Hybrid lightweight Deep-learning model for Sensor-fusion basketball Shooting-posture recognition

Fan, Jingjin; Xu, Ruizhuang; Wang, Luye; Zhang, Li

doi:10.1016/j.measurement.2021.110595

Cited by 23 publications

(11 citation statements)

References 35 publications

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“…LDL-based HCI models train machines with perceptive vision systems so that the machine can perceive the surroundings and give corresponding feedback. Machine Vision (MV) technology has been widely used in many fields, such as body behavior, emotional interaction, and facial expression recognition [20][21][22]. Behavioral symbols and facial expressions, as human nonverbal expressions, can give away human emotional and psychological content more vividly and straightforwardly than words.…”

Section: Emotional Hci Technologymentioning

confidence: 99%

Applying Lightweight Deep Learning-Based Virtual Vision Sensing Technology to Realize and Develop New Media Interactive Art Installation

Luo

2022

Computational Intelligence and Neuroscience

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The work intends to optimize the situation that interactive art devices and remote control based on traditional technology cannot meet people’s actual needs to a certain extent. With the assistance of Lightweight Deep Learning (LDL) models, Interactive Artistic Installation (IAI) shows excellent creative potential in terms of dimension, space, and sense. Virtual Vision Sensing Technology (VST) explores the emotional semantics in the human-machine environment with the help of interactive art, finds the emotional interaction elements between human and machine, and promotes Human-Computer Interaction (HCI). From the perspective of the media elements of interactive art, this paper reviews the virtual VST that subverts the expression of interactive art. Then, from the perspective of artistic creation, the impact of virtual VST on IAI thinking, methods, and artistic experience is analyzed. Thereupon, a scene construction method is designed where the physical equipment is premodeled. The model is loaded in real time with visual information. The proposed method does not require complex vision and laser scanning equipment or high-configured computer systems. The proposed new media IAI model realizes the real-time loading of the scene model. According to the physical equipment dynamic information obtained by the visual data acquisition system, the proposed method can keep the virtual scene and physical models in motion synchronization. Finally, experiment results corroborate that the environment will significantly interfere with the experimental results. The training data set with boundary occlusion will be more suitable for model training and better test results (about 97% accuracy). Hence, the research content can make the Virtual Reality works have better performance, especially the sense of experience from the perspective of aesthetics. Meanwhile, it also enriches the research theory in the field of new media art installation technology.

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Section: Emotional Hci Technologymentioning

confidence: 99%

Applying Lightweight Deep Learning-Based Virtual Vision Sensing Technology to Realize and Develop New Media Interactive Art Installation

Luo

2022

Computational Intelligence and Neuroscience

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“…Fan et al [ 20 ] proposed a squeezed convolutional gated attention (SCGA) model to recognize basketball shooting postures fused by various sensors. Sardar et al [ 21 ] proposed a mobile sensor-based human physical activity recognition platform for COVID-19-related physical activity recognition, such as hand washing, hand disinfection, nose-eye contact, and handshake, as well as contact tracing, to minimize the spread of COVID-19.…”

Section: Main Recognition Techniquesmentioning

confidence: 99%

A Survey on Artificial Intelligence in Posture Recognition

Jiang¹,

Hu²,

Wang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Over the years, the continuous development of new technology has promoted research in the field of posture recognition and also made the application field of posture recognition have been greatly expanded. The purpose of this paper is to introduce the latest methods of posture recognition and review the various techniques and algorithms of posture recognition in recent years, such as scale-invariant feature transform, histogram of oriented gradients, support vector machine (SVM), Gaussian mixture model, dynamic time warping, hidden Markov model (HMM), lightweight network, convolutional neural network (CNN). We also investigate improved methods of CNN, such as stacked hourglass networks, multi-stage pose estimation networks, convolutional pose machines, and high-resolution nets. The general process and datasets of posture recognition are analyzed and summarized, and several improved CNN methods and three main recognition techniques are compared. In addition, the applications of advanced neural networks in posture recognition, such as transfer learning, ensemble learning, graph neural networks, and explainable deep neural networks, are introduced. It was found that CNN has achieved great success in posture recognition and is favored by researchers. Still, a more in-depth research is needed in feature extraction, information fusion, and other aspects. Among classification methods, HMM and SVM are the most widely used, and lightweight network gradually attracts the attention of researchers. In addition, due to the lack of 3D benchmark data sets, data generation is a critical research direction.

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“…18 katmandan oluşan SqueezeNet mimarisi tek bir evrişim katmanı (conv1), ardından sekiz Fire modülü (fire2-9) ve son olarak bir son evrişim katmanı (conv10) ile başlar [11]. SqueezeNet, giriş kanallarının sayısını azaltmak için sıkıştırma katmanları kullanılır [12]. SqueezeNet evrişim ağı kullanılır ve daha küçük ve daha etkili bir CNN mimarisi oluşturmak için yangın modüllerinin sıkıştırma ve genişletme katmanlarını kullanır [13].…”

Section: Squuezenetunclassified

InceptionResNetV2 ve Sınıf Aktivasyon Haritaları ile Akciğer Kanserinin Tespit Edilmesi

Başaran

2022

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

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Lung cancer is a life-threatening disease. According to the World Health Organization, cancer is the disease with the highest mortality rate. This disease, which is quite insidious, does not show any symptoms in the early stages. If the disease is diagnosed correctly in the early stages, it can be treated. With computed tomography, masses in the lung region can be detected and diagnosed by experienced doctors. Convolutional neural network, which is one of the deep learning methods, is successfully applied in the detection of many diseases today. When class activation maps are trained with a convolutional neural network, distinctive regions of the image are colored according to their importance, so that the important regions for the target class can be determined. In this study, a total of 1197 lung images consisting of three classes obtained by computed tomography, class activation maps were trained with the InceptionResNetV2 convolutional neural network, and the important regions of the images were determined that the features of these regions were obtained. The Obtained features were classified using support vector machines and classified with an accuracy rate of 95.44%.

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Hybrid lightweight Deep-learning model for Sensor-fusion basketball Shooting-posture recognition

Cited by 23 publications

References 35 publications

Applying Lightweight Deep Learning-Based Virtual Vision Sensing Technology to Realize and Develop New Media Interactive Art Installation

Applying Lightweight Deep Learning-Based Virtual Vision Sensing Technology to Realize and Develop New Media Interactive Art Installation

A Survey on Artificial Intelligence in Posture Recognition

InceptionResNetV2 ve Sınıf Aktivasyon Haritaları ile Akciğer Kanserinin Tespit Edilmesi

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