Automatic bed position control based on hand gesture recognition for disabled patients

Lamb, Kalpana; Madhe, Swati

doi:10.1109/icacdot.2016.7877568

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…Simul [44] proposed a real-time facial expression and gender classification method based on RGB images and SVM and achieved a classification accuracy of face (86%), facial expression (95.33%), and gender detection (94.67%). Lamb [45] proposed an automatic bed positioning system through the patient's feature gesture input and achieved 96% accuracy by combining a new algorithm called Symlet Wavelet with the existing Euclidean distance algorithm. Mohanty [46] proposed a new hand-gesture recognition method that integrates multi-image features and multi-kernel learning SVM to improve the accuracy of multiclass handgesture recognition and generalize the algorithm.…”

Section: ) Rgb Cameramentioning

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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Section: ) Rgb Cameramentioning

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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“…Another neural network is developed by [31] to recognize 24 hand gestures based on the kinematic characteristics. The Euclidian distance is also available to detect the hand gesture based on Hu invariants of hand and area [2], eccentricity and solidity of hand gesture image [1]. Furthermore, a neural network produced a high accuracy of approximately 99% in detecting the gestures [31].…”

Section: B Hand Gesture Recognitionmentioning

confidence: 99%

“…The number of disabled patients increases due to drugs, accidents, and aging [1]. Usually, they need special rules and special auxiliary equipment such as hearing aids, visual aids, special communication devices, and special means of transportation.…”

Section: Introductionmentioning

confidence: 99%

“…A neural network can be developed to classify hand gestures, through the data obtained from an accelerometer, with high accuracy [31]. The Euclidean distance measured from the finger image can be applied to operate a bed position for up, down, left, and right maneuvers as reported by [1]. Subsequently, wrist orientation is found to be an alternative feature in recognizing hand gestures [23].…”

Section: Introductionmentioning

confidence: 99%

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Electric Wheelchair Control Using Wrist Rotation Based on Analysis of Muscle Fatigue

et al. 2022

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The wheelchair is one of the most common assistive technologies for people with motor impairment, due to its environmentally friendly features in terms of mobility and comfort. However, the operational method for conventional wheelchair is still inconvenience for people with finger problems. Therefore, in the project , a novel control method to operate an electric wheelchair, using hand gestures ( wrist rotation) is developed. Sixty-five (65) participants were involved in this study. Five hand gestures were considered and studied for forward, backward, right, left, and stop maneuvers while considering human ergonomics factor. In this study, the stop maneuver was determined based on the most comfortable hand position to mitigate a fatigue experience, with two gesture classifying methods further investigated. The first method based on threshold has a promising accuracy of 96%, and 91% precision. This method however requires a calibration every time a new user is introduced. The second method, a Naïve Bayes approach, was observed to solve the problem, as it has about 99% of both the accuracy and the precision. The evaluation of this method was then conducted with six participants that operated the wheelchair to follow the trajectories from start to end. The results showed that the participants comfortably controlled the developed wheelchair system to the goal without any collision. Results from experiments indicate that the proposed approach has high accuracy and the potential to solve the problem related to finger dependencies and hand fatigue.

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Easy Hand Gesture Control of a ROS-Car Using Google MediaPipe for Surveillance Use

Allena,

De Leon,

Wong

2022

Lecture Notes in Computer Science

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Automatic bed position control based on hand gesture recognition for disabled patients

Cited by 8 publications

References 11 publications

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

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

Electric Wheelchair Control Using Wrist Rotation Based on Analysis of Muscle Fatigue

Easy Hand Gesture Control of a ROS-Car Using Google MediaPipe for Surveillance Use

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