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Efficient monitoring and recognition of movement are crucial in enhancing athletic performance. Traditional methods have limitations in terms of high site requirements and power consumption, making them unsuitable for long-term tracking and monitoring. A potential solution to low-power monitoring of body area networks is triboelectric sensors. However, the current analysis method for badminton triboelectric sensing data is relatively simple, while flexible, triboelectric sensors based on 3D printing face issues such as discomfort when joints are bent or twisted in a large range. In light of this, a flexible arch-shaped triboelectric sensor based on 3D printing (FA-Sensor) is proposed. By combining neural network algorithms with the signal acquisition module and the master computer, an intelligent multi-sensor node system for badminton monitoring is established. The FA-Sensor exhibits high sensitivity to bending and twisting motions due to its elastic TPE shell and arched shape design. It minimizes interference with human motion during bending (10°–150°) or twisting (20°–100°) over a wide range. The peak output voltage of the FA-Sensor demonstrates a clear functional relationship with the bending angle, exhibiting piecewise sensitivities of 7.98 and 29.28 mV/°, respectively. For seven different parts of the human body, it can be quickly customized to different sizes, with stable and repeatable response outputs. In application, the badminton sports monitoring system enables real-time feedback and recognition of four typical technical movements, achieving a recognition accuracy rate of 97.2%. The system enables athletes to analyze and enhance badminton technology while also exhibiting promising potential for application in other intelligent sports domains.
Efficient monitoring and recognition of movement are crucial in enhancing athletic performance. Traditional methods have limitations in terms of high site requirements and power consumption, making them unsuitable for long-term tracking and monitoring. A potential solution to low-power monitoring of body area networks is triboelectric sensors. However, the current analysis method for badminton triboelectric sensing data is relatively simple, while flexible, triboelectric sensors based on 3D printing face issues such as discomfort when joints are bent or twisted in a large range. In light of this, a flexible arch-shaped triboelectric sensor based on 3D printing (FA-Sensor) is proposed. By combining neural network algorithms with the signal acquisition module and the master computer, an intelligent multi-sensor node system for badminton monitoring is established. The FA-Sensor exhibits high sensitivity to bending and twisting motions due to its elastic TPE shell and arched shape design. It minimizes interference with human motion during bending (10°–150°) or twisting (20°–100°) over a wide range. The peak output voltage of the FA-Sensor demonstrates a clear functional relationship with the bending angle, exhibiting piecewise sensitivities of 7.98 and 29.28 mV/°, respectively. For seven different parts of the human body, it can be quickly customized to different sizes, with stable and repeatable response outputs. In application, the badminton sports monitoring system enables real-time feedback and recognition of four typical technical movements, achieving a recognition accuracy rate of 97.2%. The system enables athletes to analyze and enhance badminton technology while also exhibiting promising potential for application in other intelligent sports domains.
Scholarly interest in artificial intelligence (AI) has surged as researchers delve into its transformative impact on various aspects of our lives. AI poses both benefits and challenges, particularly in the context of educators' endeavors to comprehend the intricacies of students' learning processes. Although the use of AI to enhance and assist student learning is relatively new, the exponential growth of scholarly attention and publications in AI and student learning in recent years underscores the compelling necessity for further inquiry. Investigating this area is crucial for understanding the emerging trends in this research domain. This study aims to provide insights into the burgeoning research trajectories on AI from a student learning perspective. Using a bibliometric approach, this study examined 663 scholarly articles pertaining to the interface between AI and student learning published between 1961 and 2024. Our findings reveal four major thematic areas including AI in education and educational technology, AI-driven learning environments, essential AI enablers, and human learning and highlight promising avenues at this intersection.
Background and Aims: The need to explore alternative teaching methods in physical education for primary school students. This study likely aims to investigate the impact of incorporating peer-assisted techniques in badminton instruction, seeking to enhance the learning experience and outcomes for primary school students in the context of physical education. Thus, this study aimed to study the effects of peer-assisted badminton teaching selected course to improve learning outcomes in primary school students and to compare the effects of learning outcomes between the pretest and post-test Methodology: The sample consisted of 40 students from simple random sampling in five badminton classrooms, examined through pretest and post-tests Forehand deep high service skill, Forehand high clear skill, drop shot skill, forehand skill, backhand skill, and satisfaction survey on peer-assisted badminton course teaching. The experiment was conducted by following the peer-assisted badminton teaching selected course, 10 weeks duration. Then the data were prepared and analyzed statistically with a packet computer program to compute the mean and standard deviation and compare teaching achievement using a t-test dependent. (*p<.05) Results: (1) The effects of peer-assisted in badminton teaching selected courses to improve learning outcomes were found in the pretest and post-test of Forehand deep high service skill (11.26+2.59 and 16.00+4.03 score), Forehand high clear skill (3.95 + 1.15 score and 5.90 + 1.65 score), drop shot skill (4.60 + 2.47 score and 8.35 + 3.02 score), forehand-wall skill test (7.07+ 2.23 and 9.88 + 2.62 score), and backhand-wall skill test (9.63+ 2.43 score and 13.28 + 2.95 score), respectively. (2) The mean comparison between the pretest and post-test of badminton skills found that long sever skill test, highball skill test, drop shot skill test, forehand wall test, and backhand wall test, all of the variables had significant differences (*p<.05). The average score post-test of all variables was greater than the pretest. (3) The mean comparison of the satisfaction survey on peer technique in badminton course teaching between the pretest and post-test found that all variables had a significant difference (*p<.05). The average post-test score of all variables was greater than the pretest. Conclusion: The study evaluated the impact of peer-assisted teaching in badminton courses on learning outcomes. Results showed significant improvements in various skills, including forehand deep high service, forehand high clear, drop shot, forehand-wall, and backhand-wall skills. Additionally, a mean comparison between pretest and post-test scores indicated significant differences in long serve, highball, drop shot, forehand wall, and backhand wall skills, with post-test scores higher in all variables. Furthermore, the satisfaction survey demonstrated a significant increase in satisfaction with peer teaching techniques in badminton courses.
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