Gesture Tracking and Recognition Algorithm for Dynamic Human Motion Using Multimodal Deep Learning

Xia, Zhonghua; Xing, Jinming; Li, Xiaofeng

doi:10.1155/2022/4387337

Cited by 10 publications

(8 citation statements)

References 19 publications

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“…According to the distance between frames obtained from the calculation above, the distance curve F(g i , g k ) is obtained. e key frame of the human motion rehabilitation training video is the frame corresponding to the extreme point in the curve [10,11], which completes the key frame extraction of the human motion rehabilitation training video.…”

Section: Extraction Of Key Framesmentioning

confidence: 99%

Effect Measurement for Human Motion Rehabilitation Training Using Improved Deep Reinforcement Learning and IoT Networks

Zhu

Liu

2022

Security and Communication Networks

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Measuring the effect of human motion rehabilitation training is important to help persons develop motion rehabilitation training plans. The current human motion rehabilitation training effect measurement algorithm has the problems of too large gap between the smoothness of the motion speed curve and the reality, high key frame extraction error rate, low measurement accuracy, long measurement time, and low satisfaction. Therefore, this paper proposes a human motion rehabilitation training effect measurement algorithm using improved deep reinforcement learning and Internet of Things (IoT) networks using IoT network technology to collect human motion rehabilitation training videos. The key frames of the human motion rehabilitation video data are extracted according to the interframe distance, and the metalearning method is used to improve the deep reinforcement learning network, and the obtained key frames are input into the improved deep reinforcement learning network to obtain the motion speed curve of human motion rehabilitation training. The smoothness of the motion velocity curve is calculated, and high smoothness indicates a good human motion rehabilitation training effect, while low smoothness indicates a poor human motion rehabilitation training effect, so as to complete the measure of human motion rehabilitation training effect. The results show that the smoothness of the motion speed curve of the proposed algorithm is closer to reality, the average error rate of key frame extraction is 1.45%, the measurement accuracy of rehabilitation training is more than 90%, the measurement time is controlled below 2.1 s, and the maximum user satisfaction is 93.1, which shows that the practical application of the algorithm is good.

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Section: Extraction Of Key Framesmentioning

confidence: 99%

Effect Measurement for Human Motion Rehabilitation Training Using Improved Deep Reinforcement Learning and IoT Networks

Zhu

Liu

2022

Security and Communication Networks

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“…As shown in Table 1, we evaluated the recognition accuracy and computational efficiency of our gesture recognition compared to other algorithms. [23][24][25] The classification accuracy was measured by the ratio between the number of correctly predicted hand gestures and the total number of validation dataset.…”

Section: Gesture Recognition Experimentsmentioning

confidence: 99%

GestureTeach: A gesture guided online teaching interactive model

Liu,

Yao,

Zhang

et al. 2023

Computer Animation & Virtual

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Online education has become more popular and effective due to the availability of high‐speed internet and technological innovations, which allow people from different locations to access educational resources and opportunities. However, online classes often face challenges such as limited interactivity and display options, which can affect the quality and effectiveness of the online learning experience. In this article, we propose GestureTeach, a new pedagogical paradigm that enables free handwriting interaction and animation generation for online teaching. GestureTeach uses gestures as a natural and intuitive way of interaction, which enhances the teacher's intention expression and the student's engagement. GestureTeach also generates animations from handwritten sketches, which improves the display effects of the interaction and the student's knowledge comprehension. We conducted a two‐stage study with 15 teachers and 90 students to evaluate the effectiveness of GestureTeach in facilitating classroom interaction. The results show that GestureTeach is preferred by both teachers and students over traditional online teaching methods and has the potential to transform the online teaching landscape by providing a seamless and interactive experience.

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“…Equation ( 13) is used as the multi-labeling classifier of human posture features in this study, and the multi-labeling classifier [19][20] is used to extract HMP features, which lays a solid foundation for the subsequent HMP estimation.…”

Section: Feature Extraction Of Hmpmentioning

confidence: 99%

Estimation of Human Motion Posture Using Multi-labeling Transfer Learning

Wang

Ren

et al. 2023

Braz. arch. biol. technol.

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Human posture estimation is the basis of many computer vision tasks, such as motion recognition, violence detection and behavior understanding. Therefore, it is of great significance to study the estimation algorithm of human motion posture (HMP). To address the problem of poor estimation effect of traditional HMP estimation algorithm, in this paper, an estimation algorithm for HMP using multi-labeling transfer learning is proposed. First, the original human motion image is labeled by using the multi-label transfer learning, the HMP features are extracted, and the original image classification is completed. Second, a regulator is constructed based on the classification results of the original image, and the regulator is used to adjust the estimation results of HMP based on a convolutional neural networks. Finally, the posture compensation function is used to compensate for the error part to realize the estimation of HMP. In the experiment, the Human3.6m data set and MPII data set were used as the basis for testing. The results show HIGHLIGHTS• This paper addresses the problem that it is difficult to mine and extract the potential features of human motion posture feature vector.• The posture compensation function is used to compensate the error part to ensure the accuracy of posture estimation results• The posture compensation function is used to correct and compensate the estimation results and improve the reliability of the estimation results.

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Gesture Tracking and Recognition Algorithm for Dynamic Human Motion Using Multimodal Deep Learning

Cited by 10 publications

References 19 publications

Effect Measurement for Human Motion Rehabilitation Training Using Improved Deep Reinforcement Learning and IoT Networks

Effect Measurement for Human Motion Rehabilitation Training Using Improved Deep Reinforcement Learning and IoT Networks

GestureTeach: A gesture guided online teaching interactive model

Estimation of Human Motion Posture Using Multi-labeling Transfer Learning

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