Dynamic evaluation of community health services and health quality based on biomechanical time series
Abstract
Background: Evaluating community health services and fitness exceptional is important for identifying areas of development and making sure powerful healthcare shipping. Biomechanical assessment, especially reading joint movements, provides valuable insights into people’s fitness popularity and useful capabilities. Although biomechanical time-collecting information has terrific promise, there is not much thorough research that comprises those parameters in population health checks. Aim: This study evaluates the dynamic relationship between biomechanical time series data of joint movements and community health quality metrics. It also finds important factors for good health and gives practical advice for improving community health services. Methods: The study utilizes a biomechanical time series dataset from Kaggle. The collected time series data was preprocessed using Z-score standardization to ensure comparability. Gated Refined Long Short-Term Memory (GRLSTM) networks were employed for tasks due to their ability to capture long-term dependencies and temporal relationships inherent in time series data. Results: Statistical analyses such as regression and ANOVA were conducted to explore relationships between joint movement patterns and health quality predictors. The GRLSTM indicates significant associations between specific joint movement patterns and health quality indicators. Regression analyses confirmed key predictors of health quality, while ANOVA demonstrated significant differences in joint movement patterns among different health quality groups. The GRLSTM model demonstrates exceptional performance, with 94% precision and 95% recall rates, an accuracy of 98% and a robust F1-score of 96%, indicating a strong equilibrium between recall and accuracy. The ANOVA shows joint angles as the strongest predictor (p < 0.001). The regression analysis identifies stride length (β = 2.30, p < 0.001) as the strongest positive predictor. Conclusion: This observation emphasizes the importance of incorporating biomechanical assessments into community health reviews, highlighting the capability of GRLSTM networks and predictive analytics in improving fitness satisfaction and healthcare strategies.
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