Application of human motion recognition utilizing deep learning and smart wearable device in sports

Zhang, Xiaojun

doi:10.1007/s13198-021-01118-7

Cited by 22 publications

(11 citation statements)

References 19 publications

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“…The ways to acquire depth information can be divided into two categories according to the different sensors used: active and passive. The active method uses sensors such as laser, radar, infrared, and ultrasonic to emit energy waves to the measured target and calculates the position of the target by calculating the TOF (Time-Of-Flight) of the energy wave [ 9 , 10 ]. This type of method has good stability, high accuracy, and fast calculation processing, but the cost of active sensors is high.…”

Section: Human Motion Recognition Algorithm Based On Depth Informationmentioning

confidence: 99%

Design of Children’s Motor Skills Training Management System Based on Human Movement Recognition Algorithm of Depth Information

Cheng

2022

Computational Intelligence and Neuroscience

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With the deepening of the concept of all-round development, the training of children’s motor skills is becoming more and more popular. Children’s motor skills training management system is based on the product of children’s motor skills training combined with modern technology. This article aims to design a management system for children’s motor skills training based on human motion recognition methods. This article proposes a human motion recognition algorithm based on depth information. The traditional human motion recognition algorithm is more biased towards children’s motor skills. For the design of the system, this article also pays great attention to children’s experience. After analyzing the performance of the system in this study, it is found that the system is not ideal for multitarget human motion recognition. Therefore, this article optimizes the recognition. After the optimization, the recognition rate of the head, upper body, and lower body of the multiobjective human motion recognition is more than 80%.

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Section: Human Motion Recognition Algorithm Based On Depth Informationmentioning

confidence: 99%

Design of Children’s Motor Skills Training Management System Based on Human Movement Recognition Algorithm of Depth Information

Cheng

2022

Computational Intelligence and Neuroscience

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“…In recent years, wearable smart devices that attract international attention are among one of them. e user can transfer the physical state of the body to the computer processing system through wearable intelligent devices and visually output it in combination with the current physical environment information of the user [2]. At present, this technology is widely used in daily life fields such as medical and healthcare, industry, and sports.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Detection and Analysis of Wearable Devices in Wushu Training

2022

Security and Communication Networks

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With the integration and development of artificial intelligence and medical technology, wearable intelligent technology has become an important health testing equipment in people’s lives, constantly testing physical function and health. However, according to the movement standards, body indicators, and constantly changing detection indexes of athletes in the exercise process, it can effectively guide athletes to use them correctly and efficiently, which has become an important task of wearable intelligent technology. In the design mode of wearable smart devices, wearable bracelets are designed with acceleration sensors, martial arts training data are measured, and machine learning technology is used to analyze and evaluate the data. When a user uses the method, a sensor is used for collecting the data, the data are transmitted to a processing platform through low-power Bluetooth, the data are analyzed through a program, the accuracy of each action is output, and finally, a standard measurement result of a section of the boxing method is combined. This paper collects and analyzes the data of body characteristics and movement characteristics of wearable intelligent devices in Wushu training. Sensor technology and filtering technology are used to collect and filter the collected information, and better analysis data are obtained. Finally, the filtered data of Wushu are analyzed, and then, the efficiency and performance of different algorithms in Wushu training are compared. Wearable intelligent equipment collects Wushu action training data and then uses fixed threshold classification to recognize Wushu action. The results show that the method used has high accuracy.

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“…For example, Xiaojun Zhang created a human motion recognition technology based on deep learning. e LSTM algorithm is used to optimize deep learning algorithms, which requires advanced smart wearables devices [5]. Bi Zhuo created a multimodal deep neural network model based on the joint cost function, which used MSR Action3D data sets to identify human motion processes.…”

Section: Related Workmentioning

confidence: 99%

Research on Methods of Physical Aided Education Based on Deep Learning

Su¹,

Feng

2022

Scientific Programming

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In order to better meet the training needs of sports and improve the standardization of sports training, an openpose-based sports posture estimation method and assisted training system are proposed, combining the basic structure and principle of openpose network. Firstly, the human posture estimation algorithm is constructed by combining with the openpose network; secondly, the overall framework, specific operation process, image acquisition, posture estimation, and other modules of the sports assistance system are designed in detail; finally, the openpose posture estimation method constructed above is validated. The results show that the value of the loss function obtained by the algorithm gradually stabilizes after 250 iterations. By using the COCO dataset as the training base and comparing it with the standard posture, it is found that the algorithm can correctly identify different badminton action postures, and the recognition rate can reach up to 94%. This shows that the algorithm is feasible and can be used for posture estimation and training of badminton sports movements.

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Application of human motion recognition utilizing deep learning and smart wearable device in sports

Cited by 22 publications

References 19 publications

Design of Children’s Motor Skills Training Management System Based on Human Movement Recognition Algorithm of Depth Information

Design of Children’s Motor Skills Training Management System Based on Human Movement Recognition Algorithm of Depth Information

Intelligent Detection and Analysis of Wearable Devices in Wushu Training

Research on Methods of Physical Aided Education Based on Deep Learning

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