Toward Haptic Communication: Tactile Alphabets Based on Fingertip Skin Stretch

Gaffary, Yoren; Argelaguet, Ferran; Marchal, Maud; Girard, Adrien; Gosselin, Florian; Emily, Mathieu; Lécuyer, Anatole

doi:10.1109/toh.2018.2855175

Cited by 13 publications

(3 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After applying the scoring template, the final similarity score is obtained. Some scholars have tried to verify the validity and reliability [41]. After reviewing the factors that affect the scoring and the current methods that mainly focus on the spoken language scoring, a method for objective scoring of the spoken language was proposed [42].…”

Section: 3mentioning

confidence: 99%

Application of Ontology Matching Algorithm Based on Linguistic Features in English Pronunciation Quality Evaluation

Zhu

2022

Occupational Therapy International

View full text Add to dashboard Cite

Traditional English classroom teaching is difficult to meet the oral learning needs of most learners. Thanks to the continuous advancement of speech processing technology, computer-assisted language learning systems are becoming more intelligent, not only pointing out learners’ pronunciation errors but also assessing their overall pronunciation level. Method. This paper uses the method of tree kernel function to measure the similarity of two ontology trees. According to the features of nodes in ontology tree, methods to calculate the external features and internal features of nodes are proposed, respectively. External features are mainly obtained by calculating the hierarchical centrality, node density, and node coverage of nodes in the ontology tree; internal features are mainly obtained by measuring the richness of internal information. According to the similarity of ontology tree and the external features and internal features of nodes, the calculation formula of structural comprehensive similarity is improved, and the features of ontology itself can be fully considered in the calculation. According to the difference of the structure, the weights of the corresponding features during the calculation are adjusted autonomously, so that the calculation results are closer to reality. In spectral image preprocessing, endpoint detection utilizes the harmonic characteristics presented by narrowband spectrograms with high frequency resolution and eliminates useless nonspeech segments by detecting the presence of voiced segments. When building the neural network model, four convolutional layers, two fully connected layers, and one softmax output layer were conceived, and dropout was used to randomly suspend the work of some neurons to avoid overfitting. Results/Discussion. Through the data analysis of mean and variance and verified by one-way analysis of variance, it proves that the sentiment evaluation method in this paper is effective. The traditional multiple linear regression method is not suitable for the corpus and application scenarios of this paper. This paper proposes a decision tree structure, which is similar to the overall scoring process of raters, and uses the Interactive Dicremiser version 3 (ID3) algorithm to build a comprehensive evaluation decision tree for pitch, rhythm, intonation, speech rate, and emotion indicators. It is proved by experiments that the accurate consistency rate of the human-machine evaluation in this paper is 93%, the adjacent consistency rate is 96%, and the Pearson correlation coefficient value of the human-machine evaluation results is 0.89. The data results prove that the evaluation method in this paper is credible.

show abstract

Section: 3mentioning

confidence: 99%

Application of Ontology Matching Algorithm Based on Linguistic Features in English Pronunciation Quality Evaluation

Zhu

2022

Occupational Therapy International

View full text Add to dashboard Cite

show abstract

“…There are several ways to display tactile Morse code. The most commonly used way of displaying tactile Morse code is with vibration (see, for instance, References [14,[16][17][18]), but it can also been done using proprioceptive information by manipulating the height of a finger [17], skin stretch on the finger tip [7], or via embossed images [9]. In a direct comparison by Tan and colleagues, it was found that vibrotactile Morse led to better performance than proprioceptively presented Morse code [17].…”

Section: Introductionmentioning

confidence: 99%

Learning the Vibrotactile Morse Code Alphabet

Plaisier

Vermeer

Kappers

2020

ACM Trans. Appl. Percept.

View full text Add to dashboard Cite

Vibrotactile Morse code provides a way to convey words using the sense of touch with vibrations. This can be useful in applications for users with a visual and/or auditory impairment. The advantage of using vibrotactile Morse code is that it is technically easy to accomplish. The usefulness of tactile Morse code also depends on how easy it is to learn to use without providing a visual representation of the code. Here we investigated learning of the vibrotactile the Morse code alphabet without any visual representation of the code and whether the learned letters can immediately be used to recognize words. Two vibration motors were used: one was attached to the left arm (dots) and the other to the right arm (dashes). We gave the participants a learning session of 30 minutes and determined how many letters they had learned. All participants managed to learn at least 15 letters in this time. Directly afterward, they were presented with 2-, 3-, 4-, or 5-letter words consisting of only the letters they had learned. Participants were able to identify words, but correct rates decreased rapidly with word length. We can conclude that it is possible to learn vibrotactile Morse code using only a vibrotactile representation (15 to 24 letters in 30 minutes). After the learning session, it was possible to recognise words, but to increase the recognition rates extra training would be beneficial.

show abstract

“…The use of tactile force sensors is very versatile. There are also studies on using tactile sensors to recognize alphabets such as Morse Code and Braille [110], and also using reinforcement learning for a robot to type on a Braille keyboard [111].…”

Section: Tactile Force Sensorsmentioning

confidence: 99%

Robotic part manipulation for 3D-print post-processing

Lim

View full text Add to dashboard Cite

The contributions of the co-authors are as follows:I wish to express my greatest gratitude to my spouse, family, and friends who have given me their utmost encouragement and support throughout this journey. My heartfelt appreciation also goes to my NTU supervisor and both HP managers for their guidance. I am also very grateful to my fellow colleagues, my thesis advisory committee, and my examiners for their directions.

show abstract

Toward Haptic Communication: Tactile Alphabets Based on Fingertip Skin Stretch

Cited by 13 publications

References 30 publications

Application of Ontology Matching Algorithm Based on Linguistic Features in English Pronunciation Quality Evaluation

Application of Ontology Matching Algorithm Based on Linguistic Features in English Pronunciation Quality Evaluation

Learning the Vibrotactile Morse Code Alphabet

Robotic part manipulation for 3D-print post-processing

Contact Info

Product

Resources

About