The DOI matches its Crossref record; 73 of the 73 references it cites were found in a registry or an index; none of the 3 sources its claims cite is checked for support yet.
AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove
Open accessAbstract
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.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The paper presents an AI-enabled sign language recognition and communication system using a 15-channel triboelectric smart glove, deep learning, and a VR interface. It recognizes 50 words and 20 sentences, and with segmentation can reconstruct sentences and recognize new/never-seen sentences at an average correct rate of 86.67%.
Key findings
- Nonsegmentation CNN achieved 91.3% accuracy for 50 words and 95% for sentences; the abstract/discussion report 20 sentences, while Results and Fig. 3j describe 17 sentences.
- Segmentation with a hierarchy classifier achieved 82.81% accuracy for word fragments and 85.58% average correct rate for sentence reconstruction; the single classifier achieved 81.9% and 79.41%, respectively.
- New/never-seen sentences created by recombining learned word units in new orders were recognized at 86.67% average correct rate with the hierarchy classifier, versus 60% with the single classifier.
- The glove uses 15 triboelectric sensors; hand motion accounts for 43% of sign language motions, with finger bending (56%), wrist motion (18%), fingertip touch (16%), and palm interaction (10%) among hand motions.
- The VR interface enables bidirectional communication: sign language is translated into text/audio and sent via TCP/IP to a nonsigner, who can type responses; a greeting conversation between avatars Lily and Mary is demonstrated.
Methods
- Triboelectric sensors were fabricated from Ecoflex and wrinkled nitrile with conductive textile electrodes, encapsulated, and stitched/glued onto a cotton glove; 15 sensors were placed on fingers, wrists, right index/middle fingertips, and left palm.
- For nonsegmentation, 100 samples per word and sentence were collected, with 80% training and 20% testing, yielding 5000 word samples and 2000 sentence samples. For segmentation, 50 samples per sentence for 17 sentences (850 total) were split with a 200-point sliding window at 50-point steps, producing 13 elements per 800-point sentence; 60%/20%/20% were used for training/validation/testing, and 5 samples for 3 new sentences tested new-sentence recognition.
- Main analysis used an optimized 1D CNN (5 kernels, 64 filters, 4 convolutional layers), PCA and t-SNE for visualization, single and hierarchy classifiers for segmentation, and a Unity VR interface with TCP/IP client–server communication.
Limitations
- No dedicated Limitations section is present. The paper states that the nonsegmentation method cannot distinguish new/never-seen sentences even when they contain the same word elements in a different order, and it introduces recognition time latency; the segmentation method has compromised recognition accuracy.
- The paper states the demonstration is limited to 50 words and 20 sentences from an American Sign Language phrase book and says the database can be broadened; new-sentence recognition reaches 86.67%, not perfect, and the single classifier is lower at 60%.
- Evident from the text: the VR communication demonstration is a greeting scenario between two avatars under an identical LAN, so broader real-world deployment and larger user populations are not tested in the paper.
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Claims the paper makes
35 Own result · 13 Own interpretation · 8 No basis given · 3 Not checked yet · 1 Own method
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the mean correlation coefficient 0.53 > 0.3 (threshold value for high similarity) by extracting the average value of 100 data samples and correlating it with the new input, which means the similarity is high between different ‘Get’ data samples.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > Data analysis of signals of 50 words and 20 sentences
plaincorrelationaln = 100- Asserted: passes — The authors put it forward as true.
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In the paper
“Therefore, a high sentence recognition accuracy of 95% is obtained, as indicated in Fig. 3j.”
- Also stated in: § Word and sentence recognition upon nonsegmentation method · plain
“j Confusion map of recognizing 17 sentences.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > Word and sentence recognition upon nonsegmentation method
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- Also stated in: § Word and sentence recognition upon nonsegmentation method · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The system successfully realizes the recognition of 50 words and 20 sentences.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Introduction
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“the sentence can be inversely reconstructed and recognition at an average correct rate of 85.58% within hierarchy classifier.”
- Also stated in: § Introduction · plain
“the whole sentence’s information can be reconstructed and recognized with an accuracy of 85.58% upon established correlation between word units and sentences.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > Word and sentence recognition upon segmentation method
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- Also stated in: § Introduction · plain
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In the paper
“showing an insufficient average correct rate of 79.41%.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > Word and sentence recognition upon segmentation method
plainn = 10- Asserted: passes — The authors put it forward as true.
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In the paper
“The nonsegmentation AI framework achieves high accuracy for 50 words (91.3%) and 20 sentences (95%) by independently identifying word and sentence signals.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Introduction
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“The nonideal consequence for other sentences, such as Y03, Y04, and Y16, is observed as well.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > Word and sentence recognition upon segmentation method
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“a high recognition accuracy of these 50 words is achieved with the value of 91.3%, as shown in the confusion matrix in Fig. 3g.”
- Also stated in: § Introduction · plain
“The nonsegmentation AI framework achieves high accuracy for 50 words (91.3%)”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > Word and sentence recognition upon nonsegmentation method
plainn = 20- Asserted: passes — The authors put it forward as true.
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- Also stated in: § Introduction · plain
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In the paper
“The confusion matrix in Fig. 4e shows an accuracy of 81.9% for the single classifier recognizing word units W01–W20.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > Word and sentence recognition upon segmentation method
plainn = 170- Asserted: passes — The authors put it forward as true.
