Browse
Submit Data
Databases
API
Help

Dataset Information

21 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove.

ABSTRACT: Sign language recognition, especially the sentence recognition, is of great significance for lowering the communication barrier between the hearing/speech impaired and the non-signers. The general glove solutions, which are employed to detect motions of our dexterous hands, only achieve recognizing discrete single gestures (i.e., numbers, letters, or words) instead of sentences, far from satisfying the meet of the signers' daily communication. Here, we propose an artificial intelligence enabled sign language recognition and communication system comprising sensing gloves, deep learning block, and virtual reality interface. Non-segmentation and segmentation assisted deep learning model achieves the recognition of 50 words and 20 sentences. Significantly, the segmentation approach splits entire sentence signals into word units. Then the deep learning model recognizes all word elements and reversely reconstructs and recognizes sentences. Furthermore, new/never-seen sentences created by new-order word elements recombination can be recognized with an average correct rate of 86.67%. Finally, the sign language recognition results are projected into virtual space and translated into text and audio, allowing the remote and bidirectional communication between signers and non-signers.

SUBMITTER: Wen F

PROVIDER: S-EPMC8433305 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

A smart glove with integrated triboelectric nanogenerator for self-powered gesture recognition and language expression.

Project description:Flexible electronics with great functional characteristics have proved to be a stepping stone in the field of wearable devices. Amongst all, gesture-sensing techniques have been widely studied for human-machine interfaces. In this paper, we propose a self-powered gesture-sensing system attached to the back of the hands, which has the capability of distinguishing hand gestures by measuring the triboelectric nanogenerator output signal. By attaching the sensor on the back of the hand, we can sense the displacement of tendons to detect the gestures. In addition, humidity resistance and durability of the device were tested and validated. Furthermore, we have established a set of rules to define the relationship between gestures and corresponding English letters. Therefore, the proposed sensor can further serve as an electronic sign language translator by converting gestures into words. Finally, we can integrate this system into gloves to enhance the applicability and utility. Overall, we have developed a real-time self-powered back-of-hand sensing system which can recognize various hand gestures.

| S-EPMC6781458 | biostudies-literature

Full-Fiber Auxetic-Interlaced Yarn Sensor for Sign-Language Translation Glove Assisted by Artificial Neural Network.

Project description:Yarn sensors have shown promising application prospects in wearable electronics owing to their shape adaptability, good flexibility, and weavability. However, it is still a critical challenge to develop simultaneously structure stable, fast response, body conformal, mechanical robust yarn sensor using full microfibers in an industrial-scalable manner. Herein, a full-fiber auxetic-interlaced yarn sensor (AIYS) with negative Poisson's ratio is designed and fabricated using a continuous, mass-producible, structure-programmable, and low-cost spinning technology. Based on the unique microfiber interlaced architecture, AIYS simultaneously achieves a Poisson's ratio of-1.5, a robust mechanical property (0.6 cN/dtex), and a fast train-resistance responsiveness (0.025 s), which enhances conformality with the human body and quickly transduce human joint bending and/or stretching into electrical signals. Moreover, AIYS shows good flexibility, washability, weavability, and high repeatability. Furtherly, with the AIYS array, an ultrafast full-letter sign-language translation glove is developed using artificial neural network. The sign-language translation glove achieves an accuracy of 99.8% for all letters of the English alphabet within a short time of 0.25 s. Furthermore, owing to excellent full letter-recognition ability, real-time translation of daily dialogues and complex sentences is also demonstrated. The smart glove exhibits a remarkable potential in eliminating the communication barriers between signers and non-signers.

| S-EPMC9249965 | biostudies-literature

Blockchain and smart contract for IoT enabled smart agriculture.

