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
Sun, Z., Zhu, M., Shan, X., & Lee, C. (2022) · Nature Communications · doi:10.1038/s41467-022-32745-8 · Cited by 532
Self-citationZhang, Z., Wen, F., Sun, Z., Guo, X., He, T., & Lee, C. (2022) · Advanced Intelligent Systems · doi:10.1002/aisy.202100228 · Cited by 490
Self-citationSun, T., Feng, B., Huo, J., Xiao, Y., Wang, W., Peng, J., Li, Z., Du, C., Wang, W., Zou, G., et al. (2024) · Nano-Micro Letters · doi:10.1007/s40820-023-01235-x · Cited by 384
Yang, Y., Guo, X., Zhu, M., Sun, Z., Zhang, Z., He, T., & Lee, C. (2022) · Advanced Energy Materials · doi:10.1002/aenm.202203040 · Cited by 352
Self-citationTriboelectric tactile sensor for pressure and temperature sensing in high-temperature applications
Liu, Y., Wang, J., Liu, T., Wei, Z., Luo, B., Chi, M., Zhang, S., Cai, C., Gao, C., Zhao, T., et al. (2025) · Nature Communications · doi:10.1038/s41467-024-55771-0 · Cited by 333
Triboelectric nanogenerators as wearable power sources and self-powered sensors
Pu, X., Zhang, C., & Wang, Z. L. (2022) · National Science Review · doi:10.1093/nsr/nwac170 · Cited by 303
Wang, C., He, T., Zhou, H., Zhang, Z., & Lee, C. (2023) · Bioelectronic Medicine · doi:10.1186/s42234-023-00118-1 · Cited by 295
Self-citationSoft Sensors and Actuators for Wearable Human–Machine Interfaces
Park, J., Lee, Y., Cho, S., Choe, A., Yeom, J., Ro, Y. G., Kim, J., Kang, D., Lee, S., & Ko, H. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00356 · Cited by 291
Decoding lip language using triboelectric sensors with deep learning
Lu, Y., Tian, H., Cheng, J., Zhu, F., Liu, B., Wei, S., Ji, L., & Wang, Z. L. (2022) · Nature Communications · doi:10.1038/s41467-022-29083-0 · Cited by 262
Li, X., Zhu, P., Zhang, S., Wang, X., Luo, X., Leng, Z., Zhou, H., Pan, Z., & Mao, Y. (2022) · ACS Nano · doi:10.1021/acsnano.1c11096 · Cited by 253
Wei, X., Li, H., Yue, W., Gao, S., Chen, Z., Li, Y., & Shen, G. (2022) · Matter · doi:10.1016/j.matt.2022.02.016 · Cited by 247
Tan, P., Han, X., Zou, Y., Qu, X., Xue, J., Li, T., Wang, Y., Luo, R., Cui, X. J., Yuan, X., et al. (2022) · Advanced Materials · doi:10.1002/adma.202200793 · Cited by 223
Liu, L., Guo, X., Liu, W., & Lee, C. (2021) · Nanomaterials · doi:10.3390/nano11112975 · Cited by 205
Self-citationSong, B., Dai, X., Fan, X., & Gu, H. (2023) · Journal of Material Science and Technology · doi:10.1016/j.jmst.2023.10.008 · Cited by 189
Chen, S., Fan, S., Qiao, Z., Wu, Z., Lin, B., Li, Z., Riegler, M. A., Wong, M. Y. H., Opheim, A. I., Korostynska, O., et al. (2025) · Advanced Materials · doi:10.1002/adma.202500412 · Cited by 206
Deep‐Learning‐Assisted Noncontact Gesture‐Recognition System for Touchless Human‐Machine Interfaces
Zhou, H., Huang, W., Xiao, Z., Zhang, S., Li, W., Hu, J., Feng, T., Wu, J. R., Zhu, P., & Mao, Y. (2022) · Advanced Functional Materials · doi:10.1002/adfm.202208271 · Cited by 185
Toward an AI Era: Advances in Electronic Skins
Fu, X., Cheng, W., Wan, G., Yang, Z., & Tee, B. C. K. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00049 · Cited by 172
Advances in flexible sensors for intelligent perception system enhanced by artificial intelligence
Niu, H., Yin, F., Kim, E., Wang, W., Yoon, D., Wang, C., Liang, J., Li, Y., & Kim, N. (2023) · InfoMat · doi:10.1002/inf2.12412 · Cited by 176
