Object Detection and Gesture Control of Four-Wheel Mobile Robot

Jain, Mohit; Sehgal, Muhammad Shoaib B.; Kalra, Y.; Jain, Prateek; Aggarwal, Naveen; Kumar, Danya Pradeep; Bera, Tarun Kumar; Singh, Rajmeet

doi:10.1109/icces45898.2019.9002323

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…Ultrasonic sensors are used to control the movement of the wheelchair, avoiding the possibility of collisions with objects until the user is able to take over some of the responsibility of steering. It simply calculates the degree of inclination and decides which direction to move [3].…”

Section: Human-machine Interface and Cost-effective Innovationmentioning

confidence: 99%

Hand Gesture Controlled Wheel Chair

Nafde,

2024

IJSREM

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The project for controlling wheelchairs through gestures offers a complete solution to enhance the mobility and independence of individuals with limited motor abilities using an intuitive gesture control system. This system incorporates essential elements, such as an Arduino Nano microcontroller, MPU (Motion Processing Unit), NRF wireless communication module, 775 DC motors, and a motor driver, to allow for smooth and responsive wheelchair operation.

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Section: Human-machine Interface and Cost-effective Innovationmentioning

confidence: 99%

Hand Gesture Controlled Wheel Chair

Nafde,

2024

IJSREM

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“…The system achieved a recognition accuracy of 96.7% for 12 hand gestures, demonstrating the effectiveness of combining sEMG and IMU data in conjunction with deep learning techniques for accurate gesture recognition in various applications. In the study by Banihashem et al[121], an adaptive gesture recognition system based on a combination of sEMG signals and fuzzy logic was developed. The system captured sEMG signals from the forearm muscles and used time-domain analysis to extract features for gesture classification.…”

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confidence: 99%

A Decade of Progress in Human Motion Recognition: A Comprehensive Survey From 2010 to 2020

Noh,

Yoon,

Lee

2024

IEEE Access

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The central perspective of this review is to categorize research in Human Motion Recognition (HMR) over the past decade into two significant categories: vision sensor-based (VSM) and wearable sensor-based methods (WSM). Within each category, research is further assessed from the viewpoints of sensors, classification algorithms, datasets, gesture types, target body parts, and performance. This approach allows for a comprehensive assessment of the overall research trends and technological advancements in HMR. Both VSM and WSM present their own sets of advantages and challenges. VSM faces challenges related to limited workspace, varying lighting conditions, occlusion, and complex image processing. Conversely, WSM, compared to VSM, deals with challenges associated with multiple sensor calibration, intrusiveness, and magnetic field mapping due to sensor placement. As such, the choice between these methods depends on the specific application, the required level of accuracy, and user preferences. Gaining insights into the nature of various HMR methods and staying informed about recent research trends is of utmost importance. By the end of this review, readers will gain a comprehensive and systematic understanding of the latest developments in HMR techniques, which will serve as a valuable resource for researchers and practitioners alike.

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