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In the paper
“the segmentation approach assisted AI brings the capability of recognizing new/never-seen sentences (average correct rate: 86.67%) that are not included in the database and are created by word element recombination in new orders.”
- Also stated in: § Abstract · plain
“new/never-seen sentences created by new-order word elements recombination can be recognized with an average correct rate of 86.67%.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: New/never-seen sentences created by new-order word element recombination are valid sign language sentences that a signer would use.
- Also stated in: § Abstract · plain
- It may state two separate claims as one. 23
- Probably not a substantial claim. 6
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Eventually, it positively contributes to the sentence recognition, which is indicated by the improved average correct rate of 85.58% with a reduced false area in Fig. 4i.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > Word and sentence recognition upon segmentation method
hedgeddirect- Asserted: passes — The authors put it forward as true.
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In the paper
“the voltage output increases when sensor area, force, bending degree, and bending speed increase.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > Glove configuration and sensor characterization
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“The mean correlation coefficient of 0.37 is larger than 0.3, indicating the high signal similarity between ‘Get’ and ‘Must’ as well”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > Data analysis of signals of 50 words and 20 sentences
plaincorrelationaln = 100- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
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In the paper
“hand motions fall into four categories, including finger bending (56%), wrist motion (18%), touch with fingertips (16%), and interaction with palm (10%).”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > Glove configuration and sensor characterization
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“the recognition latency is significantly reduced due to small-distance sliding (50 data points per sliding) as the recognition process will be triggered with each sliding.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > Word and sentence recognition upon segmentation method
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“the 3rd and 5–10th samples of sentence Y01 are wrongly classified with a poor correct rate of 30%.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > Word and sentence recognition upon segmentation method
plainn = 10- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
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In the paper
“It shows the performance of feature clustering is poor with category overlaps before going through the CNN network. After undergoing the feature extraction and classification of CNN, the visualization result realized by t-SNE in Fig. 3f indicates a desirable classification performance of the developed CNN model with clear boundaries among these 50 classes with less overlap.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > Word and sentence recognition upon nonsegmentation method
hedgeddirect- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Then the deep learning model recognizes all word elements and reversely reconstructs and recognizes sentences.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Abstract
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In the paper
“the data analysis proclaims the high similarities of word-to-word and sentence-to-sentence signals.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > Data analysis of signals of 50 words and 20 sentences
plaincorrelational- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“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.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The system enables the separate and independent recognition of words (i.e., single gestures) and sentences (i.e., continuous multiple gestures) with high accuracies of 91.3% and 95% within nonsegmentation frame.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Discussion
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In the paper
“In comparison, the hierarchy classifier achieves precise identification of 5 samples of New1, 3 samples of New2, and 5 samples of New3.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > Recognizing new sentences upon segmentation approach
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In other words, the hierarchy classifier achieves an average correct rate of 86.67% (see Supplementary Fig. 6b), superior to 60% of the single classifier.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > Recognizing new sentences upon segmentation approach
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Moving forward to the translation (i.e., recognition) stage (see Supplementary Fig. 6a), the single classifier correctly predicts 2 samples of New1, 3 samples of New2, 4 samples of New3 (average correct rate of 60%).”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > Recognizing new sentences upon segmentation approach
plainn = 3- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As provided in Supplementary Table 2, both classifiers render the label series prediction for the total 15 inputs of these 3 new sentences, although the CNN model never sees or learns the true label series before.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > Recognizing new sentences upon segmentation approach
plainn = 5- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the first step Fig. 5b(i), the speech-impaired user Lily performs sign language ‘How are you?’, which is recognized and converted to text and audio by the deep learning model.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > VR communication interface for the signer and nonsigner
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In the paper
“Overall, the nonsegmentation approach possesses better performance in the aspect of recognition accuracy either for words or sentences but with the shortcoming of apparent recognition time latency and incapability of new sentence recognition.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > Recognizing new sentences upon segmentation approach
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In the paper
“The VR interface for two-way remote communication, linked with the AI front end for sign language recognition, shows a potential prototype of a future intelligent sign language recognition and communication system.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Introduction
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The long-length signal of sentence provides more distinguishable features than the single-word signal. Therefore, a high sentence recognition accuracy of 95% is obtained”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > Word and sentence recognition upon nonsegmentation method
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Overall, the hierarchy classifier improves both the accuracy of recognizing word elements and that of identifying ever-seen/trained sentences.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > Word and sentence recognition upon segmentation method
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the single classifier may not be effective enough when reconstructing and recognizing sentences that existed in the dataset, which may hinder the identification of new/never-seen sentences.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > Word and sentence recognition upon segmentation method
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“The VR communication interface linked with AI allows interaction of the speech/hearing disordered and healthy people closely and even remotely, providing a promising platform for the immense interactions between two population groups.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > VR communication interface for the signer and nonsigner
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Probably not a substantial claim. It may state two separate claims as one.
- Rests on an assumption: Most current sign language translation systems simply display recognition results on visualized interfaces such as mobile phones and portable displays.
- Rests on an assumption: Most works do not allow interaction between signers and nonsigners.
- Rests on an assumption: There is an acute demand for effective two-way communication between signers and nonsigners.
- Rests on an assumption: The emerging VR interface is an ideal platform for advanced sign language interpretation, visualization, and communication interface.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Finally, the embedded VR interface can act as the bridge of user terminals, transmitting messages back and forth, configuring a closed-loop communication system with gloves and AI component.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Discussion
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: Most current sign language translation systems simply display recognition results on visualized interfaces such as mobile phones and portable displays.
