Previous studies have demonstrated that pattern recognition approaches to accelerometer data reduction are feasible and moderately accurate in classifying activity type in children. Whether pattern recognition techniques can be used to provide valid estimates of physical activity energy expenditure in youth remains unexplored in the research literature. Purpose To develop and test artificial neural networks (ANNs) to predict physical activity (PA) type and energy expenditure (PAEE) from processed accelerometer data collected in children and adolescents. Methods 100 participants between the ages of 5 and 15 y completed 12 activity trials that were categorized into 5 PA types: sedentary, walking, running, light intensity household activities or games, and moderate-to-vigorous intensity games or sports. During each trial, participants wore an ActiGraph GT1M on the right hip and VO2 was measured using the Oxycon Mobile portable metabolic system. ANNs to predict PA type and PAEE (METs) were developed using the following features: 10th, 25th, 50th, 75th, and 90th percentiles and the lag one autocorrelation. To determine the highest time resolution achievable, features were extracted from 10, 15, 20, 30, and 60 s windows. Accuracy was assessed by calculating the percentage of windows correctly classified and root mean square error (RMSE). Results As window size increased from 10 to 60 s, accuracy for the PA type ANN increased from 81.3% to 88.4%. RMSE for the MET prediction ANN decreased from 1.1 METs to 0.9 METs. At any given window size, RMSE values for the MET prediction ANN were 30–40% lower than conventional regression-based approaches. Conclusion ANNs can be used to predict both PA type and PAEE in children and adolescents using count data from a single waist mounted accelerometer.

Volunteers are increasingly being recruited into citizen science projects to collect observations for scientific studies. An additional goal of these projects is to engage and educate these volunteers. Thus, there are few barriers to participation resulting in volunteer observers with varying ability to complete the project’s tasks. To improve the quality of a citizen science project’s outcomes it would be useful to account for inter-observer variation, and to assess the rarely tested presumption that participating in a citizen science projects results in volunteers becoming better observers. Here we present a method for indexing observer variability based on the data routinely submitted by observers participating in the citizen science project eBird, a broad-scale monitoring project in which observers collect and submit lists of the bird species observed while birding. Our method for indexing observer variability uses species accumulation curves, lines that describe how the total number of species reported increase with increasing time spent in collecting observations. We find that differences in species accumulation curves among observers equates to higher rates of species accumulation, particularly for harder-to-identify species, and reveals increased species accumulation rates with continued participation. We suggest that these properties of our analysis provide a measure of observer skill, and that the potential to derive post-hoc data-derived measurements of participant ability should be more widely explored by analysts of data from citizen science projects. We see the potential for inferential results from analyses of citizen science data to be improved by accounting for observer skill.

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For most citizen science projects in which volunteers act as intelligent sensors, data quality cannot be determined through comparison to an objective ground truth nor through consensus. In this paper we discuss the approach implemented by eBird, based on strategies used in autonomous sensor networks, to address the challenge of establishing the accuracy of humans at the tasks of detection and identification.