Gesture Detection Using Accelerometer and Gyroscope

Gupta, Raghav; Chaudhary, Shashank; Vedant, Akshat; Choudhury, Niladri Paul; Ladwani, Vandana M.

doi:10.1007/978-981-19-5482-5_9

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…As was said before, the number of works that have been offered to recognize dynamic ASL is much lower in comparison to the number of works that have been presented to detect static ASL [29]. Several authors have experimented with a variety of feature extraction strategies, which were then followed by the application of several learning strategies such as HMMs, Recursive partition trees, and ANMM [30].…”

Section: Methodsmentioning

confidence: 99%

Two Dimensional Deep CNN Model for Vision-based Fingerspelling Recognition System

Kozhamkulova,

Nurlybaeva,

Kuntunova

et al. 2023

IJACSA

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This paper presents a novel approach to fingerspelling recognition in real-time, utilizing a twodimensional Convolutional Neural Network (2D CNN). Existing recognition systems often fall short in real-world conditions due to variations in illumination, background, and user-specific characteristics. Our method addresses these challenges, delivering significantly improved performance. Leveraging a robust 2D CNN architecture, the system processes image sequences representing the dynamic nature of fingerspelling. We focus on low-level spatial features and temporal patterns, thereby ensuring a more accurate capture of the intricate nuances of fingerspelling. Additionally, the incorporation of real-time video feed enhances the system's responsiveness. We validate our model through comprehensive experiments, showcasing its superior recognition rate over current methods. In scenarios involving varied lighting, different backgrounds, and distinct user behaviors, our system consistently outperforms. The findings demonstrate that the 2D CNN approach holds promise in improving fingerspelling recognition, thereby aiding communication for the hearing-impaired community. This work paves the way for further exploration of deep learning applications in real-time sign language interpretation. This research bears profound implications for accessibility and inclusivity in communication technology.

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Section: Methodsmentioning

confidence: 99%

Two Dimensional Deep CNN Model for Vision-based Fingerspelling Recognition System

Kozhamkulova,

Nurlybaeva,

Kuntunova

et al. 2023

IJACSA

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show abstract

“…As a result, when operating, people need to look at the phone screen and the movement of the mechanical arm at the same time, which is difficult to operate.More and more scholars have focused their research on accelerometers and gyroscopes to sense body movements [5][6][7][8]. The robot's actions are controlled by human motion to increase the robot's capability of human-computer interaction and cooperation [9][10][11][12]. On the basis of robot technology and intelligent car technology, this intelligent robot has acquired new functions.…”

mentioning

confidence: 99%

Design of Intelligent Robots Based on Body Sense Control

Li,

Wei,

Zheng

et al. 2023

J. Phys.: Conf. Ser.

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Recognizing human actions to control the robot has become a challenging task. This paper provides a manipulator control system based on a mobile platform. The bending Angle of the specific connecting rod mechanism is collected to obtain the bending degree of the finger. The nine-axis gyroscope is used to detect the attitudes of the hand and the arm, and the data is transmitted to the HT32F5352 control board. Experimental results show that intelligent robots based on motion sensing can be equipped with automatic and manual control, environmental detection, and other functions.

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Gesture Detection Using Accelerometer and Gyroscope

Cited by 2 publications

References 25 publications

Two Dimensional Deep CNN Model for Vision-based Fingerspelling Recognition System

Two Dimensional Deep CNN Model for Vision-based Fingerspelling Recognition System

Design of Intelligent Robots Based on Body Sense Control

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