- Rests on an assumption: Most works do not allow interaction between signers and nonsigners.
- Rests on an assumption: There is an acute demand for effective two-way communication between signers and nonsigners.
- Rests on an assumption: The emerging VR interface is an ideal platform for advanced sign language interpretation, visualization, and communication interface.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Besides, the intelligent system consist of smart gloves, AI-assisted recognition, and TCP/IP enabled server-client LAN communication in virtual space, providing a reliable and appealing platform for the seamless and immense interactions between nonsigners and signers either remotely or proximately.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Discussion
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: Most current sign language translation systems simply display recognition results on visualized interfaces such as mobile phones and portable displays.
- Rests on an assumption: Most works do not allow interaction between signers and nonsigners.
- Rests on an assumption: There is an acute demand for effective two-way communication between signers and nonsigners.
- Rests on an assumption: The emerging VR interface is an ideal platform for advanced sign language interpretation, visualization, and communication interface.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The authors affirm that the participant provide informed consent for publication of the images in Figs. 2a, 5b, Supplementary Figs. 2a, 8a, and the videos in Supplementary Movies 1, 2, 3.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Methods > VR communication interface building and demonstration
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Although few works have employed AI-enabled advanced data analytics to achieve highly precise sign language translation, they are still limited to the classification of discrete words that cannot meet the requirement of practical communication in the signer’s daily lives.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Discussion
What the sources say
- Not checked yetcites Supplementary Table 3
plain- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Besides, the small-distance sliding induced recognition brings the potential of the real-time recognition or translation of sentences.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > Recognizing new sentences upon segmentation approach
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
New/never-seen sentences created by new-order word element recombination are valid sign language sentences that a signer would use.
“new/never-seen sentences created by new-order word elements recombination can be recognized with an average correct rate of 86.67%.”
The paper relies on this without support.
The 50 gestures and 20 sentences selected are representative of daily sign language communication.
“we select 50 gestures that are daily used in the signer’s life as demonstration.”
“20 daily sentences are sorted out for subsequent study.”
The paper relies on this without support.
Sign language is not as prevalent as spoken language and is difficult for nonsigners to understand without prior learning.
“sign language is not as prevalent as the speaking language and difficult for nonsigners to understand without prior learning.”
The paper relies on this without support.
Most current sign language translation systems simply display recognition results on visualized interfaces such as mobile phones and portable displays.
“Most current sign language translation systems simply display the recognition result on visualized interfaces such as mobile phones and portable displays.”
The paper relies on this without support.
Most works do not allow interaction between signers and nonsigners.
“since most of works do not allow the interaction between signers and nonsigners.”
The paper relies on this without support.
There is an acute demand for effective two-way communication between signers and nonsigners.
“However, the acute demand of effective two-way communication between the signers and nonsigners is neglected”
The paper relies on this without support.
The emerging VR interface is an ideal platform for advanced sign language interpretation, visualization, and communication interface.
“which would be an ideal platform for an advanced sign language interpretation, visualization, and communication interface.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The deep learning algorithm initially identifies word components, followed by the reconstruction of the original sentences, achieving respective accuracies of 82.81% and 85.58%.”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Wen, F. et al. 2021
In the citing paper
“In addition, the segmentation method opens new avenues for recognizing novel or previously unseen sentences.”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Wen, F. et al. 2021
In the citing paper
“Therefore, as shown in Fig. 9e Wen et al. demonstrate a sign language recognition and communication system based on smart glove sensors (Wen, F. et al. 2021).”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Wen, F. et al. 2021
In the citing paper
“This breakthrough in recognizing both existing and new sentences enhances the practicality of sign language recognition systems, marking a significant step towards minimizing communication obstacles between sign language users and non-signers.”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Wen, F. et al. 2021
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Introduction
“The nonsegmentation AI framework achieves high accuracy for 50 words (91.3%) and 20 sentences (95%) by independently identifying word and sentence signals.”
§ AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove > Results > Word and sentence recognition upon nonsegmentation method
“Therefore, a high sentence recognition accuracy of 95% is obtained, as indicated in Fig. 3j.”
