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.
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.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
0 of 3 cited sources checked · 0 supported · 0 flagged
- Findingown data
The mean correlation coefficient for 'Get' compared with its own database is 0.53, indicating high similarity.
“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.”
Quote in the paperNot a substantial claim - Findingown data
The nonsegmentation deep learning model achieves a recognition accuracy of 95% for 17 sentences.
“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.”
Quote in the paperSubstantial claim - Also stated in: § Word and sentence recognition upon nonsegmentation method · plain
- Findingown data
The system successfully realizes the recognition of 50 words and 20 sentences.
“The system successfully realizes the recognition of 50 words and 20 sentences.”
Quote in the paperSubstantial claim - Findingown data
The segmentation method with hierarchy classifier achieves an average correct rate of 85.58% for reconstructing and recognizing sentences in the dataset.
“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.”
Quote in the paperSubstantial claim - Also stated in: § Introduction · plain
- Findingown data
The single classifier achieves an average correct rate of 79.41% for reconstructing and recognizing sentences.
“showing an insufficient average correct rate of 79.41%.”
Quote in the paperSubstantial claim
- Findingown data
The nonsegmentation deep learning model achieves a recognition accuracy of 95% for 20 sentences.
“The nonsegmentation AI framework achieves high accuracy for 50 words (91.3%) and 20 sentences (95%) by independently identifying word and sentence signals.”
Quote in the paperSubstantial claim - Findingown data
Nonideal consequence for other sentences, such as Y03, Y04, and Y16, is observed.
“The nonideal consequence for other sentences, such as Y03, Y04, and Y16, is observed as well.”
Quote in the paperNot a substantial claim - Findingown data
The nonsegmentation deep learning model achieves a recognition accuracy of 91.3% for 50 words.
“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%)”
Quote in the paperSubstantial claim - Also stated in: § Introduction · plain
- Findingown data
The single classifier achieves an accuracy of 81.9% for recognizing word units W01-W20.
“The confusion matrix in Fig. 4e shows an accuracy of 81.9% for the single classifier recognizing word units W01–W20.”
Quote in the paperSubstantial claim - Findingown data
The segmentation approach enables recognition of new/never-seen sentences created by new-order word element recombination with an average correct rate of 86.67%.
“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%.”
Quote in the paperSubstantial claim- 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
- Findingnone given
The segmentation approach splits entire sentence signals into word units.
“the segmentation approach splits entire sentence signals into word units.”
Quote in the paperSubstantial claim - Findingown data
The 1D CNN model exhibits optimal accuracy with 5 kernels, 64 filters, and 4 convolutional layers.
“the 1D CNN model exhibits the optimal accuracy performance with 5 kernels, 64 filters, and 4 convolutional layers.”
Quote in the paperSubstantial claim - Findingown data
Several gestures fall into strong correlation area, indicating high possibility for wrong classification.
“there are signals of several gestures fallen into the strong correlation area, which means a high similarity among these gesture signals. These gestures are in the high possibility for the wrong classification.”
Quote in the paperSubstantial claim - Findingown data
The hierarchy classifier achieves an accuracy of 82.8% for segmented word element recognition.
“h Confusion map of segmented word element recognition (accuracy 82.8%) based on hierarchy classifer.”
Quote in the paperSubstantial claim - Findingown data
The hierarchy classifier enhances recognition accuracy for word pieces to 82.81%.
“By this means, the recognition accuracy for word pieces is enhanced to 82.81% as the confusion map in Fig. 4h shows.”
Quote in the paperSubstantial claim - Findingown data
Sign language includes three major motion dynamics: elbow/shoulder motions, face muscle activities, and hand movements.
“the findings show that sign language includes three major motion dynamics, including elbow/shoulder motions, face muscle activities, and hand movements.”
Quote in the paperSubstantial claim - Findingown data
After 50 training epochs, the accuracy reaches almost 90%.
“After only 50 training epochs, the accuracy reaches almost 90%, as shown in Supplementary Fig. 3.”
Quote in the paperNot a substantial claim - Findingown data
Hand motion accounts for 43% of the three major motions.
“The hand motion accounts for a proportion of 43%, remaining dominant in the three major motions”
Quote in the paperSubstantial claim - Findingnone given
The proposed sign language recognition and communication system comprises triboelectric sensing gloves, a deep learning block, and a virtual reality interface.
“Here, we propose an artificial intelligence enabled sign language recognition and communication system comprising sensing gloves, deep learning block, and virtual reality interface.”
- Also stated in: § Introduction · plain
“Here, we show a sign language recognition and communication system comprising triboelectric sensor integrated gloves, AI block, and the VR interaction interface.”
Quote in the paperSubstantial claim - Also stated in: § Introduction · plain
- Findingown data
A greeting scenario demonstrates feasible communication between speech/hearing impaired and healthy user under identical LAN.
