A Survey on Intelligent Gesture Recognition Techniques

Ojeda-Castelo, Juan Jesus; Capobianco-Uriarte, María de las Mercedes; Piedra-Fernández, José A.; Ayala, Rosa

doi:10.1109/access.2022.3199358

Cited by 13 publications

(3 citation statements)

References 132 publications

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“…Since 2013/14, we have seen a significant increase in publications dealing with gesture recognition. The most recent publication status regarding gesture recognition can be found in [ 9 ]. This study has analysed 571 papers related to artificial intelligence and gesture recognition.…”

Section: Introductionmentioning

confidence: 99%

Using Gesture Recognition for AGV Control: Preliminary Research

Budzan

Wyżgolik

Kciuk

et al. 2023

Sensors

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In this paper, we present our investigation of the 2D Hand Gesture Recognition (HGR) which may be suitable for the control of the Automated Guided Vehicle (AGV). In real conditions, we deal with, among others, a complex background, changing lighting conditions, and different distances of the operator from the AGV. For this reason, in the article, we describe the database of 2D images created during the research. We tested classic algorithms and modified them by us ResNet50 and MobileNetV2 which were retrained partially using the transfer learning approach, as well as proposed a simple and effective Convolutional Neural Network (CNN). As part of our work, we used a closed engineering environment for rapid prototyping of vision algorithms, i.e., Adaptive Vision Studio (AVS), currently Zebra Aurora Vision, as well as an open Python programming environment. In addition, we shortly discuss the results of preliminary work on 3D HGR, which seems to be very promising for future work. The results show that, in our case, from the point of view of implementing the gesture recognition methods in AGVs, better results may be expected for RGB images than grayscale ones. Also using 3D imaging and a depth map may give better results.

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Section: Introductionmentioning

confidence: 99%

Using Gesture Recognition for AGV Control: Preliminary Research

Budzan

Wyżgolik

Kciuk

et al. 2023

Sensors

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“…In terms of deep models, Convolutional Neural Network (CNN) [20] [21] and Recurrent Neural Network (RNN) [22] are the most prominent approaches used for HGR. CNN is considered one of the most efficient deep models for image classification tasks [23].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Dynamic Hand Gesture Recognition using Feature Concatenation via Multi-Input Hybrid Model

Korti

Slimane

Lakhdari

2023

Int. j. electr. comput. eng. syst. (Online)

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Radar-based hand gesture recognition is an important research area that provides suitable support for various applications, such as human-computer interaction and healthcare monitoring. Several deep learning algorithms for gesture recognition using Impulse Radio Ultra-Wide Band (IR-UWB) have been proposed. Most of them focus on achieving high performance, which requires a huge amount of data. The procedure of acquiring and annotating data remains a complex, costly, and time-consuming task. Moreover, processing a large volume of data usually requires a complex model with very large training parameters, high computation, and memory consumption. To overcome these shortcomings, we propose a simple data processing approach along with a lightweight multi-input hybrid model structure to enhance performance. We aim to improve the existing state-of-the-art results obtained using an available IR-UWB gesture dataset consisting of range-time images of dynamic hand gestures. First, these images are extended using the Sobel filter, which generates low-level feature representations for each sample. These represent the gradient images in the x-direction, the y-direction, and both the x- and y-directions. Next, we apply these representations as inputs to a three-input Convolutional Neural Network- Long Short-Term Memory- Support Vector Machine (CNN-LSTM-SVM) model. Each one is provided to a separate CNN branch and then concatenated for further processing by the LSTM. This combination allows for the automatic extraction of richer spatiotemporal features of the target with no manual engineering approach or prior domain knowledge. To select the optimal classifier for our model and achieve a high recognition rate, the SVM hyperparameters are tuned using the Optuna framework. Our proposed multi-input hybrid model achieved high performance on several parameters, including 98.27% accuracy, 98.30% precision, 98.29% recall, and 98.27% F1-score while ensuring low complexity. Experimental results indicate that the proposed approach improves accuracy and prevents the model from overfitting.

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“…Combined with deep learning models, these sophisticated algorithms enable detecting and tracking key body joints and gestures, providing real-time feedback to physiotherapists and patients [7]. Using AI for Human Pose and Gesture Estimation offers numerous advantages in physiotherapy, such as automatic and precise posture and movement analysis, leading to improved exercise form and reduced risk of injury [8]. With the capability to identify incorrect postures and movements, AI-based solutions empower physiotherapists to provide personalized corrective guidance, optimizing the rehabilitation process and promoting faster recovery.…”

Section: Introductionmentioning

confidence: 99%

LogRF: An Approach to Human Pose Estimation Using Skeleton Landmarks for Physiotherapy Fitness Exercise Correction

Raza,

Qadri,

Akhtar

et al. 2023

IEEE Access

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Human pose and gesture estimation are crucial in correcting physiotherapy fitness exercises. In recent years, advancements in computer vision and machine learning approaches have led to the development of sophisticated pose estimation models that accurately track and analyze human movements in real time. This technology enables physiotherapists and fitness trainers to gain valuable insights into their client's exercise forms and techniques, facilitating more effective exercise corrections and personalized training regimens. This research aims to propose an efficient artificial intelligence method for human pose estimation during physiotherapy fitness exercises. We utilized a multi-class exercise dataset based on human skeleton movement points to conduct our experimental research. The dataset comprises 133 features derived from human skeleton movements during various exercises, resulting in high feature dimensionality that affects the performance of human pose estimation with machine learning and deep learning methods. We have introduced a novel Logistic regression Recursive Feature elimination (LogRF) method for feature selection.Extensive experiments demonstrate that using the top twenty selected features, the random forest method outperformed state-of-the-art studies with a high-performance score of 0.998. The performance of each applied method is validated through a k-fold approach and further enhanced using hyperparameter tuning. Our proposed study assists specialists in identifying and addressing potential biomechanical issues, improper postures, and incorrect movement patterns, which are essential for injury prevention and optimizing exercise outcomes. Furthermore, this study enhances the capabilities of remote monitoring and guidance capabilities, allowing physiotherapists to support their patient's progress with prescribed exercises continually.

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A Survey on Intelligent Gesture Recognition Techniques

Cited by 13 publications

References 132 publications

Using Gesture Recognition for AGV Control: Preliminary Research

Using Gesture Recognition for AGV Control: Preliminary Research

Enhancing Dynamic Hand Gesture Recognition using Feature Concatenation via Multi-Input Hybrid Model

LogRF: An Approach to Human Pose Estimation Using Skeleton Landmarks for Physiotherapy Fitness Exercise Correction

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