-
Preventive medicine · Aug 2023
Application of mobile sensors based on deep neural networks in sports psychological detection and prevention.
- Lei Lu, Yubao Xi, and Xiaolin Zhang.
- Physical Education College, Anhui Normal University, Wuhu, Anhui 241000, China; Physical Education College, Anqing Normal University, Anqing, Anhui 246001, China.
- Prev Med. 2023 Aug 1; 173: 107613107613.
AbstractThis article proposes a low-power, low-cost, programmable neural network processor for IoT applications. Aiming at the human inertial motion capture system, a human motion sensor detection method based on long- and short-term memory network is proposed. It provides a practical method for studying human motion detection based on mobile sensors and portable devices. Based on the research of human movement sensor detection method based on long and short-term memory network, this paper analyzes the current research status of sports psychological fatigue detection through the production mechanism, external performance, evidence and entertainment measures of sports psychological fatigue, in order to obtain a better solution. Quickly eliminate the mental fatigue of athletes. At the same time, improve and improve the physical and psychological functions of athletes of different qualities, and further improve sports performance. So far, the theoretical definition of sports mental fatigue and the operational definition of sports mental fatigue are still the center of discussion and research by sports psychologists. This article will also elaborate on the development of the most important concepts describing sports fatigue at home and abroad, using depth Neural network and mobile sensor technology have carried out research and exploration on the mental recognition of athletes in sports.Copyright © 2023 Elsevier Inc. All rights reserved.
Notes
Knowledge, pearl, summary or comment to share?You can also include formatting, links, images and footnotes in your notes
- Simple formatting can be added to notes, such as
*italics*
,_underline_
or**bold**
. - Superscript can be denoted by
<sup>text</sup>
and subscript<sub>text</sub>
. - Numbered or bulleted lists can be created using either numbered lines
1. 2. 3.
, hyphens-
or asterisks*
. - Links can be included with:
[my link to pubmed](http://pubmed.com)
- Images can be included with:
![alt text](https://bestmedicaljournal.com/study_graph.jpg "Image Title Text")
- For footnotes use
[^1](This is a footnote.)
inline. - Or use an inline reference
[^1]
to refer to a longer footnote elseweher in the document[^1]: This is a long footnote.
.