Project description:The agricultural sector is still lagging behind from all other sectors in terms of using the newest technologies. For production, the latest machines are being introduced and adopted. However, pre-harvest and post-harvest processing are still done by following traditional methodologies while tracing, storing, and publishing agricultural data. As a result, farmers are not getting deserved payment, consumers are not getting enough information before buying their product, and intermediate person/processors are increasing retail prices. Using blockchain, smart contracts, and IoT devices, we can fully automate the process while establishing absolute trust among all these parties. In this research, we explored the different aspects of using blockchain and smart contracts with the integration of IoT devices in pre-harvesting and post-harvesting segments of agriculture. We proposed a system that uses blockchain as the backbone while IoT devices collect data from the field level, and smart contracts regulate the interaction among all these contributing parties. The system implementation has been shown in diagrams and with proper explanations. Gas costs of every operation have also been attached for a better understanding of the costs. We also analyzed the system in terms of challenges and advantages. The overall impact of this research was to show the immutable, available, transparent, and robustly secure characteristics of blockchain in the field of agriculture while also emphasizing the vigorous mechanism that the collaboration of blockchain, smart contract, and IoT presents.

| S-EPMC8022535 | biostudies-literature

Wear-Resistant Smart Textiles Using Nylon-11 Triboelectric Yarns.

Project description:The ever-increasing demand for self-powered systems such as glucose biosensors and mixed reality devices has sparked significant interest in triboelectric generators, which hold large potential as renewable energy solutions. Our study explores new methods for integrating energy-harvesting capabilities into smart textiles by developing strong and efficient yarns that can convert mechanical energy into electrical energy through a triboelectric effect. Specifically, we focused on Nylon-11 (PA11), a material known for its crystalline structure well-suited for generating a powerful triboelectric response. To achieve this, we created triboelectric yarns by electrospinning PA11 fibers onto conductive carbon yarns, enabling energy-harvesting applications. Extensive testing demonstrated that these yarns possess exceptional durability, surpassing real-life usage requirements while experiencing minimal degradation. Additionally, we developed a prototype haptic device by interweaving tribopositive PA11 and tribonegative poly(vinylidene fluoride) (PVDF) triboelectric yarns. Our research has successfully yielded durable and efficient yarns with strong energy-harvesting capabilities, opening up possibilities for integrating smart textiles into practical scenarios. These technologies are promising steps to achieve greener and more reliable self-powered systems.

| S-EPMC10711711 | biostudies-literature

Biowaste Eggshell Membranes for Bio-triboelectric Nanogenerators and Smart Sensors.

Project description:In this study, we used a simple and cost-effective method to fabricate triboelectric nanogenerators (TENGs) based on biowaste eggshell membranes (EMs). We prepared stretchable electrodes with various types of EMs (hen, duck, goose, and ostrich) and employed them as positive friction materials for bio-TENGs. A comparison of the electrical properties of the hen, duck, goose, and ostrich EMs revealed that the output voltage of the ostrich EM could reach up to 300 V, due to its abundant functional groups, natural fiber structure, high surface roughness, high surface charge, and high dielectric constant. The output power of the resulting device reached 0.18 mW, sufficient to power 250 red light-emitting diodes simultaneously, as well as a digital watch. This device also displayed good durability when subjected to 9000 cycles at 30 N at a frequency of 3 Hz. Furthermore, we designed an ostrich EM-TENG as a smart sensor for the detection of body motion, including leg movement and the pressing of different numbers of fingers.

| S-EPMC9948195 | biostudies-literature

Visual cortex entrains to sign language.

Project description:Despite immense variability across languages, people can learn to understand any human language, spoken or signed. What neural mechanisms allow people to comprehend language across sensory modalities? When people listen to speech, electrophysiological oscillations in auditory cortex entrain to slow ([Formula: see text]8 Hz) fluctuations in the acoustic envelope. Entrainment to the speech envelope may reflect mechanisms specialized for auditory perception. Alternatively, flexible entrainment may be a general-purpose cortical mechanism that optimizes sensitivity to rhythmic information regardless of modality. Here, we test these proposals by examining cortical coherence to visual information in sign language. First, we develop a metric to quantify visual change over time. We find quasiperiodic fluctuations in sign language, characterized by lower frequencies than fluctuations in speech. Next, we test for entrainment of neural oscillations to visual change in sign language, using electroencephalography (EEG) in fluent speakers of American Sign Language (ASL) as they watch videos in ASL. We find significant cortical entrainment to visual oscillations in sign language <5 Hz, peaking at [Formula: see text]1 Hz. Coherence to sign is strongest over occipital and parietal cortex, in contrast to speech, where coherence is strongest over the auditory cortex. Nonsigners also show coherence to sign language, but entrainment at frontal sites is reduced relative to fluent signers. These results demonstrate that flexible cortical entrainment to language does not depend on neural processes that are specific to auditory speech perception. Low-frequency oscillatory entrainment may reflect a general cortical mechanism that maximizes sensitivity to informational peaks in time-varying signals.

| S-EPMC5474824 | biostudies-other

ArASL: Arabic Alphabets Sign Language Dataset.

