AIMC Topic: Gestures

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[Gesture accuracy recognition based on grayscale image of surface electromyogram signal and multi-view convolutional neural network].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
This study aims to address the limitations in gesture recognition caused by the susceptibility of temporal and frequency domain feature extraction from surface electromyography signals, as well as the low recognition rates of conventional classifiers...

EMGCipher: Decoding Electromyography for Upper-limb Gesture Classification with Explainable AI for Resource Optimization.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Assistive limb devices often employ surface electromyography (sEMG) and deep learning (DL) models for gesture classification. While DL models effectively classify diverse upper-limb gestures, their decision-making mechanisms often lack transparency. ...

A Graph Neural Network Model for Real-Time Gesture Recognition Based on sEMG Signals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
For seemless control of advanced hand prostheses and augmented reality, accurate and immediate hand gestures recognition is essential. Surface electromyography (sEMG) signals obtained from the forearm are commonly employed for this purpose. In this p...

EMG gesture signal analysis towards diagnosis of upper limb using dual-pathway convolutional neural network.

Mathematical biosciences and engineering : MBE
This research introduces a novel dual-pathway convolutional neural network (DP-CNN) architecture tailored for robust performance in Log-Mel spectrogram image analysis derived from raw multichannel electromyography signals. The primary objective is to...

Magnetoencephalogram-based brain-computer interface for hand-gesture decoding using deep learning.

Cerebral cortex (New York, N.Y. : 1991)
Advancements in deep learning algorithms over the past decade have led to extensive developments in brain-computer interfaces (BCI). A promising imaging modality for BCI is magnetoencephalography (MEG), which is a non-invasive functional imaging tech...

Development of an Interactive Total Body Robot Enhanced Imitation Therapy for ASD children.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
Autism is a neurodevelopmental disorder in which the available therapies target the improvement of social skills, in order to ensure a high quality of life for the child. The use of Social Assistive Robots offers new therapeutic possibilities in whic...

Privacy-Preserving British Sign Language Recognition Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Sign language is a means of communication between the deaf community and normal hearing people who use hand gestures, facial expressions, and body language to communicate. It has the same level of complexity as spoken language, but it does not employ...

An ISB-consistent Denavit-Hartenberg model of the human upper limb for joint kinematics optimization: validation on synthetic and robot data during a typical rehabilitation gesture.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Several biomedical contexts such as diagnosis, rehabilitation, and ergonomics require an accurate estimate of human upper limbs kinematics. Wearable inertial measurement units (IMU s) represent a suitable solution because of their unobtrusiveness, po...

Comparison of Data Classification Results for Leap Motion Recovery Gestures.

Studies in health technology and informatics
Static and dynamic gestures are frequently used in activities supporting learning, recovery healthcare, engineering, and 3D games to increase the interactivity between man and machine. The gestures are detected via hardware devices and data is proces...

Optimizing Input for Gesture Recognition using Convolutional Networks on HD-sEMG Instantaneous Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Hand gesture recognition using high-density surface electromyography (HD-sEMG) has gained increasing attention recently due its advantages of high spatio-temporal resolution. Convolutional neural networks (CNN) have also recently been implemented to ...