Biomedical physics & engineering express
Oct 3, 2025
This paper proposes a novel gesture prediction method for accurately predicting hand gesture types from raw sEMG signals in real time. First, we utilize a linear combination of the mean and standard deviation of sEMG signals within a sliding window t...
Wrist electromyography (EMG) is emerging as an enticing wearable input modality for human-machine interaction. Traditionally recorded from the forearm for use in transradial prostheses, wrist-based EMG sensors are now being integrated into devices su...
Communication between deaf or mute individuals and hearing persons is often hindered by the lack of mutual understanding of sign or vocal language. To bridge this gap, Indian Sign Language Recognition (ISLR) systems are essential. This paper proposes...
Sign language (SL) is a significant communication method for individuals with hearing impairments, using hand gestures to convey letters, words, and sentences. However, several people are unfamiliar with SL, creating a communication gap. An intellige...
Developing intelligent robots with integrated sensing capabilities is critical for advanced manufacturing, medical robots, and embodied intelligence. Existing robotic sensing technologies are limited to recording of acceleration, driving torque, pres...
Journal of colloid and interface science
Sep 6, 2025
Developing high-performance wearable flexible sensors that can adapt well to complex environments has become a hotspot. Herein, a polyvinyl alcohol based composite hydrogel sensor with high mechanical strength, desirable frost/swelling resistance, an...
Gesture recognition based on surface electromyography (sEMG) plays a crucial role in human-computer interaction. By analyzing sEMG signals generated from residual forearm muscle activity in trans-radial amputees, it is possible to predict their hand ...
Surgical gestures analysis is a promising method to assess surgical procedure quality, but manual annotation is time-consuming. We aimed to develop a recurrent neural network for automated surgical gesture annotation using simulated robot-assisted ra...
. Upper-limb gesture identification is an important problem in the advancement of robotic prostheses. Prevailing research into classifying electromyographic (EMG) muscular data or electroencephalographic (EEG) brain data for this purpose is often lim...
The American Sign Language Recognition Dataset is a pivotal resource for research in visual-gestural languages for American Sign Language and Sign-Language MNIST Dataset. The dataset contains over 64,000 images meticulously labeled with the correspon...
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