Motion Capture Sensor-Based Emotion Recognition Using a Bi-Modular Sequential Neural Network

Bhatia, Yajurv; Bari, ASM Hossain; Hsu, Gee-Sern; Gavrilova, Marina L.

doi:10.3390/s22010403

Cited by 16 publications

(9 citation statements)

References 38 publications

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“…Therefore, the proposed KELM-MFF-based HMR algorithm can be used to deal with large-scale datasets. Bhatia et al ( 2022 ) also have observed that the KELM is suitable for processing large-scale datasets. At present, there are few researches on large-scale dataset processing in sports.…”

Section: Analysis Of Simulation Resultsmentioning

confidence: 98%

Applying Deep Learning-Based Human Motion Recognition System in Sports Competition

Zhang

2022

Front. Neurorobot.

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The exploration here intends to compensate for the traditional human motion recognition (HMR) systems' poor performance on large-scale datasets and micromotions. To this end, improvement is designed for the HMR in sports competition based on the deep learning (DL) algorithm. First, the background and research status of HMR are introduced. Then, a new HMR algorithm is proposed based on kernel extreme learning machine (KELM) multidimensional feature fusion (MFF). Afterward, a simulation experiment is designed to evaluate the performance of the proposed KELM-MFF-based HMR algorithm. The results showed that the recognition rate of the proposed KELM-MFF-based HMR is higher than other algorithms. The recognition rate at 10 video frame sampling points is ranked from high to low: the proposed KELM-MFF-based HMR, support vector machine (SVM)-MFF-based HMR, convolutional neural network (CNN) + optical flow (CNN-T)-based HMR, improved dense trajectory (IDT)-based HMR, converse3D (C3D)-based HMR, and CNN-based HMR. Meanwhile, the feature recognition rate of the proposed KELM-MFF-based HMR for the color dimension is higher than the time dimension, by up to 24%. Besides, the proposed KELM-MFF-based HMR algorithm's recognition rate is 92.4% under early feature fusion and 92.1% under late feature fusion, higher than 91.8 and 90.5% of the SVM-MFF-based HMR. Finally, the proposed KELM-MFF-based HMR algorithm takes 30 and 15 s for training and testing. Therefore, the algorithm designed here can be used to deal with large-scale datasets and capture and recognize micromotions. The research content provides a reference for applying extreme learning machine algorithms in sports competitions.

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Section: Analysis Of Simulation Resultsmentioning

confidence: 98%

Applying Deep Learning-Based Human Motion Recognition System in Sports Competition

Zhang

2022

Front. Neurorobot.

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“…According to Figure 12 , HAR is the top-addressed challenge, namely in

of the sample. HAR includes recognizing daily performed locomotion modes [ 54 , 55 , 56 , 57 , 153 , 154 ], analyzing the behavior of the elderly in daily life [ 58 , 134 , 135 ], gait analysis [ 59 , 155 , 156 , 157 ], etc. Monitoring is the second most studied issue, namely in

of the included papers.…”

Section: Results Analysismentioning

confidence: 99%

Context-Aware Edge-Based AI Models for Wireless Sensor Networks—An Overview

Al-Saedi

Boeva

Casalicchio

et al. 2022

Sensors

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Recent advances in sensor technology are expected to lead to a greater use of wireless sensor networks (WSNs) in industry, logistics, healthcare, etc. On the other hand, advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) are becoming dominant solutions for processing large amounts of data from edge-synthesized heterogeneous sensors and drawing accurate conclusions with better understanding of the situation. Integration of the two areas WSN and AI has resulted in more accurate measurements, context-aware analysis and prediction useful for smart sensing applications. In this paper, a comprehensive overview of the latest developments in context-aware intelligent systems using sensor technology is provided. In addition, it also discusses the areas in which they are used, related challenges, motivations for adopting AI solutions, focusing on edge computing, i.e., sensor and AI techniques, along with analysis of existing research gaps. Another contribution of this study is the use of a semantic-aware approach to extract survey-relevant subjects. The latter specifically identifies eleven main research topics supported by the articles included in the work. These are analyzed from various angles to answer five main research questions. Finally, potential future research directions are also discussed.

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“…The micro-average precision (mAP) represents the sum of all true positives divided by the sum of all true positives plus the sum of all false positives. Thus, the number of correctly identified predictions is divided by the total number of predictions [ 58 ].…”

Section: Methodsmentioning

confidence: 99%

Sea Mine Detection Framework Using YOLO, SSD and EfficientDet Deep Learning Models

Munteanu

Diana

Zamfir

et al. 2022

Sensors

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In the context of new geopolitical tensions due to the current armed conflicts, safety in terms of navigation has been threatened due to the large number of sea mines placed, in particular, within the sea conflict areas. Additionally, since a large number of mines have recently been reported to have drifted into the territories of the Black Sea countries such as Romania, Bulgaria Georgia and Turkey, which have intense commercial and tourism activities in their coastal areas, the safety of those economic activities is threatened by possible accidents that may occur due to the above-mentioned situation. The use of deep learning in a military operation is widespread, especially for combating drones and other killer robots. Therefore, the present research addresses the detection of floating and underwater sea mines using images recorded from cameras (taken from drones, submarines, ships and boats). Due to the low number of sea mine images, the current research used both an augmentation technique and synthetic image generation (by overlapping images with different types of mines over water backgrounds), and two datasets were built (for floating mines and for underwater mines). Three deep learning models, respectively, YOLOv5, SSD and EfficientDet (YOLOv5 and SSD for floating mines and YOLOv5 and EfficientDet for underwater mines), were trained and compared. In the context of using three algorithm models, YOLO, SSD and EfficientDet, the new generated system revealed high accuracy in object recognition, namely the detection of floating and anchored mines. Moreover, tests carried out on portable computing equipment, such as Raspberry Pi, illustrated the possibility of including such an application for real-time scenarios, with the time of 2 s per frame being improved if devices use high-performance cameras.

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Motion Capture Sensor-Based Emotion Recognition Using a Bi-Modular Sequential Neural Network

Cited by 16 publications

References 38 publications

Applying Deep Learning-Based Human Motion Recognition System in Sports Competition

Applying Deep Learning-Based Human Motion Recognition System in Sports Competition

Context-Aware Edge-Based AI Models for Wireless Sensor Networks—An Overview

Sea Mine Detection Framework Using YOLO, SSD and EfficientDet Deep Learning Models

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