IntroductionHuman body acceleration is often used as an indicator of daily physical activity in epidemiological research. Raw acceleration signals contain three basic components: movement, gravity, and noise. Separation of these becomes increasingly difficult during rotational movements. We aimed to evaluate five different methods (metrics) of processing acceleration signals on their ability to remove the gravitational component of acceleration during standardised mechanical movements and the implications for human daily physical activity assessment.MethodsAn industrial robot rotated accelerometers in the vertical plane. Radius, frequency, and angular range of motion were systematically varied. Three metrics (Euclidian norm minus one [ENMO], Euclidian norm of the high-pass filtered signals [HFEN], and HFEN plus Euclidean norm of low-pass filtered signals minus 1 g [HFEN+]) were derived for each experimental condition and compared against the reference acceleration (forward kinematics) of the robot arm. We then compared metrics derived from human acceleration signals from the wrist and hip in 97 adults (22–65 yr), and wrist in 63 women (20–35 yr) in whom daily activity-related energy expenditure (PAEE) was available.ResultsIn the robot experiment, HFEN+ had lowest error during (vertical plane) rotations at an oscillating frequency higher than the filter cut-off frequency while for lower frequencies ENMO performed better. In the human experiments, metrics HFEN and ENMO on hip were most discrepant (within- and between-individual explained variance of 0.90 and 0.46, respectively). ENMO, HFEN and HFEN+ explained 34%, 30% and 36% of the variance in daily PAEE, respectively, compared to 26% for a metric which did not attempt to remove the gravitational component (metric EN).ConclusionIn conclusion, none of the metrics as evaluated systematically outperformed all other metrics across a wide range of standardised kinematic conditions. However, choice of metric explains different degrees of variance in daily human physical activity.

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Abstract-Publish/Subscribe systems have been extensively studied in the context of distributed information-based systems, and have proven scalable in information-dissemination for many distributed applications that have motivated the research. With the emergence of sensor-based applications and sensor networks, researchers have proposed novel publish/subscribe protocols that address the problem of distributed event dissemination for sensor network characteristics and constraints. In this paper, we focus on primitive events and the emerging class of publishers, and argue for "State-Filters" as more useful and suitable means of filtering events (than content-based filtering) in sensor-based publish/subscribe systems. Using State-Filters, we claim to achieve higher efficiency by means of filtering redundant and correlated event notifications, suppress event duplicates, and capture lasting conditions that had been previously not possible using contentbased filters. We evaluate our proposed filtering mechanism using real-world sensor data, and highlight some assumptions and pitfalls that motivate our future work in this area.