XAI for myo-controlled prosthesis: Explaining EMG data for hand gesture classification

Gozzi, Noemi; Malandri, Lorenzo; Mercorio, Fabio; Pedrocchi, Alessandra

doi:10.1016/j.knosys.2021.108053

Cited by 35 publications

(12 citation statements)

References 39 publications

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“…The study in [20] proposed fine perceptive generative adversarial networks (FP-GANs) for generating superresolution (SR) magnetic resonance (MR) images from their low-resolutions. FP-GANs used divide-and-conquer techniques for handling high/low-frequency components of MR images independently and concurrently.…”

Section: Related Workmentioning

confidence: 99%

"Human Hand Gesture Recognition Based Feature Extraction and Classification Using Sign Language Data"

Siji,

Nithya

2024

IJAER

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Hand gestures are natural forms of Human-Computer Interaction (HCI).Hand gesture recognitions are rapidly developing fields of study in computer vision and machine learning as they offer wide ranges of potential applications, allowing people to interact with robots and system interfaces in more straightforward and natural ways without requirements for additional hardware. Hence, gesture recognition studies aim to develop systems that can recognize certain human gestures and utilize them to operate devices or transmit information. In order to accompolish real-time gesture recognitions, reliable hand detections are necessary for vision-based hand gesture interfaces. The initial part of this work is data collection, and standard dataset is introduced for data validations. Dataset with 26 signs was created using Sign Language (SL) and assessed in real time for a variety of lighting scenarios. The suggested technique recognises motions and identifies human hands using a feature set of symmetric patterns that is obtained using techniques for extracting hand perimeter features. Depth based Speeded-Up Robust Features (DSURF) is employed to identify important focal spots in SL-based images along with their associated characteristics. Finally, hand gestures are classified using Improved Graph Neural Networks (IGNN) trained on symmetric patterns under varying illumination conditions. Selected characteristics are validated where experimental results suggested that this work's schema outperformed other methods in twerms of precision for a variety of illumination conditions and ranging pixel sizes.

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Section: Related Workmentioning

confidence: 99%

"Human Hand Gesture Recognition Based Feature Extraction and Classification Using Sign Language Data"

Siji,

Nithya

2024

IJAER

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“…Utilizing multimodal Utterance Representation Markup Language, gesture expressions inside the Articulated Communicator Engine (ACE) framework may be defined in two distinct ways (Salem et al, 2012 ). A gesture's exterior representation, such as the posture of the gesture stroke, can be clearly articulated in verbal words and co-verbal gestures, which are classified as feature-based explanations (Gozzi et al, 2022 ). By correlating temporal markers, gesture association to certain language pieces is discovered.…”

Section: Techniques For Multimodal Human–robot Interactionmentioning

confidence: 99%

Recent advancements in multimodal human–robot interaction

Yang

et al. 2023

Front. Neurorobot.

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Robotics have advanced significantly over the years, and human–robot interaction (HRI) is now playing an important role in delivering the best user experience, cutting down on laborious tasks, and raising public acceptance of robots. New HRI approaches are necessary to promote the evolution of robots, with a more natural and flexible interaction manner clearly the most crucial. As a newly emerging approach to HRI, multimodal HRI is a method for individuals to communicate with a robot using various modalities, including voice, image, text, eye movement, and touch, as well as bio-signals like EEG and ECG. It is a broad field closely related to cognitive science, ergonomics, multimedia technology, and virtual reality, with numerous applications springing up each year. However, little research has been done to summarize the current development and future trend of HRI. To this end, this paper systematically reviews the state of the art of multimodal HRI on its applications by summing up the latest research articles relevant to this field. Moreover, the research development in terms of the input signal and the output signal is also covered in this manuscript.

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“…which improves the classification accuracy, robustness, and computation cost while reducing the number of required EMG electrodes. [119] Artificial skins with multisensory functions have been developed to mimic the function of human skin. Ionic tactile sensors (ITS) are being developed to emulate the human skin considering not only mechanical behavior but also the human sense of touch.…”

Section: Key Work and State-of-the-art Technologiesmentioning

confidence: 99%

“…which improves the classification accuracy, robustness, and computation cost while reducing the number of required EMG electrodes. [ 119 ]…”

Section: Key Work and State‐of‐the‐art Technologiesmentioning

confidence: 99%

Novel Grasping Mechanisms of 3D‐Printed Prosthetic Hands

Moeinnia

Kim

2022

Advanced Intelligent Systems

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There are millions of amputees worldwide, and amputation has significantly affected their lives. People with hand losses have difficulties performing the activities of daily living (ADLs). Prosthetic hands have been developed to perform the functions of a human hand by grasping various objects. However, mimicking the actual grasping function of a human hand is still an unresolved research topic. Researchers continue to improve the functionality of hand prostheses to create more efficient and closer to human motion. Herein, the current challenges and approaches for enhancing the grasping function of 3D‐printed hand prostheses are investigated. Three technology sectors are discussed for the efficient grasping motion of hand prosthetics: 1) how to recognize the user's desired grasping gestures by sensing systems; 2) how to power the prosthesis for grasping objects through different actuation systems; and 3) how to perform the grasping motion by 3D design and mechanisms. This article reviews valuable information regarding the current innovations toward improving prosthetic hands’ grasping function to help researchers design more functional hand prostheses.

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XAI for myo-controlled prosthesis: Explaining EMG data for hand gesture classification

Cited by 35 publications

References 39 publications

"Human Hand Gesture Recognition Based Feature Extraction and Classification Using Sign Language Data"

"Human Hand Gesture Recognition Based Feature Extraction and Classification Using Sign Language Data"

Recent advancements in multimodal human–robot interaction

Novel Grasping Mechanisms of 3D‐Printed Prosthetic Hands

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