Human Activity Recognition and Embedded Application Based on Convolutional Neural Network

Xu, Yang; Qiu, Ting

doi:10.37965/jait.2020.0051

Cited by 84 publications

(46 citation statements)

References 15 publications

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“…Jaouedi et al [5] established a human behavior detection and recognition model using the new single-lens multibox detector (SSD) algorithm to identify human behavior better from the monitoring video, and the model shows high precision and high speed rate. Xu and Qiu [6] proposed a deep time residual system for daily life activity recognition with team members. e deep time residual model of the human activity identification system was established, which improved the performance of the human identification system.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis of Body Behavior Characteristics after Sports Training Based on Convolution Neural Network

Zhou

Wen

2021

Computational Intelligence and Neuroscience

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The use of artificial intelligence technology to analyze human behavior is one of the key research topics in the world. In order to detect and analyze the characteristics of human body behavior after training, a detection model combined with a convolutional neural network (CNN) is proposed. Firstly, the human skeleton suggestion model is established to analyze the driving mode of the human body in motion. Secondly, the number of layers and neurons in CNN are set according to the skeleton feature map. Then, the output information is classified according to the fatigue degree according to the body state after exercise. Finally, the training and performance test of the model are carried out, and the effect of the body behavior feature detection model in use is analyzed. The results show that the CNN designed in the study shows high accuracy and low loss rate in training and testing and also has high accuracy in the practical application of fatigue degree recognition after human training. According to the subjective evaluation of volunteers, the overall average evaluation is more than 9 points. The above results show that the designed convolution neural network-based detection model of body behavior characteristics after training has good performance and is feasible and practical, which has guiding significance for the design of sports training and training schemes.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis of Body Behavior Characteristics after Sports Training Based on Convolution Neural Network

Zhou

Wen

2021

Computational Intelligence and Neuroscience

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“…The trial and error method is used while running the convolution neural network [ 8 , 14 ], word embedding Keras [ 34 , 35 ], and Naïve Bayes [ 36 – 38 ]. The selection of hyperparameter is also defined as an NP-complete problem [ 39 , 40 ]. The efficient selection of hyperparameters can achieve better results [ 41 , 42 ].…”

Section: Resultsmentioning

confidence: 99%

Spoken Language Identification Using Deep Learning

Singh

Sharma

Kumar

et al. 2021

Computational Intelligence and Neuroscience

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The process of detecting language from an audio clip by an unknown speaker, regardless of gender, manner of speaking, and distinct age speaker, is defined as spoken language identification (SLID). The considerable task is to recognize the features that can distinguish between languages clearly and efficiently. The model uses audio files and converts those files into spectrogram images. It applies the convolutional neural network (CNN) to bring out main attributes or features to detect output easily. The main objective is to detect languages out of English, French, Spanish, and German, Estonian, Tamil, Mandarin, Turkish, Chinese, Arabic, Hindi, Indonesian, Portuguese, Japanese, Latin, Dutch, Portuguese, Pushto, Romanian, Korean, Russian, Swedish, Tamil, Thai, and Urdu. An experiment was conducted on different audio files using the Kaggle dataset named spoken language identification. These audio files are comprised of utterances, each of them spanning over a fixed duration of 10 seconds. The whole dataset is split into training and test sets. Preparatory results give an overall accuracy of 98%. Extensive and accurate testing show an overall accuracy of 88%.

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“…The network model is compressed on existing methods. The paper presents the recognition of six kinds of human daily activities, such as walking, sitting, standing, jogging, going upstairs, and going downstairs using neural networks on embedded devices [25].…”

Section: Ai and Robotics-based Object Detectionmentioning

confidence: 99%

Technologies Supporting Artificial Intelligence and Robotics Application Development

Chen

Luca

2021

JAIT

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Artificial intelligence (AI) and robotics have gone through three generations of development, from Turing test, logic theory machine, to expert system and self-driving car. In the third-generation today, AI and robotics have collaboratively been used in many areas in our society, including industry, business, manufacture, research, and education. There are many challenging problems in developing AI and robotics applications. We launch this new Journal of Artificial Intelligence and Technology to facilitate the exchange of the latest research and practice in AI and technologies. In this inaugural issue, we first introduce a few key technologies and platforms supporting the third-generation AI and robotics application development based on stacks of technologies and platforms. We present examples of such development environments created by both industry and academia. We also selected eight papers in the related areas to celebrate the foundation of this journal.

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Human Activity Recognition and Embedded Application Based on Convolutional Neural Network

Cited by 84 publications

References 15 publications

[Retracted] Analysis of Body Behavior Characteristics after Sports Training Based on Convolution Neural Network

[Retracted] Analysis of Body Behavior Characteristics after Sports Training Based on Convolution Neural Network

Spoken Language Identification Using Deep Learning

Technologies Supporting Artificial Intelligence and Robotics Application Development

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