On an algorithm for Vision-based hand gesture recognition

Ghosh, Dipak; Ari, Samit

doi:10.1007/s11760-015-0790-4

Cited by 20 publications

(7 citation statements)

References 22 publications

(53 reference statements)

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“…Studies have shown that the incidence of gestational diabetes and hypertension in pregnant women is getting higher and higher. Therefore, women during pregnancy and childbirth should increase their physical activity to maintain the health of themselves and their fetus [ 15 ].…”

Section: Yoga Postpartum Pelvic Floor Rehabilitation Methodsmentioning

confidence: 99%

The Effects of Yoga Exercise on Pelvic Floor Rehabilitation of Postpartum Women

2022

Journal of Healthcare Engineering

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Rehabilitation of the pelvic floor after delivery is very important for women. Pelvic floor rehabilitation can speed up the recovery of the postpartum vagina and pelvic floor muscle tension and elasticity and have a good effect on the prevention and treatment of postpartum vaginal prolapse and relaxation, urinary incontinence and other pelvic floor disorders. Thus, this article focuses on yoga exercise to explore its impact on postpartum pelvic floor rehabilitation. This article uses electrical stimulation and the treatment of pelvic floor muscles combined with the posture recognition algorithm, the yoga rehabilitation training program that has the best effect on the parturient is obtained, and the yoga myoelectric stimulation combined method and the traditional myoelectric stimulation method are designed for comparison experiments. The experimental results show that the parturients who have undergone the combined method of yoga myoelectric stimulation, in the resting state, contraction state, and Valsalva state, the position of the bladder meridian, the position of the uterus, and the position of the rectal ampulla of the parturient have a significant recovery compared those who have undergone the traditional electromyography treatment. In addition, the average area of hiatus in the pelvic floor ultrasound examination in the control group 42 days postpartum was 12.2605 cm2, while the average area of the hiatus in the pelvic floor ultrasound examination in the experimental group 42 days postpartum was 10.788 cm2; the average area of hiatus in the pelvic floor ultrasound examination in the control group at 3 months postpartum was 11.4805 cm2, and the average area of hiatus in the pelvic floor ultrasound examination in the experimental group at 3 months postpartum was 8.9475 cm2. To sum up, yoga had a very significant improvement on the physical indicators and mental health of postpartum women.

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Section: Yoga Postpartum Pelvic Floor Rehabilitation Methodsmentioning

confidence: 99%

The Effects of Yoga Exercise on Pelvic Floor Rehabilitation of Postpartum Women

2022

Journal of Healthcare Engineering

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“…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. Ghosh [47] used two ASL (American sign language) datasets for static hand-gesture recognition, and through cross-validation of various recognition algorithms, the maximum dataset-1 (82.25%) and dataset-2 (87.67%) per dataset) confirmed the recognition accuracy. Oyedotun [48] discusses the use of deep learning in recognizing static hand gestures in vision-based systems.…”

Section: ) Rgb Cameramentioning

confidence: 93%

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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“…They considered different feature extraction methods and taken accuracy which is done on different signs, numbers, alphabets and words. (Ghosh & Ari, 2016) proposed LCS feature set with block-based feature and is applied for 24 static ASL Hand Alphabet and obtained 82% accuracy. (Sharma &Verma, 2015) used hand gesture shapes and positions recognition system and obtained an accuracy of 95.44%.…”

Section: Related Workmentioning

confidence: 99%

A Smart Vision Based Single Handed Gesture Recognition system using deep neural networks

Suguna¹,

Rupavathy²,

Asmetha³

2021

Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bh

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The primary and expressive mode of human communications are gestures. Human can interact with machines using body postures and finger pointing. Advancements in human-computer interaction (HCI) has presented new innovations in technology making the users to communicate with computers in an instinctual manner. Evidences clearly state that future living space will be dominated by sensor-based devices and hence an efficient human-computer interfaces are required to exchange information. Hand gesture interfaces have been employed in multiple domains and has won social acceptance. System requirements for gesture recognition vary with the intended application areas. Responsiveness, Learnability, Cost and Accuracy are major drivers for success of hand gesture recognition systems. This paper suggest a HCI design that requires no wearable markers or gloves. A noninvasive vision based framework has been suggested for human-machine interface. Deep Neural Networks have provided promising results in vision based tasks. Convolutional Neural Networks (CNN) are claimed for image recognition problems as they learn features from images gradually and automatically. An optimal CNN architecture has been proposed to recognize single handed gestures. The images of hand gestures convey a numerical representation of ten digits. Image augmentation has been performed to increase the size of training data for deep learning. Depending on application, the interpretation of gesture can be customized. The classification performance has been analyzed with metrics reported by confusion matrix. The proposed architecture performs well both in training and testing reporting the accuracy of 98.2% and 96.2% respectively. Tuning the hyper parameter has improved test accuracy.

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On an algorithm for Vision-based hand gesture recognition

Cited by 20 publications

References 22 publications

The Effects of Yoga Exercise on Pelvic Floor Rehabilitation of Postpartum Women

The Effects of Yoga Exercise on Pelvic Floor Rehabilitation of Postpartum Women

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

A Smart Vision Based Single Handed Gesture Recognition system using deep neural networks

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