Artificial Intelligence–Enabled Gesture‐Language‐Recognition Feedback System Using Strain‐Sensor‐Arrays‐Based Smart Glove

Lu, Wen‐xiao; Fang, Ping; Zhu, Minglu; Zhu, Yi-run; Fan, Xinjian; Zhu, Tianchen; Zhou, Xuan; Wang, Fengxia; Chen, Tao; Sun, Lining

doi:10.1002/aisy.202200453

Cited by 4 publications

(3 citation statements)

References 66 publications

(51 reference statements)

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“… Lu et al (2023) presented a novel wearable smart glove for gesture language communication, addressing limitations in existing solutions for the hearing-impaired. By integrating strain-sensor arrays and machine learning, the glove achieved over 99% accuracy in recognizing gestures, offering real-time feedback and applications across industries like entertainment, healthcare, and sports training.…”

Section: Review Of Related Workmentioning

confidence: 99%

A machine learning-driven web application for sign language learning

Orovwode,

Ibukun,

Abubakar

2024

Front. Artif. Intell.

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Addressing the increasing demand for accessible sign language learning tools, this paper introduces an innovative Machine Learning-Driven Web Application dedicated to Sign Language Learning. This web application represents a significant advancement in sign language education. Unlike traditional approaches, the application’s unique methodology involves assigning users different words to spell. Users are tasked with signing each letter of the word, earning a point upon correctly signing the entire word. The paper delves into the development, features, and the machine learning framework underlying the application. Developed using HTML, CSS, JavaScript, and Flask, the web application seamlessly accesses the user’s webcam for a live video feed, displaying the model’s predictions on-screen to facilitate interactive practice sessions. The primary aim is to provide a learning platform for those who are not familiar with sign language, offering them the opportunity to acquire this essential skill and fostering inclusivity in the digital age.

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Section: Review Of Related Workmentioning

confidence: 99%

A machine learning-driven web application for sign language learning

Orovwode,

Ibukun,

Abubakar

2024

Front. Artif. Intell.

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“…At the same time, human skin is the largest perception network on the human body [9], and traditional human-machine interaction devices often only focus on its interaction effect, while ignoring the comfort and realism of human experience [10][11][12][13][14][15]. With the development of flexible electronic technology, the preparation of sensitive units of sensors has gradually become refined and patterned, which has opened a new journey to explore high-performance flexible strain sensors for humanmachine interaction [16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…Lu et al [20] proposed a low-cost and efficient gesture language recognition feedback system that combines strain sensor arrays and machine learning technology. This intelligent glove uses machine learning to achieve gesture language recognition, with an accuracy rate of over 99%, but its sensitivity is too low (GF = 1.6).…”

Section: Introductionmentioning

confidence: 99%

Highly sensitive strain sensors based on dispensing technology for human–machine interaction

Chen,

Huang,

Zhang

et al. 2023

Flex. Print. Electron.

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Flexible strain sensors have stable and sensitive sensing performance under deformation conditions such as pressing, bending, and stretching. However, the preparation process of high-performance strain sensors is still very complex, which also limits the application and production of sensors. At the same time, most sensors are unstable and inefficient, so they cannot meet people’s expectations for high sensitivity and stability. In order to solve the above problems, this paper proposes a resistive strain sensor based on dispensing technology, with carbon black and polyurethane mixture as printing ink. Then, a sensor-sensitive layer with a right-angle serpentine structure is printed directly by air pressure extrusion. The sensor can detect changes at 0.1% strain and withstand 2400 tensile cycles while maintaining a sensitivity of 28.07 in the range of 0%–40%. In addition, the sensor can accurately and stably reflect the changes in different joints of the human body. At the same time, the data glove based on the strain sensor shows great application potential in the fields of gesture recognition and human–machine interaction.

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A High‐Precision Dynamic Movement Recognition Algorithm Using Multimodal Biological Signals for Human–Machine Interaction

Cao,

Ma,

Chen

et al. 2024

Advanced Intelligent Systems

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Accurate recognition of human dynamic movement is essential for seamless human–machine interaction (HMI) across various domains. However, most of the existing methods are single‐modal movement recognition, which has inherent limitations, such as limited feature representation and instability to noise, which will affect its practical performance. To address these limitations, this article proposes a novel fusion approach that can integrate two biological signals, including electromyography (EMG) and bioelectrical impedance (BI). The fusion method combines EMG for capturing dynamic movement features and BI for discerning key postures representing discrete points within dynamic movements. In this method, the identification of key postures and their temporal sequences provide a guiding framework for the selection and weighted correction of probability prediction matrices in EMG‐based dynamic recognition. To verify the effectiveness of the method, six dynamic upper limb movements and nine key postures are defined, and a Universal Robot that can follow movements is employed for experimental validation. Experimental results demonstrate that the recognition accuracy of the dynamic movement reaches 96.2%, representing an improvement of nearly 10% compared with single‐modal signal. This study illustrates the potential of multimodal fusion of EMG and BI in movement recognition, with broad prospects for application in HMI fields.

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Artificial Intelligence–Enabled Gesture‐Language‐Recognition Feedback System Using Strain‐Sensor‐Arrays‐Based Smart Glove

Cited by 4 publications

References 66 publications

A machine learning-driven web application for sign language learning

A machine learning-driven web application for sign language learning

Highly sensitive strain sensors based on dispensing technology for human–machine interaction

A High‐Precision Dynamic Movement Recognition Algorithm Using Multimodal Biological Signals for Human–Machine Interaction

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