Measuring regularity of human physical activities

In this paper, we aim to assess the regularity of physical activities through longitudinal sensor data, which reflects individuals’ all physical activities over an extended period. We present several entropy models, including entropy rate, approximate entropy, and sample entropy, which offer a more comprehensive evaluation of physical activity regularity compared to metrics based solely on periodicity or stability. We also propose a framework to validate the performance of entropy models on both artificial and real- world physical activity data. The results indicate entropy rate is able to identify not only the magnitude and amount of noise but also macroscopic variations of physical activities, such as differences on duration and occurrence time. Si-multaneously, entropy rate is highly correlated with the predictability of real-world samples, further highlighting its applicability in measuring human physical activity regularity. Leveraging entropy rate, we investigate the regularity for numerous individuals. We find the composition of physical activities can partially explain the difference in regularity among individuals, and the majority of individuals exhibit temporal stability of regularity.