Wearable sign language translation system using strain sensors

Lee, Sang‐Min; Jo, Dongbin; Kim, Kyu-Beom; Jang, Jaewon; Park, Wanjun

doi:10.1016/j.sna.2021.113010

Cited by 13 publications

(9 citation statements)

References 35 publications

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“…To make the self-healing polymer electrically conductive, carbon black is added during the synthesis. This approach is quite commonly used for making commercially available polymers, which are in general electrically insulating, electrically conductive, as carbon black is widely available [ 18 , 19 ]. Other approaches to make polymers electrically conductive include the addition of carbon nanotubes (CNT) [ 20 ], graphene [ 21 ], silver nanoparticles [ 22 ] or nanowires [ 23 ], …but safe-handling these nanofillers requires stringent precautions, as they pose mainly pneumological health risks, similar to those of asbestos.…”

Section: Tutorial Descriptionmentioning

confidence: 99%

An Interdisciplinary Tutorial: A Self-Healing Soft Finger with Embedded Sensor

Roels

Terryn

Ferrentino

et al. 2023

Sensors

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In the field of soft robotics, knowledge of material science is becoming more and more important. However, many researchers have a background in only one of both domains. To aid the understanding of the other domain, this tutorial describes the complete process from polymer synthesis over fabrication to testing of a soft finger. Enough background is provided during the tutorial such that researchers from both fields can understand and sharpen their knowledge. Self-healing polymers are used in this tutorial, showing that these polymers that were once a specialty, have become accessible for broader use. The use of self-healing polymers allows soft robots to recover from fatal damage, as shown in this tutorial, which increases their lifespan significantly.

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Section: Tutorial Descriptionmentioning

confidence: 99%

An Interdisciplinary Tutorial: A Self-Healing Soft Finger with Embedded Sensor

Roels

Terryn

Ferrentino

et al. 2023

Sensors

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“…They can be divided into two groups. First, the sensor-based group [15][16][17][18][19][20] includes devices worn on the users' hands, such as gloves, EMG sensors, cables, accelerometers, touch sensors, and flexion sensors. The degree of flexion of the fingers and the fingers' motion are used as the extracted features.…”

Section: Related Workmentioning

confidence: 99%

Backhand-Approach-Based American Sign Language Words Recognition Using Spatial-Temporal Body Parts and Hand Relationship Patterns

Chophuk

Chamnongthai

Chinnasarn

2022

Sensors

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Most of the existing methods focus mainly on the extraction of shape-based, rotation-based, and motion-based features, usually neglecting the relationship between hands and body parts, which can provide significant information to address the problem of similar sign words based on the backhand approach. Therefore, this paper proposes four feature-based models. The spatial–temporal body parts and hand relationship patterns are the main feature. The second model consists of the spatial–temporal finger joint angle patterns. The third model consists of the spatial–temporal 3D hand motion trajectory patterns. The fourth model consists of the spatial–temporal double-hand relationship patterns. Then, a two-layer bidirectional long short-term memory method is used to deal with time-independent data as a classifier. The performance of the method was evaluated and compared with the existing works using 26 ASL letters, with an accuracy and F1-score of 97.34% and 97.36%, respectively. The method was further evaluated using 40 double-hand ASL words and achieved an accuracy and F1-score of 98.52% and 98.54%, respectively. The results demonstrated that the proposed method outperformed the existing works under consideration. However, in the analysis of 72 new ASL words, including single- and double-hand words from 10 participants, the accuracy and F1-score were approximately 96.99% and 97.00%, respectively.

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“…[ 9–15 ] In this context, wearable sign language translation (WSLT) gloves, serving to break the communication barriers between signers and nonsigners, have been widely proposed based on flexible pressure/strain sensors. [ 12–18 ] Meanwhile, the traditional WSLT systems based on photography and image processing technologies suffer from high dependence on the environmental background and the intensity of ambient light. [ 19 ]…”

Section: Introductionmentioning

confidence: 99%

“…Among them, glove-based HMIs for the detection of hand gestures have aroused significant attention because the superb dexterity of human fingers endows the devices with the capability of performing complex tasks. [9][10][11][12][13][14][15] In this context, wearable sign language translation (WSLT) gloves, serving to break the newly designed structures, the properties of anti-harsh environments of the sensors have not received enough attention. [32][33][34] Sensors based on serpentine meander structures, on the contrary, show great mechanical durability even under a large strain range but a much lower sensitivity than that of the crack-based sensor.…”

Section: Introductionmentioning

confidence: 99%

“…Among them, glove‐based HMIs for the detection of hand gestures have aroused significant attention because the superb dexterity of human fingers endows the devices with the capability of performing complex tasks. [ 9–15 ] In this context, wearable sign language translation (WSLT) gloves, serving to break the communication barriers between signers and nonsigners, have been widely proposed based on flexible pressure/strain sensors. [ 12–18 ] Meanwhile, the traditional WSLT systems based on photography and image processing technologies suffer from high dependence on the environmental background and the intensity of ambient light.…”

Section: Introductionmentioning

confidence: 99%

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Ultra‐Robust and Sensitive Flexible Strain Sensor for Real‐Time and Wearable Sign Language Translation

Luo

Song

et al. 2023

Adv Funct Materials

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Flexible strain sensors with high sensitivity and high mechanical robustness are highly desirable for their accurate and long‐term reliable service in wearable human‐machine interfaces. However, the current application of flexible strain sensors has to face a trade‐off between high sensitivity and high mechanical robustness. The most representative examples are micro/nano crack‐based sensors and serpentine meander‐based sensors. The former one typically shows high sensitivity but limited robustness, while the latter is on the contrary. Herein, ultra‐robust and sensitive flexible strain sensors are developed by crack‐like pathway customization and ingenious modulation of low/high‐resistance regions on a serpentine meander structure. The sensors show high cyclic stability (10 000 cycles), strong tolerance to harsh environments, high gauge factor (>1000) comparable with that of the crack‐based sensor, and fast response time (<58 ms). Finally, the sensors are integrated into a wearable sign language translation system, which is wireless, low‐cost, and lightweight. Recognition rates of over 98% are demonstrated for the translation of 21 sign languages with the assistance of machine learning. This system facilitates achieving barrier‐free communication between signers and nonsigners and offers broad application prospects in gesture interaction.

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Wearable sign language translation system using strain sensors

Cited by 13 publications

References 35 publications

An Interdisciplinary Tutorial: A Self-Healing Soft Finger with Embedded Sensor

An Interdisciplinary Tutorial: A Self-Healing Soft Finger with Embedded Sensor

Backhand-Approach-Based American Sign Language Words Recognition Using Spatial-Temporal Body Parts and Hand Relationship Patterns

Ultra‐Robust and Sensitive Flexible Strain Sensor for Real‐Time and Wearable Sign Language Translation

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