DL-Net: Sparsity Prior Learning for Grasp Pattern Recognition

Huang, Ziwei; Zheng, Jingjing; Zhao, Li; Chen, Huiling; Jiang, Xianta; Zhang, Xiaoqin

doi:10.1109/access.2023.3236402

Cited by 2 publications

(8 citation statements)

References 33 publications

(37 reference statements)

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“…However, the number and quality of sEMG sensors and electrodes may be limited, which can affect their ability to adequately cover and sample the signals. The sensors and electrodes may also be constrained by size, shape, placement, and contact, which can further impact spatial and temporal coverage and sampling [96,138]. The quality of sensors and electrodes can also affect signal acquisition and transmission [45].…”

Section: Discussion Opportunities and Open Issuesmentioning

confidence: 99%

“…To address the variability and nonstationarity of sEMG signals caused by fatigue, sweat, electrode displacement, and arm position, certain techniques can be employed to assist the sEMG-based control system. These techniques can further adapt to user preferences and feedback, refining the control model [138].…”

mentioning

confidence: 99%

“…By utilizing techniques that adapt successful models from previous subjects, the training time and effort required to control an upper limb prosthesis can be reduced. Additionally, these techniques can allow the user to learn new gestures or movements with the prosthetic hand without interfering with pre-existing ones [138,139].…”

mentioning

confidence: 99%

“…Implementing sEMG-based control systems in real time and online may be limited by the computational complexity and power consumption of machine learning and deep learning algorithms. Achieving good performance and accuracy with these algorithms may require high computational resources and training data [138]. The algorithms' complexity and power consumption can affect the control system's speed and efficiency, necessitating optimization and compression techniques to reduce them [148].…”

mentioning

confidence: 99%

“…Using sEMG-based control systems for prosthetic hands requires user training and adaptation, which can be challenging and time-consuming. The user needs to learn the gestures and motions that the control system can recognize and adjust the control parameters and feedback mechanisms to suit their preferences and needs [10,138]. These factors can affect the usability and comfort of the control system and demand user-friendly and personalized techniques to facilitate them [48,93].…”

mentioning

confidence: 99%

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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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Section: Discussion Opportunities and Open Issuesmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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

Gentile,

Gruppioni

2023

Prosthesis

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Fast Rock Detection in Visually Contaminated Mining Environments Using Machine Learning and Deep Learning Techniques

Rodriguez-Guillen,

Kern,

Urrea

2024

Applied Sciences

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Advances in machine learning algorithms have allowed object detection and classification to become booming areas. The detection of objects, such as rocks, in mining operations is affected by fog, snow, suspended particles, and high lighting. These environmental conditions can stop the development of mining work, which entails a considerable increase in operating costs. It is vital to select a machine learning algorithm that is accurate, fast, and contributes to lower operational costs because of the aforementioned environmental situations. In this study, the Viola-Jones algorithm, Aggregate Channel Features (ACF), Faster Regions with Convolutional Neural Networks (Faster R-CNN), Single-Shot Detector (SSD), and You Only Look Once (YOLO) version 4 were analyzed, considering the precision metrics, recall, AP50, and average detection time. In our preliminary tests, we have observed that the differences between YOLO v4 and the latest versions are not substantial for the specific problem of rock detection addressed in our article. Therefore, YOLO v4 is an appropriate and representative choice for evaluating the effectiveness of existing methods in our study. The YOLO v4 algorithm performed the best overall, whereas the SSD algorithm performed the fastest. The results indicate that the YOLO v4 algorithm is a promising candidate for detecting rocks with visual contamination in mining operations.

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DL-Net: Sparsity Prior Learning for Grasp Pattern Recognition

Cited by 2 publications

References 33 publications

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

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

Fast Rock Detection in Visually Contaminated Mining Environments Using Machine Learning and Deep Learning Techniques

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