CNN-SkelPose: a CNN-based skeleton estimation algorithm for clinical applications

Zavala-Mondragón, Luis A.; Lamichhane, Bishal; Zhang, Lu; Haan, G. de

doi:10.1007/s12652-019-01259-5

Cited by 14 publications

(4 citation statements)

References 15 publications

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“…Aiming at these problems, this study proposes an English speaking assisted teaching system based on DLA. e classic DLAs include convolutional neural network (CNN) [18], deep neural network (DNN) [19], and LSTM [20]. In this study, to improve the pronunciation accuracy of the model for oral English, a hybrid deep neural network is used.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Assisted Teaching System for Oral English

Liu

2022

Security and Communication Networks

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The progress of global economic integration has forced English learners to have an urgent need to improve their oral English. College students’ oral English ability is currently the worst of the four abilities of listening, speaking, reading, and writing. The main reasons are internal and external. The internal reason is that the pronunciation characteristics of Chinese students are different from those of English. The external cause is that the practice environment and tools of oral English are not ideal, which affects the improvement of learners’ oral English. This study proposes using a deep learning algorithm (DLA) English in the evaluation of oral English quality to improve learners’ oral English level. The quality of oral English can be comprehensively evaluated in terms of pitch, speed of sound, and rhythm. The standard of pronunciation is the foundation of oral English and is the most critical factor. In many DLAs, the input unit of DNN at a certain moment and its upper and lower moment input units have no relationship and are independent of each other, and the timing dependencies of adjacent units are not fully considered. The results are generally not very good on speech recognition tasks. This study proposes a time-delay neural network (TDNN) and a long short-term memory (LSTM) network to calculate the posterior probability of the model state to model context-dependent features in order to solve this problem. The fusion model TDNN-LSTM is applied in the English spoken pronunciation recognition task. To compare the accuracy of oral English pronunciation, several classic DLAs are introduced. The experimental results show that the method described in this study has a number of advantages. Although the performance improvement of this method in terms of recognition accuracy is not large, a certain degree of improvement is also very important for the oral English teaching assistant system.

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Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Assisted Teaching System for Oral English

Liu

2022

Security and Communication Networks

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“…They used vision based sensor pose tracking sensors for analysis of gait over time for spatio temporal measures dementia of the elders. L. A. Zavala-Mondragon et al proposed CNN-SkelPose [28], which is a CNN-based skeleton model estimation method for clinical applications. Here, the authors used depth images for skeleton estimation for patient monitoring.…”

Section: Recent Related Workmentioning

confidence: 99%

A Deep Transfer Learning-based Edge Computing Method for Home Health Monitoring

Sufian

You

Dong

2021

2021 55th Annual Conference on Information Sciences and Systems (CISS)

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The health-care gets huge stress in a pandemic or epidemic situation. Some diseases such as COVID-19 that causes a pandemic is highly spreadable from an infected person to others. Therefore, providing health services at home for noncritical infected patients with isolation shall assist to mitigate this kind of stress. In addition, this practice is also very useful for monitoring the health-related activities of elders who live at home. The home health monitoring, a continuous monitoring of a patient or elder at home using visual sensors is one such nonintrusive sub-area of health services at home. In this article, we propose a transfer learning-based edge computing method for home health monitoring. Specifically, a pre-trained convolutional neural network-based model can leverage edge devices with a small amount of ground-labeled data and fine-tuning method to train the model. Therefore, on-site computing of visual data captured by RGB, depth, or thermal sensor could be possible in an affordable way. As a result, raw data captured by these types of sensors is not required to be sent outside from home. Therefore, privacy, security, and bandwidth scarcity shall not be issues. Moreover, real-time computing for the above-mentioned purposes shall be possible in an economical way.

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“…Given the above, through the HMD-VR environment the reflection of African philosophy in African literature can be enjoyed. The electroencephalogram (EEG) is used to study emotion recognition, and the data is transmitted to the control center to capture the user's psychological state on the analysis of multiscale convolution kernel convolutional neural networks (CNN), pushing more suitable and more wonderful scenes for user's emotional fluctuations (Zavala-Mondragon et al, 2020;Gao Q. et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Human–Computer Interaction-Oriented African Literature and African Philosophy Appreciation

Wen

Piao

2022

Front. Psychol.

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African literature has played a major role in changing and shaping perceptions about African people and their way of life for the longest time. Unlike western cultures that are associated with advanced forms of writing, African literature is oral in nature, meaning it has to be recited and even performed. Although Africa has an old tribal culture, African philosophy is a new and strange idea among us. Although the problem of “universality” of African philosophy actually refers to the question of whether Africa has heckling of philosophy in the Western sense, obviously, the philosophy bred by Africa’s native culture must be acknowledged. Therefore, the human–computer interaction-oriented (HCI-oriented) method is proposed to appreciate African literature and African philosophy. To begin with, a physical object of tablet-aid is designed, and a depth camera is used to track the user’s hand and tablet-aid and then map them to the virtual scene, respectively. Then, a tactile redirection method is proposed to meet the user’s requirement of tactile consistency in head-mounted display virtual reality environment. Finally, electroencephalogram (EEG) emotion recognition, based on multiscale convolution kernel convolutional neural networks, is proposed to appreciate the reflection of African philosophy in African literature. The experimental results show that the proposed method has a strong immersion and a good interactive experience in navigation, selection, and manipulation. The proposed HCI method is not only easy to use, but also improves the interaction efficiency and accuracy during appreciation. In addition, the simulation of EEG emotion recognition reveals that the accuracy of emotion classification in 33-channel is 90.63%, almost close to the accuracy of the whole channel, and the proposed algorithm outperforms three baselines with respect to classification accuracy.

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CNN-SkelPose: a CNN-based skeleton estimation algorithm for clinical applications

Cited by 14 publications

References 15 publications

A Deep Learning-Based Assisted Teaching System for Oral English

A Deep Learning-Based Assisted Teaching System for Oral English

A Deep Transfer Learning-based Edge Computing Method for Home Health Monitoring

Human–Computer Interaction-Oriented African Literature and African Philosophy Appreciation

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