“As Fig. 5b(i–v) demonstrates, a greeting scenario is created to demonstrate the feasible communication between the speech/hearing impaired and healthy user under the identical local area network (LAN).”
Quote in the paperSubstantial claim - Findingown data
The system has successfully achieved recognition of 50 words and 20 sentences picked up from the American sign language phrase book as demonstration.
“In summary, we have successfully achieved recognition of 50 words and 20 sentences picked up from the American sign language phrase book as demonstration, which can be further broadened by leveraging more sentences in the dataset to meet the practical daily communication demand.”
Quote in the paperSubstantial claim - Findingown data
The hierarchy classifier shows reliable performance for new/never-seen sentence recognition, indicated by less wrong predictions area.
“As expected, the hierarchy classifier shows a reliable performance for new/never-seen sentence recognition, indicated by the less wrong predictions area marked in red.”
Quote in the paperSubstantial claim - Findingown data
Never-seen sentences New1-New3 are successfully recognized using segmentation, where these sentences are created by new-order word recombination different from dataset order.
“Taking the advantages of segmentation, the never-seen sentences New1–New3 are successfully recognized where these sentences are created by new-order word recombination and the order is different from that of sentences in dataset.”
Quote in the paperSubstantial claim - Findingreasoning
The segmentation approach renders new/never-seen sentence recognition, where new sentences are not included in dataset and created by recombining learned word units in new orders.
“Furthermore, the segmentation approach renders new/never-seen sentence recognition, in which the new/never-seen sentences are not included in the dataset and created by recombining the learned word units in new orders.”
Quote in the paperSubstantial claim - Findingreasoning
The deep learning architectures, single and hierarchy classifiers, inversely infer, reconstruct and recognize the whole sentence with recognition accuracy 85.58% benefitting from established correlation of basic word units and sentences.
“Then the deep learning architectures, single and hierarchy classifiers, inversely infer, reconstruct and recognize the whole sentence (recognition accuracy: 85.58%) benefitting from the established correlation of basic word units and sentences.”
Quote in the paperSubstantial claim - Findingown data
The segmentation-assisted CNN model renders significant never-seen/new sentence identification via the established connection between word units and sentences.
“While the segmentation-assisted CNN model renders significant never-seen/new sentence identification via the established connection between word units and sentences.”
Quote in the paperSubstantial claim - Interpretationreasoning
The nonsegmentation CNN model introduces time latency because it needs the whole long-data-length signal to trigger recognition.
“the time latency for recognition is introduced because the CNN model with nonsegmentation method needs to leverage the whole long-data-length signal (generally 200 data points for the word signal and 800 data points for the sentence signal) to trigger the recognition procedure”
Quote in the paperSubstantial claim - Interpretationown data
The single classifier may not be effective enough when reconstructing and recognizing sentences that existed in the dataset.
“the single classifier may not be effective enough when reconstructing and recognizing sentences that existed in the dataset”
Quote in the paperSubstantial claim - Interpretationreasoning
The segmentation approach coupled with deep learning technology provides a universal platform for recognizing new/never-seen sentences to expand the sentence database in a labor-saving manner.
“the segmentation approach coupled with deep learning technology provides a universal platform for recognizing new/never-seen sentences to expand the sentence database in a labor-saving manner, eliminating the labor-intensive new sentence data collection.”
Quote in the paperSubstantial claim - Interpretationreasoning
The nonsegmentation CNN model cannot distinguish new/never-seen sentences even if they consist of the same word elements in a different order.
“It cannot distinguish new/never-seen sentences even though the sentence consists of the same word elements but in a different order.”
Quote in the paperSubstantial claim - Interpretationreasoning
The VR interface allows the speech impaired to use familiar sign language to engage in communication.
“Owing to the assistance of deep learning enabled sentence recognition and translation, the client of VR interface allows the speech impaired to use the sign language that they are familiar with to engage in the communication.”
Quote in the paperSubstantial claim- 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.
- Premisecitation
Current glove solutions are generally limited to identify discrete and simple numbers, letters, or words of sign language by relying on signal magnitude.
“However, current glove solutions are generally limited to identify discrete and simple numbers, letters, or words of sign language by relying on signal magnitude.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Supplementary Table 3
Not checked in the free run.
- Premiseown data
The lack of effective approach to perceive sentences hinders communication of the signer with nonsigner.
“The lack of effective approach to perceive sentences hinders the communication of the signer with nonsigner (see Supplementary Table 3).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Supplementary Table 3
Not checked in the free run.
- May combine two claims. 23
- Classified as probably not a substantial claim. 6
May combine two claims.
Findingown dataThe hierarchy classifier improves sentence recognition average correct rate to 85.58% with reduced false area.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe voltage output increases when sensor area, force, bending degree, and bending speed increase.
