Assistive pointer device for limb impaired people: A novel Frontier Point Method for hand movement recognition

2019 ◽  
Vol 98 ◽  
pp. 650-659 ◽  
Author(s):  
Rajalakshmi Krishnamurthi ◽  
Rizwan Patan ◽  
Amir H. Gandomi
Keyword(s):  
Hand Movement ◽  
Point Method ◽  
Frontier Point ◽  
Movement Recognition ◽  
Impaired People

A Motion Capture System for Hand Movement Recognition

2021 ◽  
pp. 114-121
Author(s):  
Graciela Rodríguez-Vega ◽  
Dora Aydee Rodríguez-Vega ◽  
Xiomara Penelope Zaldívar-Colado ◽  
Ulises Zaldívar-Colado ◽  
Rafael Castillo-Ortega
Keyword(s):  
Motion Capture ◽  
Hand Movement ◽  
Motion Capture System ◽  
Movement Recognition

scholarly journals SEMG Feature Extraction Based on Stockwell Transform Improves Hand Movement Recognition Accuracy

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4457 ◽  
Author(s):  
She ◽  
Zhu ◽  
Tian ◽  
Wang ◽  
Yokoi ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Frequency Analysis ◽  
Recognition Accuracy ◽  
Hand Movement ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis Methods ◽  
Movement Recognition ◽  
S Transform

Feature extraction, as an important method for extracting useful information from surfaceelectromyography (SEMG), can significantly improve pattern recognition accuracy. Time andfrequency analysis methods have been widely used for feature extraction, but these methods analyzeSEMG signals only from the time or frequency domain. Recent studies have shown that featureextraction based on time-frequency analysis methods can extract more useful information fromSEMG signals. This paper proposes a novel time-frequency analysis method based on the Stockwelltransform (S-transform) to improve hand movement recognition accuracy from forearm SEMGsignals. First, the time-frequency analysis method, S-transform, is used for extracting a feature vectorfrom forearm SEMG signals. Second, to reduce the amount of calculations and improve the runningspeed of the classifier, principal component analysis (PCA) is used for dimensionality reduction of thefeature vector. Finally, an artificial neural network (ANN)-based multilayer perceptron (MLP) is usedfor recognizing hand movements. Experimental results show that the proposed feature extractionbased on the S-transform analysis method can improve the class separability and hand movementrecognition accuracy compared with wavelet transform and power spectral density methods.

scholarly journals Using wrist vibrations to guide hand movement and whole body navigation

i-com ◽  
2014 ◽  
Vol 13 (3) ◽  
Author(s):  
Anke Brock ◽  
Slim Kammoun ◽  
Marc Macé ◽  
Christophe Jouffrais
Keyword(s):  
Virtual Environment ◽  
Visually Impaired ◽  
Hand Movement ◽  
Tactile Feedback ◽  
Whole Body ◽  
Blind People ◽  
Wizard Of Oz ◽  
Audio Cues ◽  
Visually Impaired People ◽  
Impaired People

SummaryIn the absence of vision, mobility and orientation are challenging. Audio and tactile feedback can be used to guide visually impaired people. In this paper, we present two complementary studies on the use of vibrational cues for hand guidance during the exploration of itineraries on a map, and whole body-guidance in a virtual environment. Concretely, we designed wearable Arduino bracelets integrating a vibratory motor producing multiple patterns of pulses. In a first study, this bracelet was used for guiding the hand along unknown routes on an interactive tactile map. A wizard-of-Oz study with six blindfolded participants showed that tactons, vibrational patterns, may be more efficient than audio cues for indicating directions. In a second study, this bracelet was used by blindfolded participants to navigate in a virtual environment. The results presented here show that it is possible to significantly decrease travel distance with vibrational cues. To sum up, these preliminary but complementary studies suggest the interest of vibrational feedback in assistive technology for mobility and orientation for blind people.

scholarly journals Locomo-Net: A Low -Complex Deep Learning Framework for sEMG-Based Hand Movement Recognition for Prosthetic Control

2020 ◽  
Vol 8 ◽  
pp. 1-12 ◽  
Author(s):  
Arvind Gautam ◽  
Madhuri Panwar ◽  
Archana Wankhede ◽  
Sridhar P. Arjunan ◽  
Ganesh R. Naik ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hand Movement ◽  
Prosthetic Control ◽  
Learning Framework ◽  
Movement Recognition ◽  
Low Complex
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