Hand Electromyography Circuit and Signals Classification Using Artificial Neural Network

Shahzaib, Muhammad; Shakil, Sadia

doi:10.1109/icet.2018.8603587

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…Different classifiers have been exploited in ULP control such as support vector machine, regularized least squares, whereas the gold-standard is the linear discriminant analysis (Scheme and Englehart 2011, Cloutier and Yang 2013b, Di Domenico et al 2021. Among NN, the most common architecture is the multi-layer perceptron (MLP) (Amrani et al 2017, Shahzaib andShakil 2018). The MLP is a supervised machine learning (ML) technique, which exploits labeled data to train the algorithm.…”

Section: Post-processingmentioning

confidence: 99%

Active upper limb prostheses: a review on current state and upcoming breakthroughs

et al. 2023

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The journey of a prosthetic user is characterized by the opportunities and the limitations of a device that should enable activities of daily living (ADL). In particular, experiencing a bionic hand as a functional (and, advantageously, embodied) limb constitutes the premise for promoting the practice in using the device, mitigating the risk of its abandonment. In order to achieve such a result, different aspects need to be considered for making the artificial limb an effective solution to accomplish activities of daily living. According to such a perspective, this review aims at presenting the current issues and at envisioning the upcoming breakthroughs in upper limb prosthetic devices. We first define the sources of input and feedback involved in the system control (at user-level and device-level), alongside the related algorithms used in signal analysis. Moreover, the paper focuses on the user-centered design challenges and strategies that guide the implementation of novel solutions in this area in terms of technology acceptance, embodiment, and, in general, human-machine integration based on co-adaptive processes. We here provide the readers (belonging to the target communities of researchers, designers, developers, clinicians, industrial stakeholders, and end-users) with an overview of the state-of-the-art and the potential innovations in bionic hands features, hopefully promoting interdisciplinary efforts for solving current issues of ULPs. The integration of different perspectives should be the premise to a transdisciplinary intertwining leading to a truly holistic comprehension and improvement of the bionic hands design. Overall, this paper aims to move the boundaries in prosthetic innovation beyond the development of a tool and towards the engineering of human-centered artificial limbs.

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Section: Post-processingmentioning

confidence: 99%

Active upper limb prostheses: a review on current state and upcoming breakthroughs

et al. 2023

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Classification of forearm EMG signals for 10 motions using optimum feature-channel combinations

Shahzaib

Shakil

Ghuffar

et al. 2020

Computer Methods in Biomechanics and Biomedical Engineering

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Manipulator Control System Based on Flexible Sensor Technology

Chen,

Wang,

Chen

et al. 2023

Micromachines

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The research on the remote control of manipulators based on flexible sensor technology is gradually extensive. In order to achieve stable, accurate, and efficient control of the manipulator, it is necessary to reasonably design the structure of the sensor with excellent tensile strength and flexibility. The acquisition of manual information by high-performance sensors is the basis of manipulator control. This paper starts with the manufacturing of materials of the flexible sensor for the manipulator, introduces the substrate, sensor, and flexible electrode materials, respectively, and summarizes the performance of different flexible sensors. From the perspective of manufacturing, it introduces their basic principles and compares their advantages and disadvantages. Then, according to the different ways of wearing, the two control methods of data glove control and surface EMG control are respectively introduced, the principle, control process, and detection accuracy are summarized, and the problems of material microstructure, reducing the cost, optimizing the circuit design and so on are emphasized in this field. Finally, the commercial application in this field is explained and the future research direction is proposed from two aspects: how to ensure real-time control and better receive the feedback signal from the manipulator.

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Hand Electromyography Circuit and Signals Classification Using Artificial Neural Network

Cited by 3 publications

References 23 publications

Active upper limb prostheses: a review on current state and upcoming breakthroughs

Active upper limb prostheses: a review on current state and upcoming breakthroughs

Classification of forearm EMG signals for 10 motions using optimum feature-channel combinations

Manipulator Control System Based on Flexible Sensor Technology

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