Non-invasive human-machine interface (HMI) systems with hybrid on-body sensors for controlling upper-limb prosthesis: A review
H Zhou, G Alici - IEEE Sensors Journal, 2022 - ieeexplore.ieee.org
In this work, we present a systematic review on non-invasive HMIs employing hybrid
wearable sensor modalities for recognition of upper limb intentions. Different combinations …
wearable sensor modalities for recognition of upper limb intentions. Different combinations …
A survey of sensor fusion methods in wearable robotics
Modern wearable robots are not yet intelligent enough to fully satisfy the demands of end-
users, as they lack the sensor fusion algorithms needed to provide optimal assistance and …
users, as they lack the sensor fusion algorithms needed to provide optimal assistance and …
A systematic review of sensor fusion methods using peripheral bio-signals for human intention decoding
A Dwivedi, H Groll, P Beckerle - Sensors, 2022 - mdpi.com
Humans learn about the environment by interacting with it. With an increasing use of
computer and virtual applications as well as robotic and prosthetic devices, there is a need …
computer and virtual applications as well as robotic and prosthetic devices, there is a need …
Movement error rate for evaluation of machine learning methods for sEMG-based hand movement classification
There has been increasing interest in applying learning algorithms to improve the dexterity
of myoelectric prostheses. In this work, we present a large-scale benchmark evaluation on …
of myoelectric prostheses. In this work, we present a large-scale benchmark evaluation on …
sEMG-based gesture recognition with convolution neural networks
The traditional classification methods for limb motion recognition based on sEMG have been
deeply researched and shown promising results. However, information loss during feature …
deeply researched and shown promising results. However, information loss during feature …
Improving robotic hand prosthesis control with eye tracking and computer vision: A multimodal approach based on the visuomotor behavior of grasping
The complexity and dexterity of the human hand make the development of natural and
robust control of hand prostheses challenging. Although a large number of control …
robust control of hand prostheses challenging. Although a large number of control …
Classification of upper limb phantom movements in transhumeral amputees using electromyographic and kinematic features
Recent studies have shown the ability of transhumeral amputees to generate surface
electromyography (sEMG) patterns associated to distinct phantom limb movements of the …
electromyography (sEMG) patterns associated to distinct phantom limb movements of the …
A novel multi-feature fusion network with spatial partitioning strategy and cross-attention for armband-based gesture recognition
Effectively integrating the time-space-frequency information of multi-modal signals from
armband sensor, including surface electromyogram (sEMG) and accelerometer data, is …
armband sensor, including surface electromyogram (sEMG) and accelerometer data, is …
Hand gesture recognition based on deep learning method
The classical classification method usually consists preprocessing, windowing, feature
extraction and classification. Despite the promising performance have been shown in recent …
extraction and classification. Despite the promising performance have been shown in recent …
Simultaneous estimation of multi-finger forces by surface electromyography and accelerometry signals
Myoelectric prostheses generally use pattern recognition strategies to decode users' gesture
intention; however, they lack intuitive force control. Regression strategy can extract force …
intention; however, they lack intuitive force control. Regression strategy can extract force …