Fingerprint-shaped triboelectric tactile sensor
Qu, X., Xue, J., Liu, Y., Rao, W., Liu, Z., & Li, Z. (2022) · Nano Energy · doi:10.1016/j.nanoen.2022.107324 · Cited by 164
Zhu, M., Sun, Z., & Lee, C. (2022) · ACS Nano · doi:10.1021/acsnano.2c04043 · Cited by 160
Self-citationZhang, Z., Shi, Q., He, T., Guo, X., Dong, B., LEE, J. K. W., & Lee, C. (2021) · Nano Energy · doi:10.1016/j.nanoen.2021.106517 · Cited by 146
Self-citationKim, T., Shin, Y., Kang, K., Kim, K., Kim, G., Byeon, Y., Kim, H., Gao, Y., Lee, J. R., Son, G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33457-9 · Cited by 150
Touch IoT enabled by wireless self-sensing and haptic-reproducing electronic skin
Li, D., Zhou, J., Yao, K., Liu, S., He, J., Su, J., Qu, Q., Gao, Y., Song, Z. F., Yiu, C. K., et al. (2022) · Science Advances · doi:10.1126/sciadv.ade2450 · Cited by 150
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Alagumalai, A., Shou, W., Mahian, O., Aghbashlo, M., Tabatabaei, M., Wongwises, S., Liu, Y., Zhan, J., Torralba, A. B., Chen, J., et al. (2022) · Joule · doi:10.1016/j.joule.2022.06.001 · Cited by 144
Tashakori, A., Jiang, Z., Servati, A., Soltanian, S., Narayana, H., Le, K., Nakayama, C., Yang, C.-L., Wang, Z. J., Eng, J. J., et al. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-023-00780-9 · Cited by 137
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Wang, Q., Zhang, Q., Wang, G., Wang, Y., Ren, X., & Gao, G. (2021) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.1c18758 · Cited by 137
Le, X., Shi, Q., Vachon, P., Ng, E. J., & Lee, C. (2021) · Journal of Micromechanics and Microengineering · doi:10.1088/1361-6439/ac3ab9 · Cited by 133
Self-citationWu, X., Luo, X.-M., Song, Z., Bai, Y., Zhang, B., & Zhang, G. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202303504 · Cited by 135
Sharma, S., Pradhan, G. B., Jeong, S., Zhang, S., Song, H., & Park, J. Y. (2023) · ACS Nano · doi:10.1021/acsnano.2c12784 · Cited by 128
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Wang, T., Jin, T., Lin, W., Lin, Y., Liu, H., Yue, T., Tian, Y., Li, L., Zhang, Q., & Lee, C. (2024) · ACS Nano · doi:10.1021/acsnano.3c11281 · Cited by 132
Self-citationGuo, X., Sun, Z., Zhu, Y., & Lee, C. (2024) · Advanced Materials · doi:10.1002/adma.202406778 · Cited by 125
Self-citationLi, W., Zou, K., Guo, J., Zhang, C., Feng, J., You, J., Cheng, G., Zhou, Q., Kong, M., Li, G., et al. (2024) · ACS Nano · doi:10.1021/acsnano.4c02919 · Cited by 123
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Zhao, P., Song, Y., Xie, P., Zhang, F., Xie, T., Liu, G., Zhao, J., Han, S., & Zhou, Y. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202301816 · Cited by 122
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Chen, M., Ouyang, J., Jian, A., Liu, J., Li, P., Hao, Y., Gong, Y., Hu, J., Zhou, J., Wang, R., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34918-x · Cited by 118
Mao, J., Zhou, P., Wang, X., Yao, H., Liang, L., Zhao, Y., Zhang, J., Ban, D., & Zheng, H. (2023) · Nano Energy · doi:10.1016/j.nanoen.2023.108984 · Cited by 127
Haptic Sensing and Feedback Techniques toward Virtual Reality
Shi, Y., & Shen, G. (2024) · Research · doi:10.34133/research.0333 · Cited by 124
Dai, N., Lei, I. M., Li, Z., Li, Y., Fang, P., & Zhong, J. (2022) · Nano Energy · doi:10.1016/j.nanoen.2022.108041 · Cited by 111
