Attribute-Driven Granular Model for EMG-Based Pinch and Fingertip Force Grand Recognition
15 Jan 2021-IEEE Transactions on Systems, Man, and Cybernetics (Institute of Electrical and Electronics Engineers)-Vol. 51, Iss: 2, pp 789-800
TL;DR: An attribute-driven granular model (AGrM) under a machine-learning scheme that achieved comparable pinch recognition accuracy but was of lowest computational cost and highest force grand prediction accuracy.
Abstract: Fine multifunctional prosthetic hand manipulation requires precise control on the pinch-type and the corresponding force, and it is a challenge to decode both aspects from myoelectric signals. This paper proposes an attribute-driven granular model (AGrM) under a machine-learning scheme to solve this problem. The model utilizes the additionally captured attribute as the latent variable for a supervised granulation procedure. It was fulfilled for EMG-based pinch-type classification and the fingertip force grand prediction. In the experiments, 16 channels of surface electromyographic signals (i.e., main attribute) and continuous fingertip force (i.e., subattribute) were simultaneously collected while subjects performing eight types of hand pinches. The use of AGrM improved the pinch-type recognition accuracy to around 97.2% by 1.8% when constructing eight granules for each grasping type and received more than 90% force grand prediction accuracy at any granular level greater than six. Further, sensitivity analysis verified its robustness with respect to different channel combination and interferences. In comparison with other clustering-based granulation methods, AGrM achieved comparable pinch recognition accuracy but was of lowest computational cost and highest force grand prediction accuracy.
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195 citations
TL;DR: In this paper, the authors take the sensing method used by HGR technology as the entry point, and make a detailed elaboration and systematic summary by referring to a large number of research achievements in recent years.
Abstract: Human machine interaction (HMI) is an interactive way of information exchange between human and machine. By collecting the information that can be conveyed by the person to express the intention, and then transforming and processing the information, the machine can work according to the intention of the person. However, the traditional HMI including mouse, keyboard etc. usually requires a fixed operating space, which limits people's actions and cannot directly reflect people's intentions. It requires people to learn systematically how to operate skillfully, which indirectly affects work efficiency. Hand gesture, as one of the important ways for human to convey information and express intuitive intention, has the advantages of high degree of differentiation, strong flexibility and high efficiency of information transmission, which makes hand gesture recognition (HGR) as one of the research hotspots in the field of HMI. In order to enable readers to systematically and quickly understand the research status of HGR and grasp the basic problems and development direction of HGR, this article takes the sensing method used by HGR technology as the entry point, and makes a detailed elaboration and systematic summary by referring to a large number of research achievements in recent years.
68 citations
TL;DR: This article demonstrates the potential of A-mode US in automated gesture recognition, and the prospect of sEMG/US fusion for proportional gesture interaction, and shows that the complementary advantages of US and s EMG on gesture recognition and continuous force estimation can be combined for the achievement of multi-class proportional gesture control.
Abstract: Objective: While surface electromyography (sEMG) is still dominant in the field of muscle-computer interface, ultrasound (US) sensing has been regarded as a promising alternative to sEMG, owing to its ability to precisely monitor muscle deformations. Among different US modalities, A-mode US is more compact and cost-effective for wearable applications against its cumbersome B-mode counterpart. In this article, we conduct a comprehensive comparison of wearable A-mode US and sEMG on gesture recognition and isometric muscle contraction force estimation. Methods: We experimented with eight types of gesture, with a range of 0–60% maximum voluntary contraction for each motion. Results: Results show that A-mode US outperforms sEMG on gesture recognition accuracy, robustness, and discrete force estimation accuracy, while sEMG is superior to US on continuous force estimation accuracy and ease of use in force estimation. Moreover, an extended online experiment demonstrates that the complementary advantages of US and sEMG on gesture recognition and continuous force estimation can be combined for the achievement of multi-class proportional gesture control. Significance: This article demonstrates the potential of A-mode US in automated gesture recognition, and the prospect of sEMG/US fusion for proportional gesture interaction.
37 citations
Cites methods from "Attribute-Driven Granular Model for..."
...was employed for the discrete force estimation, where data of each motion were segmented into n sub-motions with different force levels, and all the sub-motions were classified with a unified classifier [25]....
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02 Mar 2020
TL;DR: A novel method that includes subclass discriminant analysis (SDA) and principal component analysis for the simultaneous prediction of wrist rotation (pronation/supination) and finger gestures using wearable ultrasound is proposed, paving the way for musculature-driven artificial hand control and rehabilitation treatment.
Abstract: The ability to predict wrist and hand motions simultaneously is essential for natural controls of hand protheses. In this paper, we propose a novel method that includes subclass discriminant analysis (SDA) and principal component analysis for the simultaneous prediction of wrist rotation (pronation/supination) and finger gestures using wearable ultrasound. We tested the method on eight finger gestures with concurrent wrist rotations. Results showed that SDA was able to achieve accurate classification of both finger gestures and wrist rotations under dynamic wrist rotations. When grouping the wrist rotations into three subclasses, about 99.2 ± 1.2% of finger gestures and 92.8 ± 1.4% of wrist rotations can be accurately classified. Moreover, we found that the first principal component (PC1) of the selected ultrasound features was linear to the wrist rotation angle regardless of finger gestures. We further used PC1 in an online tracking task for continuous wrist control and demonstrated that a wrist tracking precision ( ${R}^{{2}}$ ) of 0.954 ± 0.012 and a finger gesture classification accuracy of 96.5 ± 1.7% can be simultaneously achieved, with only two minutes of user training. Our proposed simultaneous wrist/hand control scheme is training-efficient and robust, paving the way for musculature-driven artificial hand control and rehabilitation treatment.
