scholarly journals IoT-assisted physical education training network virtualization and resource management using a deep reinforcement learning system

2021 ◽  
Author(s):  
Qiang Li ◽  
PriyanMalarvizhi Kumar ◽  
Mamoun Alazab
Keyword(s):  
Physical Activity ◽  
Physical Education ◽  
Simulation Analysis ◽  
Activity Patterns ◽  
Activity Monitoring ◽  
Learning System ◽  
Performance Ratio ◽  
Essential Information ◽  
Physical Activity Monitoring ◽  
And Training

AbstractThe Internet of Things (IoT) development made it possible for technology to communicate physical education by connecting cost-effective heterogeneous devices and digital applications to uncontrolled and accessible environments. The traditional physical education monitoring environment creates crucial manual efforts on athletes' activity observations and tracking consistently. Similarly, remote monitoring and assessment of athletes in sports training seem to be barriers to physical education monitoring and training. It creates various chances to improve training and education through technology advancements like IoT and deep learning. Students can efficiently monitor their physical behavior to increase their physical and psychological benefits. The IoT-assisted physical activity monitoring device is proposed to track students' physical activity and enhance outcomes. The management ability allows students to organize and increase speed their physical activity in a wellness manner. In addition, this study examines the connections between monitoring ability which is an essential component for sports activities and physical activity. This system collects essential information from IoT-based wearable devices that interact with the data in real time by virtualizing the device. The IoT network includes several device activities and monitors the heartbeat and physical body temperature of a person. The analysis of specific studies and student feedback shows that the designed virtual system of physical educations is effective in its application and implementation and provides a reliable guide for developing student physical educational systems. The experimental analysis is evaluated; the solution offered is developing and supporting physical education and training approaches in reality and creates healthy environment systems to solve the health monitoring challenges posed by IoT devices. The proposed method has achieved extraordinary physical activity monitoring compared to the conventional systems, as shown by experimental findings. The simulation analysis of physical education can help students and improve the associated aspects of physical abilities with high accuracy ratio (98.3), prediction ratio (96.5%), interaction ratio (94.4%), performance ratio (95.1%), the efficiency ratio (93.2), F-score (92.2%), and reduce error rate (17.5%) and physical activity patterns.

scholarly journals The Intricacies of Children’s Physical Activity

2015 ◽  
Vol 47 (1) ◽  
pp. 269-275 ◽  
Author(s):  
Timothy A Brusseau
Keyword(s):  
Physical Activity ◽  
Physical Education ◽  
Activity Patterns ◽  
Activity Levels ◽  
Physical Activity Patterns ◽  
Change Behavior ◽  
Before And After ◽  
School Recess ◽  
Physical Activity Programming ◽  
Children’S Physical Activity

AbstractUnderstanding the physical activity patterns of youth is an essential step in preparing programming and interventions needed to change behavior. To date, little is known about the intricacies of youth physical activity across various physical activity segments (i.e. in school, out of school, recess, classroom physical activity, physical education, weekends, etc.). Therefore, the purpose of the study was to examine the physical activity patterns of elementary school children across various segments and during two seasons. A total of 287 fourth and fifth graders from the Southwest US wore the Yamax Digiwalker SW-200 pedometer for 7 consecutive days during the Fall and Spring seasons. Children were prompted to record their step counts when arriving and leaving school, before and after physical education and recess, as well as on the weekends. Means and standard deviations were calculated and ANOVAs and t tests were utilized to examine difference by sex, season, and segment. Youth were more active outside of school and on weekdays (p<0.05). Boys were generally more active than girls and all youth were more active during the milder Spring season. There is a clear need for Comprehensive School Physical Activity Programming and weekend physical activity opportunities. Furthermore, greater emphasis is needed on PE and across other activity segments for girls to increase their physical activity levels.

Validity of Garmin Vívofit 1 and Garmin Vívofit 3 for School-Based Physical Activity Monitoring

2019 ◽  
Vol 31 (1) ◽  
pp. 130-136 ◽  
Author(s):  
Adam Šimůnek ◽  
Jan Dygrýn ◽  
Lukáš Jakubec ◽  
Filip Neuls ◽  
Karel Frömel ◽  
...  
Keyword(s):  
Physical Activity ◽  
Activity Patterns ◽  
Physical Activity Promotion ◽  
After School ◽  
School Based ◽  
Physical Activity Monitoring ◽  
Activity Promotion ◽  
Before School ◽  
Criterion Method ◽  
Free Living Conditions

Purpose:Activity trackers are useful tools for physical activity promotion in adolescents, but robust validity evaluations have not been done under free-living conditions. This study evaluated the validity of the Garmin Vívofit 1 (G1) and Garmin Vívofit 3 (G3) in different settings and contexts.Methods:The participants (girls: 52%, age: 15.9 [1.9] y) wore the G1 and G3 on their nondominant wrist and the Yamax pedometer on their right hip for a period of 1 week. Validity was examined in 4 discrete segments (before school, in school, after school, and whole day). The criterion method was the Yamax pedometer.Results:Both the G1 and G3 could be considered equivalent to the Yamax pedometer regarding the before school, in school, and whole day segments. The G1 showed wider limits of agreement than G3.Conclusions:The G1 and G3 trackers exhibited acceptable validity for 3 of the 4 segments (before school, in school, and whole day measurements). The results were less accurate during the after-school segment. The evidence that the validity of the monitors varied depending on the setting and context is an important consideration for research on adolescent activity patterns.

Effectiveness of Secondary School Conceptual Physical Education: A 20-Year Longitudinal Study

2018 ◽  
Vol 15 (12) ◽  
pp. 927-932 ◽  
Author(s):  
Pamela H. Kulinna ◽  
Charles B. Corbin ◽  
Hyeonho Yu
Keyword(s):  
Physical Activity ◽  
High School ◽  
Physical Education ◽  
Activity Patterns ◽  
High School Graduation ◽  
National Sample ◽  
Moderate Physical Activity ◽  
Physically Active ◽  
Comparison Groups ◽  
School Graduation

Background: Previous research findings from Project Active Teen demonstrated the effectiveness of high school conceptual physical education (CPE) in promoting active lifestyles. Method: This study followed Project Active Teen participants 20 years after graduation from high school and 24 years after taking a CPE class. Physical activity behaviors were assessed using the same procedures as previous Project Active Teen studies. Activity patterns were compared with patterns while in high school and shortly after high school graduation. Activity patterns were also compared with a national sample of age-equivalent adults. Results: Twenty years after high school graduation, former CPE students were less likely to be inactive and more likely to be moderately active than when in high school and were less likely to be inactive and more likely to be moderately active than national sample age-equivalent peers. They were typically not more vigorously physically active than comparison groups. Conclusion: Results support the long-term effectiveness of CPE in reducing inactive behavior and promoting moderate physical activity later in life.

Sign in / Sign up

Export Citation Format

Share Document