Triboelectric nanogenerators for wearable sensing applications: A system level analysis
Bulathsinghala, R. L., Ding, W. K., & Dharmasena, R. D. I. G. (2023) · Nano Energy · doi:10.1016/j.nanoen.2023.108792 · Cited by 111
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Xiong, X., Liang, J., & Wu, W. H. (2023) · Nano Energy · doi:10.1016/j.nanoen.2023.108542 · Cited by 109
Yao, D., Wang, W., Wang, H., Luo, Y., Ding, H., Gu, Y., Wu, H., Tao, K., Yang, B., Pan, S., et al. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202416482 · Cited by 112
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Zhang, Z., Wang, L., & Lee, C. (2023) · Advanced Sensor Research · doi:10.1002/adsr.202200072 · Cited by 107
Self-citationDudem, B., Dharmasena, R. D. I. G., Riaz, R., Vivekananthan, V., Wijayantha, K. G. U., Lugli, P., Petti, L., & Silva, S. R. P. (2022) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.1c20984 · Cited by 107
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Kim, K., Yang, H., Lee, J.-H., & Lee, W. G. (2023) · Advanced Science · doi:10.1002/advs.202303234 · Cited by 110
Decoupled Temperature–Pressure Sensing System for Deep Learning Assisted Human–Machine Interaction
Chen, Z., Liu, S., Kang, P., Wang, Y., Liu, H., Liu, C., & Shen, C. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202411688 · Cited by 105
Integrated intelligent tactile system for a humanoid robot
Bao, R., Tao, J., Zhao, J., Dong, M., Li, J., & Pan, C. (2023) · 中国科学通报：英文版 · doi:10.1016/j.scib.2023.04.019 · Cited by 104
Li, N., Wang, X., Liu, Y., Li, Y., Li, J., Qin, Z., & Jiao, T. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.149303 · Cited by 104
Wearable triboelectric devices for haptic perception and VR/AR applications
Fang, H., Guo, J., & Wu, H. (2022) · Nano Energy · doi:10.1016/j.nanoen.2022.107112 · Cited by 105
Hou, X., Xin, L., Fu, Y., Na, Z., Gao, G., Liu, Y., Xu, Q., Zhao, P., Yan, G. Z., Su, Y., et al. (2023) · Nano Energy · doi:10.1016/j.nanoen.2023.109034 · Cited by 101
Self-powered electrotactile textile haptic glove for enhanced human-machine interface
Xu, G., Wang, H., Zhao, G., Fu, J., Yao, K., Jia, S., Shi, R., Huang, X., Wu, P., Li, J., et al. (2025) · Science Advances · doi:10.1126/sciadv.adt0318 · Cited by 101
Sun, Z., Zhu, M., & Lee, C. (2021) · Nanoenergy Advances · doi:10.3390/nanoenergyadv1010005 · Cited by 100
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Li, Y., Liu, C., Zou, H., Che, L., Sun, P., Yan, J., Liu, W., Xu, Z., Yang, W., Dong, L., et al. (2023) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2022.101191 · Cited by 100
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Duan, S., Zhang, H., Liu, L., Lin, Y., Zhao, F., Chen, P., Cao, S., Zhou, K., Gao, C., Liu, Z., et al. (2024) · Materials Today · doi:10.1016/j.mattod.2024.08.013 · Cited by 102
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Xu, J., Pan, J., Cui, T., Zhang, S., Yang, Y., & Ren, T. (2023) · Sensors · doi:10.3390/s23041868 · Cited by 101
Xie, X., Wang, Q., Zhao, C., Sun, Q., Gu, H., Li, J., Tu, X., Nie, B., Sun, X., Liu, Y., et al. (2024) · ACS Nano · doi:10.1021/acsnano.4c03554 · Cited by 95
Chen, J., He, T., Du, Z., & Lee, C. (2023) · Nano Energy · doi:10.1016/j.nanoen.2023.108898 · Cited by 97
