Dynamic Hand Gesture Recognition Using 3D-CNN and LSTM Networks

Rehman, Muneeb U.; Ahmed, Fawad; Khan, Muhammad Attique; Tariq, Usman; Alfouzan, Faisal Abdulaziz; Ahmad, Jawad

doi:10.32604/cmc.2022.019586

Cited by 32 publications

(8 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, deep learning has emerged as one of the techniques to be used for image processing tasks. It has been found that it produces significant outcomes in different fields, including agriculture [ 10 ], medicine [ 11 , 12 ], gesture recognition [ 13 ], and remote sensing [ 14 ], etc. In the medical field, it is used for the detection and classification of different diseases, including skin diseases [ 15 , 16 ], different types of ulcers, and cancer [ 17 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Chest X-ray Classification for the Detection of COVID-19 Using Deep Learning Techniques

Khan

Rehman

Ahmed

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

Recent technological developments pave the path for deep learning-based techniques to be used in almost every domain of life. The precision of deep learning techniques make it possible for these to be used in the medical field for the classification and detection of various diseases. Recently, the coronavirus (COVID-19) pandemic has put a lot of pressure on the health system all around the world. The diagnosis of COVID-19 is possible by PCR testing and medical imagining. Since COVID-19 is highly contagious, diagnosis using chest X-ray is considered safe in various situations. In this study, a deep learning-based technique is proposed to classify COVID-19 infection from other non-COVID-19 infections. To classify COVID-19, three different pre-trained models named EfficientNetB1, NasNetMobile and MobileNetV2 are used. The augmented dataset is used for training deep learning models while two different training strategies have been used for classification. In this study, not only are the deep learning model fine-tuned but also the hyperparameters are fine-tuned, which significantly improves the performance of the fine-tuned deep learning models. Moreover, the classification head is regularized to improve the performance. For the evaluation of the proposed techniques, several performance parameters are used to gauge the performance. EfficientNetB1 with regularized classification head outperforms the other models. The proposed technique successfully classifies four classes that include COVID-19, viral pneumonia, lung opacity, and normal, with an accuracy of 96.13%. The proposed technique shows superiority in terms of accuracy when compared with recent techniques present in the literature.

show abstract

Section: Introductionmentioning

confidence: 99%

Chest X-ray Classification for the Detection of COVID-19 Using Deep Learning Techniques

Khan

Rehman

Ahmed

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…A wide range of applications, involving smart homes, video surveillance, sign language recognition, human–robot interaction, and health care, have recently embraced dynamic hand gestures. All of these applications require high levels of accuracy against a busy background, optimum recognition, and temporal precision (Huenerfauth and Lu, 2014 ; Ur Rehman et al, 2022 ).…”

Section: Techniques For Multimodal Human–robot Interactionmentioning

confidence: 99%

Recent advancements in multimodal human–robot interaction

Yang

et al. 2023

Front. Neurorobot.

View full text Add to dashboard Cite

Robotics have advanced significantly over the years, and human–robot interaction (HRI) is now playing an important role in delivering the best user experience, cutting down on laborious tasks, and raising public acceptance of robots. New HRI approaches are necessary to promote the evolution of robots, with a more natural and flexible interaction manner clearly the most crucial. As a newly emerging approach to HRI, multimodal HRI is a method for individuals to communicate with a robot using various modalities, including voice, image, text, eye movement, and touch, as well as bio-signals like EEG and ECG. It is a broad field closely related to cognitive science, ergonomics, multimedia technology, and virtual reality, with numerous applications springing up each year. However, little research has been done to summarize the current development and future trend of HRI. To this end, this paper systematically reviews the state of the art of multimodal HRI on its applications by summing up the latest research articles relevant to this field. Moreover, the research development in terms of the input signal and the output signal is also covered in this manuscript.

show abstract

“…The issue with detecting an object in an image is obtaining a region, suggesting, and testing it according to the class of object. As a result, some researchers relied on a single approach to identify the object in the image as in the studies [18][19][20]. While others used a mix of two approaches as in the studies [21][22][23][24].…”

Section: Related Workmentioning

confidence: 99%

Static Hand Gesture Angle Recognition via Aggregated Channel Features (ACF) Detector

Mirza¹,

Taban²,

Karam³

et al. 2022

View full text Add to dashboard Cite

Static hand gesture recognition is critical in the development of a system for human-computer interaction. Many human-computer interactions, such as human-robot interaction, game control, control of smart home devices, and others, use hand gestures as a fundamental and natural language of the body. The direction of rotation of static hand gestures is the subject of this research, and the focus is on six degrees of rotation (0°, 45°, 90°, 180°, 270°, and 315°). This work presents an ideal approach that can recognize the angle of hand gestures based on the Aggregate Channel Features (ACF) detector. This approach consists of three main stages: preprocessing (image labelling), computer training, and hand angle detection based on the ACF detector. The training process consists of 25 stages. The static hand gesture dataset contained 569 images (361 for training and 208 for testing). The average time cost to detect all hand gesture angles was 0.9445 seconds, and all hand angles were recognized with 100% accuracy. This is a strong indication that supports our approach.

show abstract

Dynamic Hand Gesture Recognition Using 3D-CNN and LSTM Networks

Cited by 32 publications

References 31 publications

Chest X-ray Classification for the Detection of COVID-19 Using Deep Learning Techniques

Chest X-ray Classification for the Detection of COVID-19 Using Deep Learning Techniques

Recent advancements in multimodal human–robot interaction

Static Hand Gesture Angle Recognition via Aggregated Channel Features (ACF) Detector

Contact Info

Product

Resources

About