Design of an Underactuated Prosthetic Hand with Flexible Multi-Joint Fingers and EEG-Based Control

Teng, Zhicheng; Xu, Guanghua; Liang, Renghao; Li, Min; Zhang, Sicong; Chen, Jiazhou; Han, Chengcheng

doi:10.1109/cbs.2018.8612240

Cited by 10 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This classification considered information from 2018 to 2023. For forty-six scientific articles: in 2018, seven were found [50,53,78,88,108,110,111]; in 2019, seven [38,48,51,76,82,86,109]; in 2020, twelve [37,[44][45][46][47]49,70,81,91,112,113,123]; in 2021, nine [36,52,69,79,[83][84][85]87,89]; in 2022, eight [35,[71][72][73][74][75]80,124]; and in 2023, three [77,90,114].…”

Section: According To the Year Of Publicationmentioning

confidence: 99%

Upper Limb Prostheses by the Level of Amputation: A Systematic Review

Segura,

Romero,

Abarca

et al. 2024

Prosthesis

View full text Add to dashboard Cite

This review article aims to provide an updated and comprehensive overview of the latest trends in adult upper limb prostheses, specifically targeting various amputation levels such as transradial, transmetacarpal, transcarpal, and transhumeral. A systematic search was conducted across multiple databases, including IEEE Xplore, MDPI, Scopus, Frontiers, and Espacenet, covering from 2018 to 2023. After applying exclusion criteria, 49 scientific articles (33 patents and 16 commercial prostheses) were meticulously selected for review. The article offers an in-depth analysis of several critical aspects of upper limb prostheses. It discusses the evolution and current state of input control mechanisms, the number of degrees of freedom, and the variety of grips available in prostheses, all tailored according to the level of amputation. Additionally, the review delves into the selection of materials used in developing these prostheses and examines the progression of technology readiness levels. A significant focus is also placed on the evolution of prosthesis weight over the years for different amputation levels. Moreover, the review identifies and explores critical technological challenges and prospects in upper limb prostheses. Finally, the article culminates with a conclusion that encapsulates the key findings and insights on the advancements and ongoing developments in this field.

show abstract

Section: According To the Year Of Publicationmentioning

confidence: 99%

Upper Limb Prostheses by the Level of Amputation: A Systematic Review

Segura,

Romero,

Abarca

et al. 2024

Prosthesis

View full text Add to dashboard Cite

show abstract

“…Previous works chiefly focused either on the definition of suitable control systems [31][32][33][34][35] or on the mechanical design (e.g., [36,37]). The considered movements were generally slow (e.g., more than a couple of seconds), so sometimes, they were not suitable for industrial uses, in which fast movements are required.…”

Section: Contributions Of This Workmentioning

confidence: 99%

Influence of Joint Stiffness and Motion Time on the Trajectories of Underactuated Robots

et al. 2023

View full text Add to dashboard Cite

Underactuated robots have fewer actuators than degrees of freedom (DOF). Nonactuated joints can be equipped with torsional springs. Underactuated robots can be controlled in a point-to-point motion if they have a particular mass distribution that makes them differentially flat. The trajectory described by the robot moving from the start point to the end point largely depends on the torsional stiffness of the nonactuated joints and on motion time. Thus, the same point-to-point motion can be obtained by sweeping different parts of the workspace. This property increases the dexterity of the robot. This paper focuses on the trajectories of a 3-DOF robot moving in the horizontal plane with two actuators and a torsional spring. Parametric analyses showing the effect of torsional stiffness and motion time are presented. The existence of combinations of torsional stiffness and motion time that minimize the motion torques or the swept area is discussed. The area swept by the underactuated robot is compared with the one swept by an equivalent actuated robot performing the same task. Reductions in the swept area of up to 36% are obtained. Finally, numerical results are validated by means of experimental tests on a simplified prototype.

show abstract

“…First, we calculated the spectrogram using the SciPy library. To avoid singularity in the next steps, a negligible number (10 ) has been added to the spectrogram. Then, we used the NumPy library to calculate the logarithm from the previous step to compute a speech 2D feature.…”

Section: Experimental Setup and Speech Data Setmentioning

confidence: 99%

“…Electric powered prosthetic hands can receive commands from users by one or few ways such as push-button, joystick, keyboard, text, electroencephalography (EEG) [9], Electroneurography (ENG) [10][11][12], electromyography (EMG) [13][14][15][16][17][18][19][20], vision [21][22][23], and speech [24][25][26][27][28]. Among these ways, electromyography is the most convenient for amputees.…”

Section: Introductionmentioning

confidence: 99%

End-to-End Learning of Speech 2D Feature-Trajectory for Prosthetic Hands

Jafarzadeh

Tadesse

2020

2020 Second International Conference on Transdisciplinary AI (TransAI)

View full text Add to dashboard Cite

Speech is one of the most common forms of communication in humans. Speech commands are essential parts of multimodal controlling of prosthetic hands. In the past decades, researchers used automatic speech recognition systems for controlling prosthetic hands by using speech commands. Automatic speech recognition systems learn how to map human speech to text. Then, they used natural language processing or a look-up table to map the estimated text to a trajectory. However, the performance of conventional speech-controlled prosthetic hands is still unsatisfactory. Recent advancements in generalpurpose graphics processing units (GPGPUs) enable intelligent devices to run deep neural networks in real-time. Thus, architectures of intelligent systems have rapidly transformed from the paradigm of composite subsystems optimization to the paradigm of end-to-end optimization. In this paper, we propose an end-to-end convolutional neural network (CNN) that maps speech 2D features directly to trajectories for prosthetic hands. The proposed convolutional neural network is lightweight, and thus it runs in real-time in an embedded GPGPU. The proposed method can use any type of speech 2D feature that has local correlations in each dimension such as spectrogram, MFCC, or PNCC. We omit the speech to text step in controlling the prosthetic hand in this paper. The network is written in Python with Keras library that has a TensorFlow backend. We optimized the CNN for NVIDIA Jetson TX2 developer kit. Our experiment on this CNN demonstrates a root-mean-square error of 0.119 and 20ms running time to produce trajectory outputs corresponding to the voice input data. To achieve a lower error in real-time, we can optimize a similar CNN for a more powerful embedded GPGPU such as NVIDIA AGX Xavier.

show abstract

Design of an Underactuated Prosthetic Hand with Flexible Multi-Joint Fingers and EEG-Based Control

Cited by 10 publications

References 15 publications

Upper Limb Prostheses by the Level of Amputation: A Systematic Review

Upper Limb Prostheses by the Level of Amputation: A Systematic Review

Influence of Joint Stiffness and Motion Time on the Trajectories of Underactuated Robots

End-to-End Learning of Speech 2D Feature-Trajectory for Prosthetic Hands

Contact Info

Product

Resources

About