Percentage of patients stating at least one causal belief in that category (n = 678).

<p>Percentage of patients stating at least one causal belief in that category (n = 678).</p

Causal beliefs: logistic regressions, Odds ratio (Exp(b)).

<p>Causal beliefs: logistic regressions, Odds ratio (Exp(b)).</p

Category system of causal beliefs of mental disorders.

<p>Frequencies of coded categories were reported for each category on the level of responses (N = 1858).</p

Acute pulmonary inflammatory response (PMNs) to TiO ; Figure 4 and Figure S-2 (Supplemental Material available online at ) and carbonaceous particles (; ) in rats and mice, with particle number () and () as the dose metric

<p><b>Copyright information:</b></p><p>Taken from "Inflammatory Response to TiO and Carbonaceous Particles Scales Best with BET Surface Area"</p><p></p><p>Environmental Health Perspectives 2007;115(6):A290-A291.</p><p>Published online Jan 2007</p><p>PMCID:PMC1892122.</p><p>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose</p

Agreement of cutpoints on activity intensity, minute by minute.

<p>Percent of time (total 14.7 hours / day, 8780 days).</p

Feature importances estimated by Random Forest with 1000 trees with the Best Ankle + Best Hip feature set and the segmentation strategy of 180s windows with 120s overlap.

<p>Feature importances estimated by Random Forest with 1000 trees with the Best Ankle + Best Hip feature set and the segmentation strategy of 180s windows with 120s overlap.</p

Exemplary raw accelerometer readings for one hour during which two participants, (a) male and (b) female, had a jogging activity.

<p>The inclinometer and number of steps time series are not shown for clarity because they are in a different unit with much smaller values. Jogging ‘diary’ relates to the reported jogging period by the user. Jogging ‘golden’ is the jogging period per the ‘golden standard’ labels.</p

Comparison of the baseline and proposed approach for feature extraction per sensor location by the best obtained value per metric.

<p>Comparison of the baseline and proposed approach for feature extraction per sensor location by the best obtained value per metric.</p

The jogging period matching ratio per feature set type and applied post-classification rule for the highest-accuracy classification model obtained with the proposed and baseline feature sets.

<p>The jogging period matching ratio per feature set type and applied post-classification rule for the highest-accuracy classification model obtained with the proposed and baseline feature sets.</p

Performance of different classifiers on the 4 final feature sets, depending on feature type with the segmentation strategy of 60s windows without overlap.

<p>Performance of different classifiers on the 4 final feature sets, depending on feature type with the segmentation strategy of 60s windows without overlap.</p