Human Motion Target Posture Detection Algorithm Using Semi-Supervised Learning in Internet of Things

Chen, Lei; Li, Shangbin

doi:10.1109/access.2021.3091430

Cited by 15 publications

(6 citation statements)

References 35 publications

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“…Using the voiced energy decision method based on SIFT described above, to solve the judgment error of the octave/half-frequency relationship commonly existing in the traditional pitch extraction algorithm, and to reduce the complexity of the voiced energy decision method, the pitch period is roughly estimated through open-loop pitch analysis and recorded as top [17]. On this basis, the candidate closed-loop pitch set δ is set by using the information of the first and last frames of the frame in the UN subtracted sampling data and the values around the open-loop pitch value.…”

Section: Open Loop Closed-loop Pitch Extractionmentioning

confidence: 99%

Waveform Feature Extraction of Intelligent Singing Skills under the Background of Internet of Things

Luo

2022

Mobile Information Systems

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Aiming at the complex and changeable characteristics of intelligent singing skills in the context of Internet of Things, this paper proposes a feature extraction method suitable for intelligent singing skills in this context. Firstly, focusing on vocal features, the time-domain algorithm based on open-loop and closed-loop gene extraction extracts the genetic features of songs with accompaniment; then, the section and its features are extracted by using the windowed moving matching algorithm, and the segments are divided by using the similarity between adjacent segments to obtain the segment features with emotional factors. The segment features are input into the improved BP emotion recognizer for emotion recognition. Finally, the intelligent singing skills of the whole music are determined. The experimental results show that, with the increase in feature extraction time, the accuracy of the extraction results of the existing methods changes little, which is basically maintained at a low level between 15% and 30%. When the proposed method is for feature extraction of intelligent singing skill information, the accuracy shows a continuous growth trend, and with the growth of time, its accuracy is significantly higher than the existing methods, indicating that the proposed method has significant advantages in the accuracy of feature extraction. Because this waveform feature extraction method is applied to the intelligent singing skills under the background of the Internet of Things, it has the advantages of high extraction efficiency, high accuracy, and reliability.

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Section: Open Loop Closed-loop Pitch Extractionmentioning

confidence: 99%

Waveform Feature Extraction of Intelligent Singing Skills under the Background of Internet of Things

Luo

2022

Mobile Information Systems

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“…According to the key running method, for novice longdistance runners, developing wrong running habits can easily cause different degrees of sports injuries to the body [11].…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Running Posture Detection Based on Artificial Intelligence Combined with Sensor

Lai

Wang

et al. 2022

Journal of Sensors

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In order to avoid injuries caused by incorrect running posture to a greater extent and reduce the impact on athletes’ performance and physical health, on the basis of artificial intelligence sensors, the author studies the accurate detection of intelligent running motion posture. Using artificial intelligence sensors, an adaptive error quaternion unscented Kalman filter (DAUKF) algorithm is designed. The attitude analysis and recognition system based on the inertial measurement unit can not only measure the motion information of human body but also obtain the motion characteristic data and movement state of the human body through the analysis of posture data. Use the error quaternion and gyroscope drift error to establish the equation of state, the measurement values of the accelerometer and magnetometer are used to establish the observation equation, and the fading memory method is introduced to adaptively adjust the observation noise covariance, so as to reduce the interference of the system itself and the environment on attitude detection. Experimental results show that the proposed method improves the attitude detection accuracy, effectively suppresses the influence of drift error and dynamic observation noise, and provides a foot attitude detection scheme suitable for long-distance running.

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“…Videobased pose recognition usually means inputting video data and extracting and analyzing video features through various image processing and recognition methods [2]. In order to achieve the purpose of human action recognition in video, it has a wide range of applications [3. e key of video-based posture state recognition is to extract appropriate video features and analyze and recognize these features reasonably and accurately [4]. Physical motion recognition technology is widely used in various elds.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Posture Recognition for Motion-Assisted Evaluation

Yuan

Zheng

2022

Mobile Information Systems

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With the development of computer vision technology, human action pose recognition has gradually become a popular research direction, but there are still some problems in the application research based on pose recognition in sports action assisted evaluation. In this paper, the human motion pose recognition technology based on deep learning is introduced into this field to realize the intelligence of sports-assisted training. Firstly, we analyze the advantages and limitations of the state-of-the-art human motion pose recognition algorithms in computer vision in specific fields. On this basis, a human motion space recognition method based on periscope neural network is proposed. Firstly, the classical radar signal processing method is used to preprocess the echo signal of human spatial position and generate the frequency image in the process of human spatial position. Then, the periscope neural network (CNN) is constructed, and the time-frequency image is used as the input data of CNN to train the network parameters. Finally, the method is tested by using the open dataset in the network. The experimental results show that the designed CNN can accurately identify four different types of physical motion, and the accuracy coefficient is at least 97%.

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Human Motion Target Posture Detection Algorithm Using Semi-Supervised Learning in Internet of Things

Cited by 15 publications

References 35 publications

Waveform Feature Extraction of Intelligent Singing Skills under the Background of Internet of Things

Waveform Feature Extraction of Intelligent Singing Skills under the Background of Internet of Things

Intelligent Running Posture Detection Based on Artificial Intelligence Combined with Sensor

Deep Learning-Based Posture Recognition for Motion-Assisted Evaluation

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