“the voltage output increases when sensor area, force, bending degree, and bending speed increase.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe mean correlation coefficient for 'Must' compared with 'Get' database is 0.37, indicating high signal similarity between 'Get' and 'Must'.
“The mean correlation coefficient of 0.37 is larger than 0.3, indicating the high signal similarity between ‘Get’ and ‘Must’ as well”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataHand motions fall into four categories: finger bending (56%), wrist motion (18%), touch with fingertips (16%), interaction with palm (10%).
“hand motions fall into four categories, including finger bending (56%), wrist motion (18%), touch with fingertips (16%), and interaction with palm (10%).”
Quote in the paperSubstantial claimMay combine two claims.
FindingreasoningThe 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.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe 3rd and 5-10th samples of sentence Y01 are wrongly classified with a poor correct rate of 30%.
“the 3rd and 5–10th samples of sentence Y01 are wrongly classified with a poor correct rate of 30%.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe t-SNE visualization shows poor feature clustering before CNN and desirable classification after CNN with clear boundaries among 50 classes.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
FindingreasoningThe deep learning model recognizes all word elements and reversely reconstructs and recognizes sentences.
“Then the deep learning model recognizes all word elements and reversely reconstructs and recognizes sentences.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe data analysis shows high similarities of word-to-word and sentence-to-sentence signals.
“the data analysis proclaims the high similarities of word-to-word and sentence-to-sentence signals.”
Quote in the paperSubstantial claimMay combine two claims.
FindingreasoningThe system projects sign language recognition results into virtual space and translates them into text and audio, allowing remote and bidirectional communication between signers and non-signers.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Findingnone givenThe system enables separate and independent recognition of words and sentences with high accuracies of 91.3% and 95% within nonsegmentation frame.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe hierarchy classifier achieves precise identification of 5 samples of New1, 3 samples of New2, and 5 samples of New3.
“In comparison, the hierarchy classifier achieves precise identification of 5 samples of New1, 3 samples of New2, and 5 samples of New3.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe hierarchy classifier achieves an average correct rate of 86.67% for new sentence translation, superior to 60% of the single classifier.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe single classifier correctly predicts 2 samples of New1, 3 samples of New2, 4 samples of New3, average correct rate 60%.
“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%).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataBoth classifiers render label series prediction for the total 15 inputs of the 3 new sentences, although the CNN model never sees or learns the true label series before.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe speech-impaired user Lily performs sign language 'How are you?', which is recognized and converted to text and audio by the deep learning model.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe nonsegmentation approach possesses better recognition accuracy for words or sentences but has apparent recognition time latency and incapability of new sentence recognition.
“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.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
InterpretationreasoningThe 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.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningThe long-length signal of sentences provides more distinguishable features than single-word signals, contributing to high accuracy.
“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”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataThe hierarchy classifier improves both the accuracy of recognizing word elements and that of identifying ever-seen/trained sentences.
“Overall, the hierarchy classifier improves both the accuracy of recognizing word elements and that of identifying ever-seen/trained sentences.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationnone givenThe 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.
“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.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Interpretationnone givenThe 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 immense interactions between two population groups.
“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.”
Quote in the paperSubstantial claim- 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.
Probably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationnone givenThe 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.
“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.”
Quote in the paperNot a substantial claim- 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.
Probably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningThe intelligent system provides a reliable and appealing platform for seamless and immense interactions between nonsigners and signers either remotely or proximately.
“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.”
Quote in the paperNot a substantial claim- 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.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe authors affirm that the participant provide informed consent for publication of images and videos.
“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.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationFew works have employed AI-enabled advanced data analytics to achieve highly precise sign language translation, but they are still limited to classification of discrete words that cannot meet practical communication requirements.
“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.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Supplementary Table 3
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningThe small-distance sliding induced recognition brings the potential of real-time recognition or translation of sentences.
“Besides, the small-distance sliding induced recognition brings the potential of the real-time recognition or translation of sentences.”
Quote in the paperNot a substantial claim
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?
The deep learning algorithm initially identifies word components, followed by reconstruction of the original sentences, achieving respective accuracies of 82.81% and 85.58%.
“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
The segmentation method opens new avenues for recognizing novel or previously unseen sentences.
“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
Wen et al. demonstrate a sign language recognition and communication system based on smart glove sensors.
“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
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.
“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.
Nonsegmentation sentence accuracy is attributed to both 20 and 17 sentences
MinorThe paper attributes its 95% nonsegmentation accuracy to 20 sentences, but the results section says that accuracy was obtained for 17 sentences. This changes the scope of a headline recognition result.
“The nonsegmentation AI framework achieves high accuracy for 50 words (91.3%) and 20 sentences (95%) by independently identifying word and sentence signals.”
“Therefore, a high sentence recognition accuracy of 95% is obtained, as indicated in Fig. 3j.”
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- 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
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