[HTML][HTML] Real-time EMG based pattern recognition control for hand prostheses: A review on existing methods, challenges and future implementation

N Parajuli, N Sreenivasan, P Bifulco, M Cesarelli… - Sensors, 2019 - mdpi.com
Upper limb amputation is a condition that significantly restricts the amputees from performing
their daily activities. The myoelectric prosthesis, using signals from residual stump muscles …

[HTML][HTML] Wearable technology in stroke rehabilitation: towards improved diagnosis and treatment of upper-limb motor impairment

P Maceira-Elvira, T Popa, AC Schmid… - … of neuroengineering and …, 2019 - Springer
Stroke is one of the main causes of long-term disability worldwide, placing a large burden on
individuals and society. Rehabilitation after stroke consists of an iterative process involving …

Deep learning for electromyographic hand gesture signal classification using transfer learning

U Côté-Allard, CL Fall, A Drouin… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
In recent years, deep learning algorithms have become increasingly more prominent for
their unparalleled ability to automatically learn discriminant features from large amounts of …

[HTML][HTML] Deep learning with convolutional neural networks applied to electromyography data: A resource for the classification of movements for prosthetic hands

M Atzori, M Cognolato, H Müller - Frontiers in neurorobotics, 2016 - frontiersin.org
Natural control methods based on surface electromyography (sEMG) and pattern
recognition are promising for hand prosthetics. However, the control robustness offered by …

[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 …

[HTML][HTML] Electromyography data for non-invasive naturally-controlled robotic hand prostheses

M Atzori, A Gijsberts, C Castellini, B Caputo… - Scientific data, 2014 - nature.com
Recent advances in rehabilitation robotics suggest that it may be possible for hand-
amputated subjects to recover at least a significant part of the lost hand functionality. The …

[HTML][HTML] Self-recalibrating surface EMG pattern recognition for neuroprosthesis control based on convolutional neural network

X Zhai, B Jelfs, RHM Chan, C Tin - Frontiers in neuroscience, 2017 - frontiersin.org
Hand movement classification based on surface electromyography (sEMG) pattern
recognition is a promising approach for upper limb neuroprosthetic control. However …

[HTML][HTML] Surface electromyography signal processing and classification techniques

RH Chowdhury, MBI Reaz, MABM Ali, AAA Bakar… - Sensors, 2013 - mdpi.com
Electromyography (EMG) signals are becoming increasingly important in many applications,
including clinical/biomedical, prosthesis or rehabilitation devices, human machine …

[HTML][HTML] Feature extraction and selection for myoelectric control based on wearable EMG sensors

A Phinyomark, R N. Khushaba, E Scheme - Sensors, 2018 - mdpi.com
Specialized myoelectric sensors have been used in prosthetics for decades, but, with recent
advancements in wearable sensors, wireless communication and embedded technologies …

Myoelectric control of prosthetic hands: state-of-the-art review

P Geethanjali - Medical Devices: Evidence and Research, 2016 - Taylor & Francis
Myoelectric signals (MES) have been used in various applications, in particular, for
identification of user intention to potentially control assistive devices for amputees, orthotic …