Sedentary behavior health outcomes and identifying the uncertain behavior patterns in adult

Uncertain sedentary behavior has evolved as a new health concern in recent periods. Being inactive for long periods is a significant risk factor among all the adult age groups, especially over-reliance on vehicles for mobility. Sensors are making it easier to monitor seating habits throughout the active period. However, experts are divided on the most appropriate objective metrics for capturing the cumulative information of sedentary time throughout the day. Due to discrepancies in measuring methods, data processing techniques, and the absence of fundamental outcome indicators like cumulative sedentary period, evaluating the several research studies sedentary patterns was unrealistic. In this research study, a novel design was suggested with adaptive computations, namely, fleeting granularity, to differentiate instances of daily human activities. Multivariate transitory information is acquired from sophisticated units (essential cells). Our proposed scalable algorithms can identify Frequent Behavior Patterns (FBPs) with a timeframe estimate by employing collected widespread multivariate data (fleeting granularity). It has been evidenced that the applicability of the example by differentiating proof computations on two certifiable datasets. The assessment of the relationships, accuracy, and applicability of sedentary factors is the primary subject of this research.