Self-citationZhang, Z., Liu, X., Zhou, H., Xu, S., & Lee, C. (2023) · Small Structures · doi:10.1002/sstr.202300325 · Cited by 99
Self-citationWang, L., Fei, Z., Wu, Z., Yao, Y., Qi, Y., Wang, J., Zhao, L., Zhang, C., Zhang, Y., Qin, G., et al. (2023) · Nano Energy · doi:10.1016/j.nanoen.2023.108504 · Cited by 93
Veeramuthu, L., Cho, C., Venkatesan, M., Ranjith Kumar, G., Hsu, H., Bo-Xun, Z., Kau, L., Chung, M., Lee, W., & Kuo, C. (2022) · Nano Energy · doi:10.1016/j.nanoen.2022.107592 · Cited by 91
AI‐Driven TENGs for Self‐Powered Smart Sensors and Intelligent Devices
Baburaj, A., Jayadevan, S., Aliyana, A. K., Kumar, S. K. N., & Stylios, G. K. (2025) · Advanced Science · doi:10.1002/advs.202417414 · Cited by 93
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Chen, M., Li, P., Wang, R., Xiang, Y., Huang, Z., Qiao, Y., He, M., Liu, J., Wang, J., Su, M., et al. (2022) · Advanced Materials · doi:10.1002/adma.202200985 · Cited by 89
Huang, Q., Jiang, Y., Duan, Z., Yuan, Z., Wu, Y., Peng, J., Xu, Y., Li, H., He, H., & Tai, H. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3264620 · Cited by 87
Yang, Y. F., Hou, X., Geng, W., Mu, J., Zhang, L., Wang, X., He, J., Xiong, J., & Chou, X. (2022) · Science China Technological Sciences · doi:10.1007/s11431-021-1984-9 · Cited by 89
Shen, S., Yi, J., Sun, Z., Guo, Z., He, T., Ma, L., Li, H., Fu, J., Lee, C., & Wang, Z. L. (2022) · Nano-Micro Letters · doi:10.1007/s40820-022-00965-8 · Cited by 86
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Xing, F., Ou, Z. Q., Gao, X., Chen, B., & Wang, Z. L. (2022) · Advanced Functional Materials · doi:10.1002/adfm.202205275 · Cited by 85
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Yin, F., Niu, H., Kim, E., Shin, Y. K., Li, Y., & Kim, N. (2023) · InfoMat · doi:10.1002/inf2.12424 · Cited by 87
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Yang, Y., Shi, Q., Zhang, Z., Shan, X., Salam, B., & Lee, C. (2022) · InfoMat · doi:10.1002/inf2.12360 · Cited by 84
Self-citationDong, B., Zhang, Z., Shi, Q., Wei, J., Ma, Y., Xiao, Z., & Lee, C. (2022) · Science Advances · doi:10.1126/sciadv.abl9874 · Cited by 83
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Han, J., Xu, N., Yu, J., Wang, Y., Xiong, Y., Wei, Y., Wang, Z. L., & Sun, Q. (2022) · Energy & Environmental Science · doi:10.1039/d2ee02557d · Cited by 82
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Zhang, Z., Zhu, Z., Zhou, P., Zou, Y., Yang, J., Haick, H., & Wang, Y. (2023) · ACS Nano · doi:10.1021/acsnano.3c02513 · Cited by 79
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Zhang, Y., & Jiang, X. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2023.045731 · Cited by 82
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Self-citationSun, H., Gao, X., Guo, L., Tao, L., Guo, Z. H., Shao, Y., Cui, T., Yang, Y., Pu, X., & Ren, T. (2022) · InfoMat · doi:10.1002/inf2.12385 · Cited by 78
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Zhou, L., Zhao, B., Liang, J., Lu, F., Yang, W., Xu, J., Zheng, J., Liu, Y., Wang, R., & Liu, Z. (2024) · Materials Horizons · doi:10.1039/d4mh00126e · Cited by 76
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Hu, Z., Lin, L., Lin, W., Xu, Y., Xia, X., Peng, Z., Sun, Z., & Wang, Z. (2023) · Advanced Intelligent Systems · doi:10.1002/aisy.202200371 · Cited by 77