Citations
Cites
Double-slit photoelectron interference in strong-field ionization of the neon dimer
Kunitski, M., Eicke, N., Huber, P., Köhler, J., Zeller, S., Voigtsberger, J., Schlott, N., Henrichs, K., Sann, H., Trinter, F., et al. (2019) · Institutional Repository of Leibniz Universität Hannover (Leibniz Universität Hannover) · doi:10.15488/5174 · Cited by 8,378
Double-slit photoelectron interference in strong-field ionization of the neon dimer
Kunitski, M., Eicke, N., Huber, P., Köhler, J., Zeller, S., Voigtsberger, J., Schlott, N., Henrichs, K., Sann, H., Trinter, F., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-07882-8 · Cited by 8,162
U1 snRNP regulates cancer cell migration and invasion in vitro
Oh, J.-M., Venters, C. C., Di, C., Pinto, A. M., Wan, L., Younis, I., Cai, Z., Arai, C., So, B. R., Duan, J., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-13993-7 · Cited by 7,157
Flexible triboelectric generator
Fan, F.-R., Tian, Z.-Q., & Lin Wang, Z. (2012) · Nano Energy · doi:10.1016/j.nanoen.2012.01.004 · Cited by 6,705
Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis
Gao, W., Emaminejad, S., Nyein, H. Y. Y., Challa, S., Chen, K., Peck, A., Fahad, H. M., Ota, H., Shiraki, H., Kiriya, D., et al. (2016) · Nature · doi:10.1038/nature16521 · Cited by 5,087
Yang, J. C., Mun, J., Kwon, S. Y., Park, S., Bao, Z., & Park, S. (2019) · Advanced Materials · doi:10.1002/adma.201904765 · Cited by 1,641
Grammar, Gesture, and Meaning in American Sign Language
Liddell, S. K. (2003) · Cambridge University Press eBooks · doi:10.1017/cbo9780511615054 · Cited by 1,390
Learning the signatures of the human grasp using a scalable tactile glove
Sundaram, S., Kellnhofer, P., Li, Y., Zhu, J.-Y., Torralba, A., & Matusik, W. (2019) · Nature · doi:10.1038/s41586-019-1234-z · Cited by 1,162
Skin-integrated wireless haptic interfaces for virtual and augmented reality
Yu, X., Xie, Z., Yu, Y., Lee, J., Vazquez-Guardado, A., Luan, H., Ruban, J., Ning, X., Akhtar, A., Li, D., et al. (2019) · Nature · doi:10.1038/s41586-019-1687-0 · Cited by 1,025
Self-citationHergenrother, C. W., Maleszewski, C., Nolan, M. C., Li, J., d’Aubigny, C. D., Shelly, F. C., Howell, E. S., Kareta, T., Izawa, M. R. M., Barucci, M. A., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09213-x · Cited by 902
Cited by
Sorted by citations
Items 101–200 of 507
Zhu, Y., Zhao, T., Sun, F., Jia, C., Ye, H., Jiang, Y., Wang, K., Huang, C., Xie, Y., & Mao, Y. (2023) · Nano Energy · doi:10.1016/j.nanoen.2023.108520 · Cited by 59
A Survey on Haptics: Communication, Sensing and Feedback
Emami, M., Bayat, A., Tafazolli, R., & Quddus, A. U. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3444051 · Cited by 69
Yang, C., Zhang, D., Wang, W., Zhang, H., & Zhou, L. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.159157 · Cited by 59
Transparent Thermally Tunable Microwave Absorber Prototype Based on Patterned VO2 Film
Lu, Z., Zhang, Y., Wang, H., Xia, C., Liu, Y., Dou, S., Li, Y., & Tan, J. (2022) · Engineering · doi:10.1016/j.eng.2022.10.005 · Cited by 58
Li, L., Xu, H. G., Li, Z., Zhong, B., Lou, Z., & Wang, L. (2024) · Advanced Materials · doi:10.1002/adma.202414054 · Cited by 58
Faisal, M. A. A., Abir, F. F., Ahmed, M. U., & Ahad, M. A. R. (2022) · Scientific Reports · doi:10.1038/s41598-022-25108-2 · Cited by 59
Zhang, S., Suresh, L., Yang, J., Zhang, X., & Tan, S. C. (2022) · Advanced Intelligent Systems · doi:10.1002/aisy.202100194 · Cited by 56
Song, T., Jiang, S., Cai, N., & Chen, G. (2023) · Chemical Engineering Journal · doi:10.1016/j.cej.2023.146292 · Cited by 55
Shrestha, K., Pradhan, G. B., Asaduzzaman, M., Reza, M. S., Bhatta, T., Kim, H., Lee, Y., & Park, J. Y. (2023) · Advanced Energy Materials · doi:10.1002/aenm.202302471 · Cited by 55
Li, Y., Sun, Z., Huang, M., Sun, L., Liu, H., & Lee, C. (2024) · Advanced Energy and Sustainability Research · doi:10.1002/aesr.202400116 · Cited by 55
Self-citationGuo, X., Zhang, Z., Ren, Z., Li, D., Xu, C., Wang, L., Liu, W., Zhuge, Y., Zhou, G., & Lee, C. (2025) · Advanced Materials · doi:10.1002/adma.202510417 · Cited by 57
Self-citationDegradable MXene-Doped Polylactic Acid Textiles for Wearable Biomonitoring
Huang, J., Xie, G., Wei, Q., Su, Y., Xu, X., & Jiang, Y. (2022) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.2c18395 · Cited by 55
Nanomaterial-based flexible sensors for metaverse and virtual reality applications
