AI-driven big data analysis on the impact of sports-induced cell molecular biomechanical stimuli on human physiological and mental health
Abstract
With the intensification of study and work pressure, people's physical and mental health has been compromised. Sports, in general, can enhance human physiological systems like the cardiovascular and respiratory systems from a macroscopic perspective. However, from a cell molecular biomechanics viewpoint, during exercise, mechanical forces are exerted on cells. This can lead to changes in cell shape and cytoskeletal organization. Appropriate mechanical stimulation can activate intracellular signaling pathways related to cell growth and repair. Yet, without proper guidance, issues arise. Inadequate exercise might not provide sufficient mechanical stress to trigger beneficial cell molecular responses. On the other hand, excessive exercise can cause excessive mechanical damage to cells, such as disrupting cell membranes and cytoskeletons, leading to sports injuries. To address this, this paper utilized Artificial Intelligence (AI) and Big Data (BD) to study sports. A sports auxiliary system integrating AI and BD was proposed. The research on students in Class A and Class B showed that many were in a sub-optimal state of health. After sports with the aid of the proposed system, significant improvements were observed. In Class A, the proportion of students with physiological sub-health declined by 53.33%, and in Class B by 40%. The number of students with psychological issues also decreased. This indicates that the sports auxiliary system has a clear application need. By analyzing data related to cell molecular responses during exercise using AI and BD, it can optimize exercise regimens. This helps maintain appropriate mechanical stimulation on cells, promoting beneficial cell molecular biomechanical adaptations and ultimately enhancing both physiological and mental health.
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