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Qi, L., Kong, L., Wang, Y., Song, J., Azam, A., Zhang, Z., & Yan, J. (2023) · Advanced Energy Materials · doi:10.1002/aenm.202302699 · Cited by 76
Self-citationCheng, A., Li, X., Li, D., Chen, Z., Cui, T., Tao, L., Jian, J., Xiao, H., Shao, W., Tang, Z.-Y., et al. (2025) · Nature Communications · doi:10.1038/s41467-024-55649-1 · Cited by 73
Emerging Wearable Acoustic Sensing Technologies
Liu, T., Mao, Y., Dou, H., Zhang, W., Yang, J., Wu, P., Li, D., & Mu, X. (2025) · Advanced Science · doi:10.1002/advs.202408653 · Cited by 75
Shrestha, K., Pradhan, G. B., Bhatta, T., Sharma, S., Lee, S., Song, H., Jeong, S., & Park, J. Y. (2023) · Nano Energy · doi:10.1016/j.nanoen.2023.108180 · Cited by 67
Xu, L. J., Zhong, S., Yue, T., Zhang, Z., Lu, X. F., Lin, Y., Li, L., Tian, Y., Jin, T., Zhang, Q., et al. (2024) · EcoMat · doi:10.1002/eom2.12448 · Cited by 67
Self-citationBaihan, A., Alutaibi, A. I., Alshehri, M., & Sharma, S. K. (2024) · Scientific Reports · doi:10.1038/s41598-024-76174-7 · Cited by 66
Multi-parameter e-skin based on biomimetic mechanoreceptors and stress field sensing
Shang, C., Xu, Q., Liang, N., Zhang, J., Li, L., & Peng, Z. (2023) · npj Flexible Electronics · doi:10.1038/s41528-023-00252-5 · Cited by 64
From brain to movement: Wearables-based motion intention prediction across the human nervous system
Tang, C., Xu, Z., Occhipinti, E., Yi, W., Xu, M., Kumar, S., VIRK, G. S., Gao, S., & Occhipinti, L. G. (2023) · Nano Energy · doi:10.1016/j.nanoen.2023.108712 · Cited by 65
Directional Moisture-Wicking Triboelectric Materials Enabled by Laplace Pressure Differences
Wang, Z., Zou, X., Liu, T., Zhu, Y., Wu, D., Bai, Y., Du, G., Luo, B., Zhang, S., Chi, M., et al. (2024) · Nano Letters · doi:10.1021/acs.nanolett.4c01962 · Cited by 63
Intelligent fibers and textiles for wearable biosensors
Fu, W., Yu, B., Ji, D., Zhou, Z., Li, X., Wang, R., Lu, W., Sun, Y., & Dai, Y. (2024) · Responsive materials · doi:10.1002/rpm.20240018 · Cited by 66
Lu, L., Wu, J., Zhang, Y., Liu, C., Hu, Y. Y., Chen, B., Zhu, Y., & Mao, Y. (2025) · Nano Research · doi:10.26599/nr.2025.94907924 · Cited by 63
Recent Progress in Self-Powered Sensors Based on Triboelectric Nanogenerators
Wu, J., Yang, Z., & Li, X. (2021) · Sensors · doi:10.3390/s21217129 · Cited by 62
Zhong, J., Hou, X., He, J., Xue, F., Yang, Y. F., Chen, L., Yu, J., Mu, J., Geng, W., & Chou, X. (2022) · Nano Energy · doi:10.1016/j.nanoen.2022.107289 · Cited by 62
Learning Hand Kinematics for Parkinson's Disease Assessment Using a Multimodal Sensor Glove
Li, Y., Yin, J., Liu, C., Xue, B., Shokoohi, C., Ge, G., Hu, M.-L. M., Li, T., Tao, X., Rao, Z., et al. (2023) · Advanced Science · doi:10.1002/advs.202206982 · Cited by 61
Xie, L., Lei, H., Liu, Y., Lu, B., Qin, X., Zhu, C., Ji, H., Gao, Z., Wang, Y., Lv, Y., et al. (2024) · Advanced Materials · doi:10.1002/adma.202406235 · Cited by 61
Yang, B., Cheng, J., Qu, X., Song, Y., Yang, L., Shen, J., Bai, Z., & Ji, L. (2025) · Advanced Science · doi:10.1002/advs.202408689 · Cited by 59
Fish gills inspired parallel-cell triboelectric nanogenerator
Yin, P., Aw, K. C., Jiang, X., Xin, C., Guo, H., Tang, L., Peng, Y., & Li, Z. (2022) · Nano Energy · doi:10.1016/j.nanoen.2022.106976 · Cited by 59
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.
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