Progress and prospects of the human–robot collaboration

A Ajoudani, AM Zanchettin, S Ivaldi, A Albu-Schäffer… - Autonomous …, 2018 - Springer
Recent technological advances in hardware design of the robotic platforms enabled the
implementation of various control modalities for improved interactions with humans and …

Emerging wearable interfaces and algorithms for hand gesture recognition: A survey

S Jiang, P Kang, X Song, BPL Lo… - IEEE Reviews in …, 2021 - ieeexplore.ieee.org
Hands are vital in a wide range of fundamental daily activities, and neurological diseases
that impede hand function can significantly affect quality of life. Wearable hand gesture …

[HTML][HTML] Gesture recognition using surface electromyography and deep learning for prostheses hand: state-of-the-art, challenges, and future

W Li, P Shi, H Yu - Frontiers in neuroscience, 2021 - frontiersin.org
Amputation of the upper limb brings heavy burden to amputees, reduces their quality of life,
and limits their performance in activities of daily life. The realization of natural control for …

[HTML][HTML] Comparison of six electromyography acquisition setups on hand movement classification tasks

S Pizzolato, L Tagliapietra, M Cognolato, M Reggiani… - PloS one, 2017 - journals.plos.org
Hand prostheses controlled by surface electromyography are promising due to the non-
invasive approach and the control capabilities offered by machine learning. Nevertheless …

The extraction of neural information from the surface EMG for the control of upper-limb prostheses: emerging avenues and challenges

D Farina, N Jiang, H Rehbaum… - … on Neural Systems …, 2014 - ieeexplore.ieee.org
Despite not recording directly from neural cells, the surface electromyogram (EMG) signal
contains information on the neural drive to muscles, ie, the spike trains of motor neurons …

Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use.

E Scheme, K Englehart - Journal of Rehabilitation Research …, 2011 - search.ebscohost.com
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 …

EMG‐based estimation of limb movement using deep learning with recurrent convolutional neural networks

P Xia, J Hu, Y Peng - Artificial organs, 2018 - Wiley Online Library
A novel model based on deep learning is proposed to estimate kinematic information for
myoelectric control from multi‐channel electromyogram (EMG) signals. The neural …

[HTML][HTML] Current state of digital signal processing in myoelectric interfaces and related applications

M Hakonen, H Piitulainen, A Visala - Biomedical Signal Processing and …, 2015 - Elsevier
This review discusses the critical issues and recommended practices from the perspective of
myoelectric interfaces. The major benefits and challenges of myoelectric interfaces are …

Control of upper limb prostheses: Terminology and proportional myoelectric control—A review

A Fougner, Ø Stavdahl, PJ Kyberd… - … on neural systems …, 2012 - ieeexplore.ieee.org
The recent introduction of novel multifunction hands as well as new control paradigms
increase the demand for advanced prosthetic control systems. In this context, an …

Myoelectric control of artificial limbs—is there a need to change focus?[In the spotlight]

N Jiang, S Dosen, KR Muller… - IEEE Signal Processing …, 2012 - ieeexplore.ieee.org
In this article, the basic concept of myoelectric control and the state of the art in both industry
and academia will be presented. It will emerge that there is a gap between industrial and …