37 citations
Cites methods from "Attribute-Driven Granular Model for..."
...Different unsupervised algorithms have been attempted in this field, including K-means clustering [25], [28], dynamic cluster formation [26], Gaussian mixture model [29], hierarchical clustering [24], nearest neighbor (NN) clustering [22], and valley...
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TL;DR: A multi-modal sensing system that can collect surface electromyography, near-infrared spectroscopy (NIRS) and mechanomyography (MMG) simultaneously simultaneously and can reliably obtain three kinds of muscle contraction information from the perspective of electrophysiology, oxygen metabolism and low-frequency vibration of myofiber is presented.
Abstract: The research on muscular activity based human-machine interface (HMI) is of great significance, such as controlling prosthetic hand to improve the life quality of amputee patients. However, the HMI performance is limited by muscular fatigue due to frequent muscle contraction. To overcome the drawback, this paper presents a multi-modal sensing system that can collect surface electromyography (sEMG), near-infrared spectroscopy (NIRS) and mechanomyography (MMG) simultaneously. To evaluate the performance of the multi-modal signal acquisition system, incremental isometric voluntary contractions experiment is carried out. The experimental results show that the proposed system can reliably obtain three kinds of muscle contraction information from the perspective of electrophysiology, oxygen metabolism and low-frequency vibration of myofiber. Furthermore, muscle fatigue induced experiment imitating HMI usage is performed, and it convincingly demonstrates a significantly ( ${p} ) improved classification accuracy (CA) by using multi-modal features. The CA is compensated by 3.6% ~ 22.9% in the presence of muscular fatigue. These results suggest that multi-modal sensing can improve the HMI performance and robustness. The outcomes of this study have great potential to promote the biomedical and clinical applications of human-machine interaction.
25 citations
References
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TL;DR: M Modes of information granulation (IG) in which the granules are crisp (c-granular) play important roles in a wide variety of methods, approaches and techniques, but this does not reflect the fact that in almost all of human reasoning and concept formation thegranules are fuzzy (f- Granular).
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TL;DR: A novel approach to the control of a multifunction prosthesis based on the classification of myoelectric patterns is described, which increases the number of functions which can be controlled by a single channel of myOElectric signal but does so in a way which does not increase the effort required by the amputee.
Abstract: A novel approach to the control of a multifunction prosthesis based on the classification of myoelectric patterns is described. It is shown that the myoelectric signal exhibits a deterministic structure during the initial phase of a muscle contraction. Features are extracted from several time segments of the myoelectric signal to preserve pattern structure. These features are then classified using an artificial neural network. The control signals are derived from natural contraction patterns which can be produced reliably with little subject training. The new control scheme increases the number of functions which can be controlled by a single channel of myoelectric signal but does so in a way which does not increase the effort required by the amputee. Results are presented to support this approach. >
1,898 citations
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...EMG feature set [12], [41], [42], including mean absolute value (MAV), waveform length (WL), zero changes (ZC), and slope sign changes (SSC), was selected in this paper; hence,...
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TL;DR: The pertinent issues and best practices in EMG pattern recognition are described, the major challenges in deploying robust control are identified, and research directions that may have an effect in the near future are advocated.
Abstract: Using electromyogram (EMG) signals to control upper-limb prostheses is an important clinical option, offering a person with amputation autonomy of control by contracting residual muscles. The dexterity with which one may control a prosthesis has progressed very little, especially when control- ling multiple degrees of freedom. Using pattern recognition to discriminate multiple degrees of freedom has shown great promise in the research literature, but it has yet to transition to a clinically viable option. This article describes the pertinent issues and best practices in EMG pattern recognition, identifies the major challenges in deploying robust control, and advocates research directions that may have an effect in the near future.
837 citations
"Attribute-Driven Granular Model for..." refers background in this paper
...The EMG pattern of the same grasp under different grasping force mismatches with each other, and it leads to the misclassification of pattern recognition systems [3], [7]–[9]....
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...The presence of contractions from unseen force levels increased the error considerably by more than 32%, and to counteract the severe degradation, a pooled training set comprising all force levels was suggested [3]....
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TL;DR: The resulting taxonomy incorporates all grasps found in the reviewed taxonomies that complied with the grasp definition and is shown that due to the nature of the classification, the 33 grasp types might be reduced to a set of 17 more generalgrasps if only the hand configuration is considered without the object shape/size.
Abstract: In this paper, we analyze and compare existing human grasp taxonomies and synthesize them into a single new taxonomy (dubbed “The GRASP Taxonomy” after the GRASP project funded by the European Commission). We consider only static and stable grasps performed by one hand. The goal is to extract the largest set of different grasps that were referenced in the literature and arrange them in a systematic way. The taxonomy provides a common terminology to define human hand configurations and is important in many domains such as human–computer interaction and tangible user interfaces where an understanding of the human is basis for a proper interface. Overall, 33 different grasp types are found and arranged into the GRASP taxonomy. Within the taxonomy, grasps are arranged according to 1) opposition type, 2) the virtual finger assignments, 3) type in terms of power, precision, or intermediate grasp, and 4) the position of the thumb. The resulting taxonomy incorporates all grasps found in the reviewed taxonomies that complied with the grasp definition. We also show that due to the nature of the classification, the 33 grasp types might be reduced to a set of 17 more general grasps if only the hand configuration is considered without the object shape/size.
636 citations
"Attribute-Driven Granular Model for..." refers methods in this paper
...The selection of these motions followed the Feix’s taxonomy [40] and, meanwhile, considered the executability by the dexterous self-powered hand prostheses, called i-LIMB ultra revolution....
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