Wang, J., Suo, J., Song, Z., Li, W. J., & Wang, Z. (2023) · International Journal of Extreme Manufacturing · doi:10.1088/2631-7990/acded1 · Cited by 57
Zhou, J. W., Liu, Y., Zhuo, F., Chen, H., Cao, H., Fu, Y., Xie, J., & Duan, H. (2023) · Chemical Engineering Journal · doi:10.1016/j.cej.2023.147790 · Cited by 54
Sun, T., Yao, C., Liu, Z., Huang, S., Huang, X., Zheng, S., Liu, J., Shi, P., Zhang, T., Chen, H., et al. (2024) · Nano Energy · doi:10.1016/j.nanoen.2024.109395 · Cited by 53
Multifunctional Tactile Sensors for Object Recognition
Wang, L., Qi, X., Li, C., & Wang, Y. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202409358 · Cited by 54
A Wearable Multidimensional Motion Sensor for AI-Enhanced VR Sports
Guo, Z. H., Zhang, Z., An, K., He, T., Sun, Z., Pu, X., & Lee, C. (2023) · Research · doi:10.34133/research.0154 · Cited by 53
Self-citationLiang, Q., Zhang, D., He, T., Zhang, Z., Wang, H., Chen, S., & Lee, C. (2023) · ACS Nano · doi:10.1021/acsnano.3c08739 · Cited by 52
Self-citationDeep-Learning-Assisted Underwater 3D Tactile Tensegrity
Xu, P., Zheng, J., Liu, J., Liu, X., Wang, X., Wang, S., Guan, T., Fu, X., Xu, M., Xie, G., et al. (2023) · Research · doi:10.34133/research.0062 · Cited by 51
A survey on sign language literature
Alaghband, M., Maghroor, H. R., & Garibay, I. I. (2023) · Machine Learning with Applications · doi:10.1016/j.mlwa.2023.100504 · Cited by 50
Lu, X. F., Tan, H., Zhang, H., Wang, W., Xie, S., Yue, T., & Chen, F. (2025) · Nature Communications · doi:10.1038/s41467-025-56169-2 · Cited by 50
Li, C., Wang, T., Zhou, S., Sun, Y., Xu, Z., Xu, S., Shu, S., Yi, Z., Jiang, B., Xie, S., et al. (2024) · Research · doi:10.34133/research.0366 · Cited by 49
A noise-tolerant human–machine interface based on deep learning-enhanced wearable sensors
Chen, X., Lou, Z., Gao, X., Yin, L., Qin, S., Lin, M., Zhang, F., Lu, Y., Ding, S., Liu, R., et al. (2025) · Nature Sensors · doi:10.1038/s44460-025-00001-3 · Cited by 49
Self-citationBhatia, D., Lee, K.-S., Niazi, M. U. K., & Park, H. (2022) · Nano Energy · doi:10.1016/j.nanoen.2022.107179 · Cited by 48
Xu, F., Jin, X., Lan, C., Guo, Z. H., Zhou, R., Sun, H., Shao, Y., Meng, J., Liu, Y., & Pu, X. (2023) · Nano Energy · doi:10.1016/j.nanoen.2023.108312 · Cited by 48
Deep‐learning enabled smart insole system aiming for multifunctional foot‐healthcare applications
Tian, Y., Zhang, L., Zhang, C., Bao, B., Li, Q., Wang, L., Song, Z., & Li, D. (2023) · Exploration · doi:10.1002/exp.20230109 · Cited by 49
Lan, B., Yang, T., Tian, G., Ao, Y., Jin, L., Xiong, D., Wang, S., Zhang, H., Deng, L., Sun, Y. M., et al. (2023) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.2c20520 · Cited by 47
Humanoid electronic-skin technology for the era of Artificial Intelligence of Things
Zhang, H., Hong, J., Zhu, J., Duan, S., Xia, M., Chen, J., Sun, B., Xi, M., Gao, F., Xiao, Y., et al. (2025) · Matter · doi:10.1016/j.matt.2025.102136 · Cited by 49
Self-citationA disposable cup inspired smart floor for trajectory recognition and human-interactive sensing
Zhao, L., Zhou, T., Chang, S.-D., Zou, H., Gao, Q., Wu, Z., Yan, G., Wei, K., Yeatman, E. M., Meng, G., et al. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.122524 · Cited by 46
Li, R., Wei, D., & Wang, Z. L. (2024) · Nanomaterials · doi:10.3390/nano14020165 · Cited by 46
Yang, Z., Zhu, Z., Chen, Z., Liu, M., Zhao, B., Liu, Y., Cheng, Z., Wang, S., Yang, W., & Yu, T. (2021) · Sensors · doi:10.3390/s21248422 · Cited by 46
Hou, X., Zhu, M., Sun, L., Ding, T., Huang, Z., Shi, Y., Su, Y., Li, L., Chen, T., & Lee, C. (2021) · Nano Energy · doi:10.1016/j.nanoen.2021.106894 · Cited by 45
Self-citationLiu, C., Ma, F., Sun, Q., Hu, Q., Tong, W., Guo, X., Hu, R., Liu, P., Huang, Y., Hao, X., et al. (2024) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.4c04686 · Cited by 45
Tribophotonics: An emerging self-powered wireless solution toward smart city
Wang, H., Fu, J., Wang, J., Su, L., & Zi, Y. (2022) · Nano Energy · doi:10.1016/j.nanoen.2022.107196 · Cited by 45
AI‐Enabled Metal‐Polymer Plain Bearing Based on the Triboelectric Principle
Gao, M., Sun, T., Li, Y., Zhang, Z., Lee, C., & Choi, J. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202304070 · Cited by 44
Self-citationA wearable system for sign language recognition enabled by a convolutional neural network
