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Sub-health problems are becoming increasingly serious in today’s society, and some organizations are not paying enough attention to adolescent sports health data. For adolescent sports, health needs to be measured regularly and tested constantly so that the intake of diet and medication can be reasonably adjusted according to their biochemical indicators. The Smart Health Life Growth Cloud System can effectively manage residents’ health data digitally and informally, enabling users to manage their health data better and facilitating doctors to keep abreast of users’ health conditions, while also facilitating the government to conduct research and studies on the physical fitness of adolescents in the areas under its jurisdiction. The cloud-based management platform for student physical health management relies on the mobile internet as a practical service platform whose primary role is to provide young people with a convenient sporting life, focusing on practicality, service, and interactivity. We also collect sensor data to detect gait patterns (with or without leg contact) and filter them through an adaptive hybrid filter to differentiate between the two patterns. In turn, the Smart Health Life Growth Cloud system changes the traditional medical model and greatly improves the information and intelligence of the healthcare industry. Using the exercise individual health evaluation model in this article is controlled to be within 20%, thus concluding that the exercise individual health evaluation model proposed in this article can predict the exercise limit of an exercise individual more accurately.
Sub-health problems are becoming increasingly serious in today’s society, and some organizations are not paying enough attention to adolescent sports health data. For adolescent sports, health needs to be measured regularly and tested constantly so that the intake of diet and medication can be reasonably adjusted according to their biochemical indicators. The Smart Health Life Growth Cloud System can effectively manage residents’ health data digitally and informally, enabling users to manage their health data better and facilitating doctors to keep abreast of users’ health conditions, while also facilitating the government to conduct research and studies on the physical fitness of adolescents in the areas under its jurisdiction. The cloud-based management platform for student physical health management relies on the mobile internet as a practical service platform whose primary role is to provide young people with a convenient sporting life, focusing on practicality, service, and interactivity. We also collect sensor data to detect gait patterns (with or without leg contact) and filter them through an adaptive hybrid filter to differentiate between the two patterns. In turn, the Smart Health Life Growth Cloud system changes the traditional medical model and greatly improves the information and intelligence of the healthcare industry. Using the exercise individual health evaluation model in this article is controlled to be within 20%, thus concluding that the exercise individual health evaluation model proposed in this article can predict the exercise limit of an exercise individual more accurately.
The study’s objective was to identify the features of internal movement structure that depend on speed and the importance of unloading when jogging on an anti-gravity treadmill. The aim was to investigate whether the internal structure of running changes under unloaded conditions, using an anti-gravity treadmill. Twenty male competitive middle- and long-distance runners with the following characteristics participated in the study: age, 25 ± 5 years; body weight, 67.2 ± 8.9 kg; body height, 177 ± 11 cm; and training experience, 9 ± 5 years. The gastrocnemius (GC), tibialis anterior (T), quadriceps femoris (Q), biceps femoris (B), and gluteus (G) were the five lower limb muscles whose muscle activity was evaluated. Surface electromyography (sEMG) was used to measure muscle activation while jogging and running on the AlterG Anti-Gravity Treadmill. The study method involved capturing the examined muscular activity at four different speeds: 6, 10, 14, and 18 km/h. At each of these speeds, four two-minute measurements were taken with varying body weight relief: 100%, 75%, 50%, and 25% of body weight. Repeated measures multivariate analysis of variance (RM-MANOVA) [F = 3.4663 p = 0.0001] showed that as running speed increases, the muscular activity of each muscle, expressed as a percentage of maximum muscle tension (%MVIC), decreases significantly. Results indicate that running pace affects the dynamics of the reduction in muscle activity in every examined muscle. As one runs faster, the decline in dynamics becomes more intense. At the slowest jogging pace (6 km/h), the variations were almost negligible (±4 percentage points between 25% and 100% body weight relief) as unloading increased. However, the discrepancies reached up to 14 percentage points at the fastest running speed (18 km/h). In every muscle studied, distinctive patterns and significant dynamics at high speeds were observed. The study’s findings suggest that using an anti-gravity treadmill for training can be beneficial, yet it is important to consider the significant relationships between speed and relief, as these variables could impact maintaining a proper movement pattern and running style. This knowledge may be useful when choosing the right training regimens and loads for runners recovering from injuries.
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