Artificial-Hand Technology—Current State of Knowledge in Designing and Forecasting Changes

Szkopek, Jacek; Redlarski, Grzegorz

doi:10.3390/app9194090

Cited by 10 publications

(4 citation statements)

References 137 publications

(287 reference statements)

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“…In the field of robotic hands, a similar tendency towards human-like designs has been observed [11]. Most of the high-tech hands have been developed by research laboratories but are rarely translated to commercial devices.…”

Section: Related Workmentioning

confidence: 54%

sEMG-Based Robust Recognition of Grasping Postures with a Machine Learning Approach for Low-Cost Hand Control

Mora,

García-Ortiz,

Cerdá-Boluda

2024

Sensors

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The design and control of artificial hands remains a challenge in engineering. Popular prostheses are bio-mechanically simple with restricted manipulation capabilities, as advanced devices are pricy or abandoned due to their difficult communication with the hand. For social robots, the interpretation of human intention is key for their integration in daily life. This can be achieved with machine learning (ML) algorithms, which are barely used for grasping posture recognition. This work proposes an ML approach to recognize nine hand postures, representing 90% of the activities of daily living in real time using an sEMG human–robot interface (HRI). Data from 20 subjects wearing a Myo armband (8 sEMG signals) were gathered from the NinaPro DS5 and from experimental tests with the YCB Object Set, and they were used jointly in the development of a simple multi-layer perceptron in MATLAB, with a global percentage success of 73% using only two features. GPU-based implementations were run to select the best architecture, with generalization capabilities, robustness-versus-electrode shift, low memory expense, and real-time performance. This architecture enables the implementation of grasping posture recognition in low-cost devices, aimed at the development of affordable functional prostheses and HRI for social robots.

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Section: Related Workmentioning

confidence: 54%

sEMG-Based Robust Recognition of Grasping Postures with a Machine Learning Approach for Low-Cost Hand Control

Mora,

García-Ortiz,

Cerdá-Boluda

2024

Sensors

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“…1(a). However, although selecting a DC motor that can operate in these operating points is possible, the selection must require a DC motor with a high nominal power, which becomes large and heavy [6], [18].…”

Section: Conceptual Designmentioning

confidence: 99%

Design of a Single-DoF Prosthetic Hand With Practical Maximum Grip Force and Grasp Speed for ADLs Using Dual-Motor Actuator

Vanich

Tangpornprasert

Virulsri

2023

IEEE Robot. Autom. Lett.

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Sufficient grip force and appropriate grasp speed provided by a prosthetic hand are essential for daily use. However, due to limitations in size and weight, it is very challenging to select motors that can generate enough power to provide sufficient grip force and high grasp speed. Accordingly, this research proposes a new prosthetic hand design using a dual-motor actuator as an actuation strategy, which consists of two motors separated by a differential mechanism to operate at the load (high force) and no-load (high speed) conditions individually. One motor is used for creating a high torque and the other for creating a high speed, so the prosthetic hand can operate at the loads without using a high-power motor with excessive size and weight. The developed prosthetic hand can perform grasping motions with the following achievements: It can provide a maximum grip force of 80.2 N and maintain a maximum of 62 N with non-backdrivable mechanisms, and provide an average closing time of 1.2 s. Furthermore, the design can provide more grip force than research prosthetic hands using a single motor.

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“…One of the most agile robotic hands integrated into a humanoid, is the one in iCub, which has 9 DOF in total. Humanoid hands that are detached from a robotic trunk and mounted to a rigid frame can have a higher number of DOF (Szkopek and Redlarski, 2019). For example, the Gifu Hand III has 16 DOF (Kawasaki and Mouri, 2019), and the Shadow Hand even 20 DOF (Rothling et al).…”

Section: Manipulationmentioning

confidence: 99%

Do robots outperform humans in human-centered domains?

Riener,

Rabezzana,

Zimmermann

2023

Front. Robot. AI

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The incessant progress of robotic technology and rationalization of human manpower induces high expectations in society, but also resentment and even fear. In this paper, we present a quantitative normalized comparison of performance, to shine a light onto the pressing question, “How close is the current state of humanoid robotics to outperforming humans in their typical functions (e.g., locomotion, manipulation), and their underlying structures (e.g., actuators/muscles) in human-centered domains?” This is the most comprehensive comparison of the literature so far. Most state-of-the-art robotic structures required for visual, tactile, or vestibular perception outperform human structures at the cost of slightly higher mass and volume. Electromagnetic and fluidic actuation outperform human muscles w.r.t. speed, endurance, force density, and power density, excluding components for energy storage and conversion. Artificial joints and links can compete with the human skeleton. In contrast, the comparison of locomotion functions shows that robots are trailing behind in energy efficiency, operational time, and transportation costs. Robots are capable of obstacle negotiation, object manipulation, swimming, playing soccer, or vehicle operation. Despite the impressive advances of humanoid robots in the last two decades, current robots are not yet reaching the dexterity and versatility to cope with more complex manipulation and locomotion tasks (e.g., in confined spaces). We conclude that state-of-the-art humanoid robotics is far from matching the dexterity and versatility of human beings. Despite the outperforming technical structures, robot functions are inferior to human ones, even with tethered robots that could place heavy auxiliary components off-board. The persistent advances in robotics let us anticipate the diminishing of the gap.

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Artificial-Hand Technology—Current State of Knowledge in Designing and Forecasting Changes

Cited by 10 publications

References 137 publications

sEMG-Based Robust Recognition of Grasping Postures with a Machine Learning Approach for Low-Cost Hand Control

sEMG-Based Robust Recognition of Grasping Postures with a Machine Learning Approach for Low-Cost Hand Control

Design of a Single-DoF Prosthetic Hand With Practical Maximum Grip Force and Grasp Speed for ADLs Using Dual-Motor Actuator

Do robots outperform humans in human-centered domains?

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