An Improved Kinect-Based Real-Time Gesture Recognition Using Deep Convolutional Neural Networks for Touchless Visualization of Hepatic Anatomical Mode

Liu, Jiaqing; Furusawa, Kotaro; Tateyama, Tomoko; Iwamoto, Yutaro; Chen, Yen‐Wei

doi:10.18178/joig.7.2.45-49

Cited by 10 publications

(3 citation statements)

References 10 publications

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“…Developed a machine learning model using the Graph Convolutional Network (GCN) architecture. The input data has a size of [93,3,10], where 93 represents the number of samples, 3 is the number of input features, and 10 is the length of each feature. The model consists of three GCN layers: conv1, conv2, and conv3.…”

Section: Resultsmentioning

confidence: 99%

“…Another paper focuses on the challenge of maintaining a sterile surgical environment while effectively visualizing 3D medical images during operations. For more details, see [10] To address this issue, the authors propose an enhanced realtime gesture recognition system using deep convolutional neural networks and a Microsoft Kinect device. They developed a new dataset featuring 25 distinct hand gestures for deep learning, selecting the nine most accurate gestures for a touchless visualization system.…”

Section: Literature Surveymentioning

confidence: 99%

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ML-Based Approach for Enhancing Sewing Operator Training in the Apparel Industry Using Hand Movement Recognition

Tharuka Rathnayake,

Rathnayake,

A. Karunasena

et al. 2023

IRJIET

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In the apparel industry, the training of sewing operators plays a pivotal role in ensuring the production of top-quality garments. This research presents a novel approach to improve training methods through the implementation of a real-time hand movement recognition system. This system is designed to identify omissions and incorrect hand actions, providing immediate alerts based on Garment Standard Data (GSD) for prompt corrective actions. Leveraging advanced computer vision techniques and a graph neural network (GNN), the framework achieves an impressive 85.7% accuracy in monitoring and analyzing sewing operators' hand movements. By comparing detected movements with predefined standards, the system identifies deviations and offers instant feedback to operators. Experimental results underscore the system's effectiveness in pinpointing incorrect steps and hand movements, highlighting the potential of GNNs to elevate training in the apparel industry. The developed system significantly enhances sewing operator efficiency and productivity, ultimately leading to the production of higher-quality garments.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

ML-Based Approach for Enhancing Sewing Operator Training in the Apparel Industry Using Hand Movement Recognition

Tharuka Rathnayake,

Rathnayake,

A. Karunasena

et al. 2023

IRJIET

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“…As Table 1 shows, a combination of the Histogram of Oriented Gradients (HOG) for feature extraction and Support Vector Machine (SVM) for gesture recognition ( [24], [33]) is a popular approach among researchers. The same combination was used at an earlier stage of [26] research, however, a deep learning AlexNet model eventually was chosen due to a better performance. In [31] the authors emphasized an influence of a distance at which gestures were recognized by Kinect on a recognition accuracy.…”

Section: Gesture Recognition Methodsmentioning

confidence: 99%

An Overview of Kinect Based Gesture Recognition Methods

Alexeev,

Tsoy,

Edgar A.

et al. 2024

Proceedings of International Conference on Artificial Life and Robotics

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Visual sensors play an important role in a broad variety of robotic systems applications. Even though Kinect technology appeared over 10 years ago, Kinect sensors are still actively employed by researchers around the world. This paper presents an overview of Kinect and Kinect 2 sensors' applications in a human gesture based control. We analyzed existing research papers to estimate a popularity of particular feature extraction and gesture recognition methods, recommendations on a distance between an object of interest and a sensor, reported accuracy and latency of the sensor. Our analysis is supposed to facilitate a selection of a suitable combination of methods for a particular application of Kinect sensor in gesture recognition while considering its performance.

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Research on gesture recognition method based on PSO optimized LSTM for surface muscle signals

Dai,

Lu,

Qin

et al. 2024

2024 6th Asia Symposium on Image Processing (ASIP)

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An Improved Kinect-Based Real-Time Gesture Recognition Using Deep Convolutional Neural Networks for Touchless Visualization of Hepatic Anatomical Mode

Cited by 10 publications

References 10 publications

ML-Based Approach for Enhancing Sewing Operator Training in the Apparel Industry Using Hand Movement Recognition

ML-Based Approach for Enhancing Sewing Operator Training in the Apparel Industry Using Hand Movement Recognition

An Overview of Kinect Based Gesture Recognition Methods

Research on gesture recognition method based on PSO optimized LSTM for surface muscle signals

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