A New Kinect-Based Posture Recognition Method in Physical Sports Training Based on Urban Data

He, Dianchen; Li, Li

doi:10.1155/2020/8817419

Cited by 9 publications

(3 citation statements)

References 23 publications

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“…This experiment performs 3D human pose detection studies using the approaches of HPDM, HPDFLML, HPDMF, HPDDS, and HPDMK in addition to detection of the suggested method.…”

Section: Resultsmentioning

confidence: 99%

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Recent Advances in Nanosensors for Motion Detection

Xiu,

Yin,

Zhang

et al. 2024

ACS Appl. Electron. Mater.

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The continuous development of nanosensor technology has brought revolutionary changes to motion detection in the field of sports. The small size and high sensitivity of nanosensors make them ideal for real-time motion data acquisition. This paper aims to explore the potential applications of nanosensors in the field of sports, especially in the field of motion monitoring. We first introduce the working principles and types of nanosensors and then discuss the different manufacturing methods and practical applications of each sensor and the technical challenges that these sensors face. We then discuss its application in different aspects. The application of these nanosensors has intriguing prospects in the realm of motion detection as well as a plethora of other options for human−computer interaction. Research on the resilience of these sensors and the trend toward lower energy use will remain crucial. Future developments in nanosensors include obtaining energy from the human body, shrinking and using less power, increasing robustness and dependability, and lowering costs. These developments could encourage the broad usage of nanosensors for motion detection, opening up application possibilities and improving user satisfaction all around.

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“…This experiment performs 3D human pose detection studies using the approaches of HPDM, HPDFLML, HPDMF, HPDDS, and HPDMK in addition to detection of the suggested method.…”

Section: Resultsmentioning

confidence: 99%

“…In another context, He and Li demonstrated a Kinect-based method for identifying human posture. Their method includes utilizing Kinect to determine the coordinates of joints.…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Nanosensors for Motion Detection

Xiu,

Yin,

Zhang

et al. 2024

ACS Appl. Electron. Mater.

View full text Add to dashboard Cite

show abstract

“…The explanation is that athletes with a high athletics level have more detailed information about mental training techniques, and some of them, therefore, use these methods. (He et al, 2020) suggested a Kinect-based posture recognition method (KPRM) for Physical Sports Training Based on Urban Data. Kinect firstly uses the spatial coordinates of human body joints.…”

Section: Significance Of Student Sports Trainingmentioning

confidence: 99%

Evaluating the Efficiency of Student Sports Training Based on Supervised Learning

Song

Díaz

Kadry

2022

International Journal of Technology and Human Interaction

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The empirical evaluation of the success of a participant is critical for a thorough assessment of sporting events. Evaluating students' efficiency or scripting in sports is limited, even if skilled experts do it. In this paper, support vector machine-assisted sports training (SVMST) has been proposed to evaluate student sports efficiency. Sports training prototypes are based on different criteria that participate in the matches, traditional game statistics, person quality measures, and opposing data. The success of students is divided into two grades: moderate and large. The primarily supervised learning-based classification method is used to create a template for identifying student sports training efficiency. SVM implements learning methods, data collection methods, effective model assessment methods, and particular difficulties in predicting sports performance. The experimental results show SVMST to high student performance of 98.7%, a low error rate of 9.8%, enhanced assessment ratio of 97.6%, training outcome of 95.6%, and an efficiency ratio of 96.8%.

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A Unified Approach for Patient Activity Recognition in Healthcare Using Depth Camera

et al. 2021

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Context-awareness is an essential part of pervasive computing. Video-based human activity recognition (HAR) has arisen as an imperative module to detect user's situation for involuntary facility delivery in context-aware domains. The activity recognition systems are frequently employed for protective and practical health care. Most of the existing works utilize RGB (red, green, and blue) cameras which present confidentiality and security concerns in the health-care domain. The existing approaches also do not sustain their performance results under the presence of a depth camera. Moreover, the accuracy of an HAR system relies on the extraction and selection of the prominent features from the feature space. To address these limitations, in this research, we first employ a depth camera to resolve the confidentiality and security concerns, and propose an unsupervised segmentation algorithm that can accurately segment the human body from the video frame. Then, we propose a new feature selection technique that has the ability to excerpt and select the best features from the feature space. Our proposed feature selection method focuses on the selection of confined features from the series of images and discriminate their category based on reversion (i.e., regression) value. The proposed method extracts and selects the best features through the benefits of forward selection and backward regression algorithms. Finally, we have trained and tested our proposed system by employing hidden Markov model (HMM) to label the activities. The proposed approach presents a significant performance against the existing works using depth camera.

show abstract

A New Kinect-Based Posture Recognition Method in Physical Sports Training Based on Urban Data

Cited by 9 publications

References 23 publications

Recent Advances in Nanosensors for Motion Detection

Recent Advances in Nanosensors for Motion Detection

Evaluating the Efficiency of Student Sports Training Based on Supervised Learning

A Unified Approach for Patient Activity Recognition in Healthcare Using Depth Camera

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