State-of-the-Art Method in Prosthetic Hand Design: A Review

Triwiyanto, Triwiyanto; Rahmawati, Triana; Pawana, I Putu Alit; Lamidi, L.; Hamzah, Torib; Pudji, Andjar; Mak’ruf, Muhammad Ridha; Luthfiyah, Sari

doi:10.4028/www.scientific.net/jbbbe.50.15

Cited by 9 publications

(5 citation statements)

References 27 publications

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“…where F(t) stands for the estimation of F(t) in (1). S and A are the mixing matrices, which need to be determined during the training phase.…”

Section: Motor Neuron Synergymentioning

confidence: 99%

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Decoding Multi-DoF Movements Using a CST-Based Force Generation Model With Single-DoF Training

Xu,

Yu,

Zhao

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Recent developments in dexterous myoelectric prosthetics have established a hardware base for humanmachine interfaces. Although pattern recognition techniques have seen successful deployment in gesture classification, their applications remain largely confined to certain specific discrete gestures. Addressing complex daily tasks demands an immediate need for precise simultaneous and proportional control (SPC) for multiple degrees of freedom (DoFs) movements. In this paper, we introduce an SPC approach for multi-DoF wrist movements using the cumulative spike trains (CSTs) of motor unit pools, merely leveraging single-DoF training. The efficacy of our proposed approach was validated offline against existing methods respectively based on non-negative matrix factorization and motor unit spike trains, using experimental data. The experimental process includes both single-DoF (for training) and multi-DoF (for testing) movements. We evaluated the performance using Pearson correlation coefficient (R) and the normalized root mean square error (nRMSE). The results reveal that our method outperforms comparative approaches in force estimation for both testing datasets (3 and 4). On average, for dataset 3, R and nRMSE of the flexion/extension DoF (the pronation/supination DoF) are 0.923±0.037 (0.901±0.040) and 12.3±3.1% (12.9±2.2%); similarly, those of dataset 4 are 0.865±0.057 (0.837±0.053) and 14.9±2.9% (15.4±2.0%), respectively. The outcomes demonstrate the effectiveness of our method in simultaneous and proportional force estimation for multi-DoF wrist movements, showing a promising potential as a neuralmachine interface for SPC of dexterous myoelectric prostheses.

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“…where F(t) stands for the estimation of F(t) in (1). S and A are the mixing matrices, which need to be determined during the training phase.…”

Section: Motor Neuron Synergymentioning

confidence: 99%

“…D EXTEROUS myoelectric prostheses have significantly bridged the gap, mimicking the degrees of freedom (DoFs) offered by human hands [1], [2]. However, the practical applications of prosthetic hands are hindered due to lacking of a robust humanmachine interface with intuitive and flexible control systems [3].…”

Section: Introductionmentioning

confidence: 99%

Decoding Multi-DoF Movements Using a CST-Based Force Generation Model With Single-DoF Training

Xu,

Yu,

Zhao

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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show abstract

“…The control strategies can be classified into two main categories: feedforward and feedback control. Feedforward control relies on the user's intention to generate commands for the prosthetic hand, while feedback control provides information to the user about the state and outcome of the hand's actions [13].…”

Section: Prosthetic Hands Controlmentioning

confidence: 99%

A Perspective on Prosthetic Hands Control: From the Brain to the Hand

Gentile,

Gruppioni

2023

Prosthesis

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The human hand is a complex and versatile organ that enables humans to interact with the environment, communicate, create, and use tools. The control of the hand by the brain is a crucial aspect of human cognition and behaviour, but also a challenging problem for both neuroscience and engineering. The aim of this study is to review the current state of the art in hand and grasp control from a neuroscientific perspective, focusing on the brain mechanisms that underlie sensory integration for hand control and the engineering implications for developing artificial hands that can mimic and interface with the human brain. The brain controls the hand by processing and integrating sensory information from vision, proprioception, and touch, using different neural pathways. The user’s intention can be obtained to control the artificial hand by using different interfaces, such as electromyography, electroneurography, and electroencephalography. This and other sensory information can be exploited by different learning mechanisms that can help the user adapt to changes in sensory inputs or outputs, such as reinforcement learning, motor adaptation, and internal models. This work summarizes the main findings and challenges of each aspect of hand and grasp control research and highlights the gaps and limitations of the current approaches. In the last part, some open questions and future directions for hand and grasp control research are suggested by emphasizing the need for a neuroscientific approach that can bridge the gap between the brain and the hand.

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“…exoskeletons, prostheses, and orthoses [1][2][3][4]. This is especially the case for myocontrolled hand prostheses or exoskeletons due to the indispensability of human hand in activities of daily living [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Concurrent and continuous estimation of multi-finger forces by synergy mapping and reconstruction: a pilot study

Teng,

Xu,

Zhang

et al. 2023

J. Neural Eng.

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Objective. The absence of intuitive control in present myoelectric interfaces makes it a challenge for users to communicate with assistive devices efficiently in real-world conditions. This study aims to tackle this difficulty by incorporating neurophysiological entities, namely muscle and force synergies, onto multi-finger force estimation to allow intuitive myoelectric control. Approach. Eleven healthy subjects performed six isometric grasping tasks at three muscle contraction levels. The exerted fingertip forces were collected concurrently with the surface electromyographic (sEMG) signals from six extrinsic and intrinsic muscles of hand. Muscle synergies were then extracted from recorded sEMG signals, while force synergies were identified from measured force data. Afterwards, a linear regressor was trained to associate the two types of synergies. This would allow us to predict multi-finger forces simply by multiplying the activation signals derived from muscle synergies with the weighting matrix of initially identified force synergies. To mitigate the false activation of unintended fingers, the force predictions were finally corrected by a finger state recognition procedure. Main results. We found that five muscle synergies and four force synergies are able to make a tradeoff between the computation load and the prediction accuracy for the proposed model; When trained and tested on all six grasping tasks, our method (SYN-II) achieved better performance (R 2 = 0.80 ± 0.04, NRMSE = 0.19 ± 0.01) than conventional sEMG amplitude-based method; Interestingly, SYN-II performed better than all other methods when tested on two unknown tasks outside the four training tasks (R 2 = 0.74 ± 0.03, NRMSE = 0.22 ± 0.02), which indicated better generalization ability. Significance. This study shows the first attempt to link between muscle and force synergies to allow concurrent and continuous estimation of multi-finger forces from sEMG. The proposed approach may lay the foundation for high-performance myoelectric interfaces that allow users to control robotic hands in a more natural and intuitive manner.

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State-of-the-Art Method in Prosthetic Hand Design: A Review

Cited by 9 publications

References 27 publications

Decoding Multi-DoF Movements Using a CST-Based Force Generation Model With Single-DoF Training

Decoding Multi-DoF Movements Using a CST-Based Force Generation Model With Single-DoF Training

A Perspective on Prosthetic Hands Control: From the Brain to the Hand

Concurrent and continuous estimation of multi-finger forces by synergy mapping and reconstruction: a pilot study

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