics (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
Soft robotics have broken the rigid wall of interaction between humans and robots due to their own definition and manufacturing principles, allowing robotic systems to adapt to humans and enhance or restore their capabilities. In this research we propose a dexterous bioinspired soft active hand prosthesis based in the skeletal architecture of the human hand. The design includes the imitation of the musculoskeletal components and morphology of the human hand, allowing the prosthesis to emulate the biomechanical properties of the hand, which results in better grips and a natural design. CAD models for each of the bones were developed and 3D printing was used to manufacture the skeletal structure of the prosthesis, also soft materials were used for the musculoskeletal components. A myoelectric control system was developed using a recurrent neural network (RNN) to classify the hand gestures using electromyography signals; the RNN model achieved an accuracy of 87% during real time testing. Objects with different size, texture and shape were tested to validate the grasping performance of the prosthesis, showing good adaptability, soft grasping and mechanical compliance to object of the daily life.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.