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Hand

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sEMG-Based Gesture Recognition Using Deep Learning From Noisy Labels.

IEEE journal of biomedical and health informatics
Gesture recognition for myoelectric prosthesis control utilizing sparse multichannel surface Electromyography (sEMG) is a challenging task, and from a Muscle-Computer Interface (MCI) standpoint, the performance is still far from optimal. However, the...

A Novel Wearable Soft Glove for Hand Rehabilitation and Assistive Grasping.

Sensors (Basel, Switzerland)
In order to assist patients with finger rehabilitation training and grasping objects, we propose a new type of soft rehabilitation gloves (SRGs), which has both flexion/extension and abduction/adduction movement function for every finger. This paper ...

Discriminating Free Hand Movements Using Support Vector Machine and Recurrent Neural Network Algorithms.

Sensors (Basel, Switzerland)
Decoding natural hand movements is of interest for human-computer interaction and may constitute a helpful tool in the diagnosis of motor diseases and rehabilitation monitoring. However, the accurate measurement of complex hand movements and the deco...

A Dual-Branch Self-Boosting Framework for Self-Supervised 3D Hand Pose Estimation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Although 3D hand pose estimation has made significant progress in recent years with the development of the deep neural network, most learning-based methods require a large amount of labeled data that is time-consuming to collect. In this paper, we pr...

Human Grasp Mechanism Understanding, Human-Inspired Grasp Control and Robotic Grasping Planning for Agricultural Robots.

Sensors (Basel, Switzerland)
As the end execution tool of agricultural robots, the manipulator directly determines whether the grasping task can be successfully completed. The human hand can adapt to various objects and achieve stable grasping, which is the highest goal for mani...

EEGSym: Overcoming Inter-Subject Variability in Motor Imagery Based BCIs With Deep Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
In this study, we present a new Deep Learning (DL) architecture for Motor Imagery (MI) based Brain Computer Interfaces (BCIs) called EEGSym. Our implementation aims to improve previous state-of-the-art performances on MI classification by overcoming ...

Analysis of the Leap Motion Controller's Performance in Measuring Wrist Rehabilitation Tasks Using an Industrial Robot Arm Reference.

Sensors (Basel, Switzerland)
The Leap Motion Controller (LMC) is a low-cost markerless optical sensor that performs measurements of various parameters of the hands that has been investigated for a wide range of different applications. Research attention still needs to focus on t...

Design and Research of an Underactuated Manipulator Based on the Metamorphic Mechanism.

Sensors (Basel, Switzerland)
Robot hands play an important role in the interaction between robots and the environment, and the precision and complexity of their tasks in work production are becoming higher and higher. However, because the traditional manipulator has too many dri...

Analysis of Neural Network Based Proportional Myoelectric Hand Prosthesis Control.

IEEE transactions on bio-medical engineering
OBJECTIVE: We show that state-of-the-art deep neural networks achieve superior results in regression-based multi-class proportional myoelectric hand prosthesis control than two common baseline approaches, and we analyze the neural network mapping to ...

Sensing, Actuating, and Interacting Through Passive Body Dynamics: A Framework for Soft Robotic Hand Design.

Soft robotics
Robotic hands have long strived to reach the performance of human hands. The physical complexity and extraordinary capabilities of the human hand, in terms of sensing, actuation, and cognitive abilities, make achieving this goal challenging. At the h...