Liu, Y., Jiang, X., Yu, X., Ye, H., Ma, C., Wang, W., & Hu, Y. (2023) · Nano Energy · doi:10.1016/j.nanoen.2023.108767 · Cited by 44
Jiang, C., Xu, W., Li, Y., Yu, Z., Wang, L., Hu, X., Xie, Z., Liu, Q., Yang, B., Wang, X., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53654-y · Cited by 46
Intelligent Virtual Reality and Augmented Reality Technologies: An Overview
Λαμπρόπουλος, Γ. (2025) · Future Internet · doi:10.3390/fi17020058 · Cited by 49
Self‐Powered Smart Gloves Based on Triboelectric Nanogenerators
Shen, S., Xiao, X., Yin, J., Xiao, X., & Chen, J. (2022) · Small Methods · doi:10.1002/smtd.202200830 · Cited by 43
Duan, S., Lin, Y., Shi, Q., Wei, X., Zhu, D., Hong, J., Xiang, S., Yuan, W., Shen, G., & Wu, J. (2024) · Advanced Fiber Materials · doi:10.1007/s42765-024-00434-4 · Cited by 43
Intelligent Sensing Technologies Based on Flexible Wearable Sensors: A Review
Nong, H., Jin, M., Pan, C., Zhou, H., Zhang, C., Pan, X., Chen, Y., Wei, X., Lu, Y., Zhao, K., et al. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3405173 · Cited by 44
Self-citationYe, G., Wu, Q., Chen, Y., Wang, X., Xiang, Z., Duan, J., Wan, Y., & Yang, P. (2024) · Advanced Fiber Materials · doi:10.1007/s42765-024-00458-w · Cited by 43
Yang, Y., Chen, Y., Liu, Y., & Yin, R. (2025) · Advanced Fiber Materials · doi:10.1007/s42765-024-00506-5 · Cited by 45
Plastic-Optical-Fiber-Enabled Smart Glove for Machine-Learning-Based Gesture Recognition
Li, J., Liu, B., Hu, Y., Liu, J., He, X., Yuan, J., & Wu, Q. (2023) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2023.3277119 · Cited by 42
Progress in self-powered sensors—Moving toward artificial intelligent and neuromorphic system
Wen, F., Wang, C., & Lee, C. (2023) · Nano Research · doi:10.1007/s12274-023-5879-4 · Cited by 42
Self-citationZhang, C., Zhang, L., Bao, B., Ouyang, W., Chen, W., Li, Q., & Li, D. (2022) · Advanced Materials Technologies · doi:10.1002/admt.202201138 · Cited by 42
Wu, S., Liu, P., Tong, W., Li, J., Xu, G., Teng, F., Liu, J., Han, F., Hu, R., Yang, A., et al. (2022) · Composites Science and Technology · doi:10.1016/j.compscitech.2022.109816 · Cited by 41
Cui, Z., Hua, Q., Shi, Y., Wei, R., Dong, Z., Dai, X., Huang, T., Shen, G., Wang, Z. L., & Hu, W. (2025) · Nano Energy · doi:10.1016/j.nanoen.2025.110911 · Cited by 42
Versatile MXene/(GO-AgNWs) electronic textile enabled by mixed-scale assembly strategy
Abay, Z. M., Wei, Y., Tang, Z., Liu, Y., Chen, K., Li, H., Wang, Q., Yuan, J., Hu, P., Lu, D., et al. (2025) · Nano Energy · doi:10.1016/j.nanoen.2025.110963 · Cited by 41
Wang, S., Wang, X., Wang, S., Yu, W. P., Yu, L., Hou, L., Tang, Y., Zhang, Z., Yao, N., Cao, C., et al. (2023) · Advanced Intelligent Systems · doi:10.1002/aisy.202200412 · Cited by 40
Self-citationYang, Q., Ye, Z., Wu, R., Lyu, H., Li, C. Z., Xu, K., & Yang, G. (2023) · Advanced Materials Technologies · doi:10.1002/admt.202300561 · Cited by 40
Recent Progress in Wearable Self‐Powered Biomechanical Sensors: Mechanisms and Applications
Zhang, S., Lin, X., Wan, J., Xu, C., & Han, M. (2024) · Advanced Materials Technologies · doi:10.1002/admt.202301895 · Cited by 40
Li, Q., Wu, T., Zhao, W. G., Li, Y., Ji, J., & Wang, G. (2022) · Composites Part B Engineering · doi:10.1016/j.compositesb.2022.110000 · Cited by 40
Li, Y., Yang, L., He, Z., Liu, Y., Wang, H., Zhang, W., Teng, L., Chen, D., & Song, G. (2022) · Advanced Intelligent Systems · doi:10.1002/aisy.202200128 · Cited by 40
Self‐Powered All‐Optical Tactile Sensing Platform for User‐Interactive Interface
Wei, R., He, J., Ge, S., Liu, H., Ma, X., Tao, J., Cui, X. J., Mo, X., Li, Z., Wang, C., et al. (2022) · Advanced Materials Technologies · doi:10.1002/admt.202200757 · Cited by 39
Wang, T., Zhao, Y., & Wang, Q. (2022) · Soft Robotics · doi:10.1089/soro.2021.0209 · Cited by 40
Zheng, Z., Xia, J., Wang, B., & Guo, Y. (2022) · Nano Energy · doi:10.1016/j.nanoen.2022.107047 · Cited by 37
Debonding‐On‐Demand Polymeric Wound Patches for Minimal Adhesion and Clinical Communication
Zeng, Q., Wang, F., Hu, R., Ding, X., Lu, Y., Shi, G., Haick, H., & Zhang, M. (2022) · Advanced Science · doi:10.1002/advs.202202635 · Cited by 39
Roles of Low‐Dimensional Nanomaterials in Pursuing Human–Machine–Thing Natural Interaction
Zhao, X., Xuan, J., Li, Q., Gao, F., Xun, X., Liao, Q., & Zhang, Y. (2022) · Advanced Materials · doi:10.1002/adma.202207437 · Cited by 38
Machine Learning‐Enhanced Triboelectric Sensing Application