Project description:A fully-labelled dataset of Arabic Sign Language (ArSL) images is developed for research related to sign language recognition. The dataset will provide researcher the opportunity to investigate and develop automated systems for the deaf and hard of hearing people using machine learning, computer vision and deep learning algorithms. The contribution is a large fully-labelled dataset for Arabic Sign Language (ArSL) which is made publically available and free for all researchers. The dataset which is named ArSL2018 consists of 54,049 images for the 32 Arabic sign language sign and alphabets collected from 40 participants in different age groups. Different dimensions and different variations were present in images which can be cleared using pre-processing techniques to remove noise, center the image, etc. The dataset is made available publicly at https://data.mendeley.com/datasets/y7pckrw6z2/1.

| S-EPMC6661066 | biostudies-literature

Syntactic priming in American Sign Language.

Project description:Psycholinguistic studies of sign language processing provide valuable opportunities to assess whether language phenomena, which are primarily studied in spoken language, are fundamentally shaped by peripheral biology. For example, we know that when given a choice between two syntactically permissible ways to express the same proposition, speakers tend to choose structures that were recently used, a phenomenon known as syntactic priming. Here, we report two experiments testing syntactic priming of a noun phrase construction in American Sign Language (ASL). Experiment 1 shows that second language (L2) signers with normal hearing exhibit syntactic priming in ASL and that priming is stronger when the head noun is repeated between prime and target (the lexical boost effect). Experiment 2 shows that syntactic priming is equally strong among deaf native L1 signers, deaf late L1 learners, and hearing L2 signers. Experiment 2 also tested for, but did not find evidence of, phonological or semantic boosts to syntactic priming in ASL. These results show that despite the profound differences between spoken and signed languages in terms of how they are produced and perceived, the psychological representation of sentence structure (as assessed by syntactic priming) operates similarly in sign and speech.

| S-EPMC4364966 | biostudies-literature

The Uniformity and Diversity of Language: Evidence from Sign Language.

Project description:Evidence from sign language strongly supports three positions: (1) language is a coherent system with universal properties; (2) sign languages diverge from spoken languages in some aspects of their structure; and (3) domain-external factors can be identified that account for some crucial aspects of language structure -- uniform and diverse -- in both modalities. Assuming that any of these positions excludes the others defeats the purpose of the enterprise.

| S-EPMC2976059 | biostudies-literature

Smart pooling: AI-powered COVID-19 informative group testing.

Project description:Massive molecular testing for COVID-19 has been pointed out as fundamental to moderate the spread of the pandemic. Pooling methods can enhance testing efficiency, but they are viable only at low incidences of the disease. We propose Smart Pooling, a machine learning method that uses clinical and sociodemographic data from patients to increase the efficiency of informed Dorfman testing for COVID-19 by arranging samples into all-negative pools. To do this, we ran an automated method to train numerous machine learning models on a retrospective dataset from more than 8000 patients tested for SARS-CoV-2 from April to July 2020 in Bogotá, Colombia. We estimated the efficiency gains of using the predictor to support Dorfman testing by simulating the outcome of tests. We also computed the attainable efficiency gains of non-adaptive pooling schemes mathematically. Moreover, we measured the false-negative error rates in detecting the ORF1ab and N genes of the virus in RT-qPCR dilutions. Finally, we presented the efficiency gains of using our proposed pooling scheme on proof-of-concept pooled tests. We believe Smart Pooling will be efficient for optimizing massive testing of SARS-CoV-2.

| S-EPMC9020431 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data