Shang, R., Chen, H., Cai, X., Shi, X., Yang, Y., Wei, X., Wang, J., & Xu, Y. F. (2024) · Advanced Materials Technologies · doi:10.1002/admt.202301316 · Cited by 39
Zhao, P., Song, Y., Hu, Z., Zhong, Z., Li, Y., Zhou, K., Qin, T., Yan, Y., Hsu, H.-H., Han, S.-T., et al. (2024) · Nano Energy · doi:10.1016/j.nanoen.2024.110118 · Cited by 37
Zhou, Z., Zhang, W., Zhang, J., Zhang, Y., Yin, X., & He, B. (2022) · Sensors and Actuators A Physical · doi:10.1016/j.sna.2022.114004 · Cited by 37
Enhancing blind-dumb assistance through a self-powered tactile sensor-based Braille typing system
Liu, W., Yu, W., Li, K., Zhou, S., Wang, Q., & Yu, H. D. (2023) · Nano Energy · doi:10.1016/j.nanoen.2023.108795 · Cited by 37
A general self-powered wireless sensing solution based on triboelectric-discharge effect
Wang, H., Xia, X., Fu, J., Li, J., Chen, C., Dai, Y., Fan, Z., Hu, G., & Zi, Y. (2022) · Nano Energy · doi:10.1016/j.nanoen.2022.107982 · Cited by 37
Revolutionizing wearable sustainable energy enabled by mechano-electric conversion fibers
Zhao, J., Fan, X., Xie, H., Luo, Y., Li, Z., Peng, X., Tao, G., Wang, Z. L., & Dong, K. (2025) · Energy & Environmental Science · doi:10.1039/d5ee00144g · Cited by 36
Machine-Learning Assisted Handwriting Recognition Using Graphene Oxide-Based Hydrogel
Liu, Y., Zhuo, F., Zhou, J. W., Kuang, L., Tan, K., Lu, H., Cai, J., Guo, Y., Cao, R., Fu, Y., et al. (2022) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.2c17943 · Cited by 35
Kim, S., Cho, W., Hwang, J., & Kim, J. (2022) · Nano Energy · doi:10.1016/j.nanoen.2022.108109 · Cited by 34
Tian, X. Y., Dong, S., Yang, M., Ng, H., Liu, Y., Hu, H., & Hua, T. (2022) · Advanced Materials Technologies · doi:10.1002/admt.202201294 · Cited by 34
Babu, A., & Mandal, D. (2024) · ACS Applied Energy Materials · doi:10.1021/acsaem.3c02642 · Cited by 33
Fang, Z., Kong, L., Chen, J., Chen, H., Zhao, X., Luo, D., & Zhang, Z. (2024) · Nano Energy · doi:10.1016/j.nanoen.2024.109882 · Cited by 33
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Qu, X., Cheng, S., Liu, Y., Hu, Y., Shan, Y., Luo, R., Weng, S., Li, H., Niu, H., Gu, M., et al. (2024) · Advanced Materials · doi:10.1002/adma.202402457 · Cited by 33
Yan, C., Jiang, S., Wang, Y., Deng, J., Wang, X., Chen, Z., Chen, T., Huang, H., & Wu, H. (2024) · Nano Energy · doi:10.1016/j.nanoen.2024.110425 · Cited by 33
Yu, J., Tang, J., Wang, L., Guo, Y., Ma, W., Yang, L., & Chen, S. (2023) · Nano Energy · doi:10.1016/j.nanoen.2023.109009 · Cited by 33
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Zhu, R., Fan, D., Jiang, L., Feng, H., Wang, H., & Dai, J. S. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3347133 · Cited by 32
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Kong, K., Wang, L., Wu, H., Le, Z., Zhang, Y., Wang, Z., Yu, Z., Wu, X., Li, L., Li, W., et al. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.154870 · Cited by 31
Triboelectric nanogenerators for self-powered sensors and other applications
Lee, C., Qin, Y., & Wang, Y. (2025) · MRS Bulletin · doi:10.1557/s43577-025-00877-z · Cited by 31
Self-citationBioinspired Adaptive Sensors: A Review on Current Developments in Theory and Application
Gong, G., Zhou, Y., Li, Q., Zhao, W., Geng, S., Li, H., Leng, Y., Zhu, S., Ding, G., Zhai, Y., et al. (2025) · Advanced Materials · doi:10.1002/adma.202505420 · Cited by 31
Guo, Y., Li, K., Yue, W., Kim, N., Li, Y., Shen, G., & Lee, J. (2024) · Nano-Micro Letters · doi:10.1007/s40820-024-01545-8 · Cited by 30
Flexible nanogenerators for intelligent robotics: design, manufacturing, and applications
Zhao, H., Ruan, L., Wang, Z., Shu, M., Lyu, C., Bulathsinghala, R. L., Dharmasena, I., Wu, C., & Ding, W. (2024) · International Journal of Extreme Manufacturing · doi:10.1088/2631-7990/ad94b8 · Cited by 31
Lu, W., Fang, P., Zhu, M., Zhu, Y.-R., Fan, X., Zhu, T., Zhou, X., Wang, F., Chen, T., & Sun, L. (2023) · Advanced Intelligent Systems · doi:10.1002/aisy.202200453 · Cited by 30
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Chu, H., Xue, J., Luo, D., Zheng, H., & Li, Z. (2024) · Advanced Materials Technologies · doi:10.1002/admt.202302068 · Cited by 29
Ma, Y., Liu, W., Liu, X., Wang, N., & Zhang, H. (2024) · International Journal of Optomechatronics · doi:10.1080/15599612.2024.2342279 · Cited by 29
Recent progress in sign language recognition: a review
Wali, A., Shariq, R., Shoaib, S. M., Amir, S., & Farhan, A. A. (2023) · Machine Vision and Applications · doi:10.1007/s00138-023-01479-y · Cited by 29
Recent advances in multimodal skin-like wearable sensors
Wu, S., Zhao, S., Wu, L., Phan, H., He, S., Tang, J., Xu, J., Chu, D., Wang, C., & Peng, S. (2024) · Applied Physics Reviews · doi:10.1063/5.0217328 · Cited by 31
Park, S. U., Lee, H. K., Kim, H. B., Kim, D., Kim, W., Joo, J., Kim, B., Lee, B. W., Jung, Y. H., Park, S., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-63644-3 · Cited by 28
Gao, C., Long, Z., Zhong, T., Liang, S., & Xing, L. (2022) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/ac5192 · Cited by 28
Kong, X., Wen, W., Guan, Y., Lin, Z., Zheng, J., Xie, B., Li, S., Xue, J., & Hu, Q. (2025) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.5c06453 · Cited by 29
Ansar, H., Mudawi, N. A., Alotaibi, S. S., Alazeb, A., Alabdullah, B. I., Alonazi, M., & Park, J. (2023) · IEEE Access · doi:10.1109/access.2023.3300712 · Cited by 27
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Zerrouki, N., Harrou, F., Houacine, A., Bouarroudj, R., Cherifi, M. Y., Ait-Djafer, A. Z., & Sun, Y. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3354784 · Cited by 28
Zhou, Q., Deng, S., Gao, A., Wang, B., Lai, J.-X., Pan, J. W., Huang, L., Pan, C., Meng, G., & Xia, F. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202306619 · Cited by 26
Gao, Y., Xu, B., Qiu, M., Li, Z., Ahmed, T., Yang, Y., Guan, X., & Hong, F. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202416577 · Cited by 26
Smart textiles for chronic disease management: Advancements, applications, and future prospects
Wang, Z., Zhao, X., Yan, K., Zhang, P., Zhang, S., & Fan, H. (2025) · Materials Science and Engineering R Reports · doi:10.1016/j.mser.2025.100987 · Cited by 26
Ren, Z., Zhang, Z., Zhuge, Y., Xiao, Z., Xu, S., Zhou, J., & Lee, C. (2025) · Nano-Micro Letters · doi:10.1007/s40820-025-01743-y · Cited by 26
Self-citationWan, J., Xu, S., Lin, J., Ji, L., Cheng, J., Li, Z., Qu, X., & Li, C. (2025) · Small · doi:10.1002/smll.202504078 · Cited by 27
Ding, I.-J., & Zheng, N.-W. (2022) · Sensors · doi:10.3390/s22030803 · Cited by 26
Hou, X., Zhong, J., Yang, C., Yang, Y. F., He, J., Mu, J., Geng, W., & Chou, X. (2022) · Journal of Materiomics · doi:10.1016/j.jmat.2022.04.003 · Cited by 26
Recent Progress of Energy-Storage-Device-Integrated Sensing Systems
Yuan, M., Zhang, X., Wang, J., & Zhao, Y. (2023) · Nanomaterials · doi:10.3390/nano13040645 · Cited by 26
Development and application of nanogenerators in humanoid robotics
Qu, X., Yang, Z. P., Cheng, J., Li, Z., & Ji, L. (2023) · Nano Trends · doi:10.1016/j.nwnano.2023.100013 · Cited by 28
Zheng, T. Z., Li, G., Zhang, L., & Lei, Y. (2023) · Journal of Materials Chemistry A · doi:10.1039/d3ta05954e · Cited by 26
Lee, K., Gul, O., Kwon, Y., Jeong, J., Cho, S., Ahn, J., Yu, J., Kim, C.-M., Lee, D., Han, H., et al. (2025) · Advanced Functional Materials · doi:10.1002/adfm.202519859 · Cited by 25
Metadata
- Authors
- Wen, F., Zhang, Z., He, T., & Lee, C.
- Affiliations
- National University of Singapore (SG)
- Corresponding author
- Lee, Chengkuo
- Venue
- Nature Communications
- Published
- 2021-09-10
- Volume
- 12
- Issue
- 1
- Pages
- 5378-5378
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC8433305
- OpenAlex
- W3197059061
- Semantic Scholar
- 8ee7ba1cc742cb5bfdeeabebcaa913e57a8ec42c
- Cited by
- 494
- References
- 73
- Citations by year
- 2026: 58 · 2025: 121 · 2024: 121 · 2023: 107 · 2022: 78 · 2021: 9
- FWCI
- 48.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- National University of Singapore (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Key Research and Development Program of China (2019YFB2004800, R‐2020‐S‐002)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Sensor and Energy Harvesting Materials
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Keywords
- sign language recognition, sentence recognition, virtual reality communication, two-way communication, hearing loss, word segmentation, gesture sensing, deep learning
- MeSH terms
- Virtual Reality, Wearable Electronic Devices, Deep Learning, Communication Devices for People with Disabilities, Communication Aids for Disabled, Deafness, Gestures, Humans, Sign Language
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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