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APPENDIX: supplementary information and additional data-plots 1. Table of health and fitness statistics for the 24 participants. Images and placement of the movement sensor.Placement on the lateral malleolar prominence of the fibula of the right leg Device sensor board with housing and battery 4 cms Correlation of PCA component and activity level.Mean acceleration, averaged over the 40 minute play session, for all 118 data sets and the corresponding values for Principal Component Axis 1 (see figure 3c in paper).Correlation plot of mean acceleration versus PCA component 1. Additional raw-data traces from the 5-day longitudinal study.Acceleration data traces, acquired on Friday of the study week for the cluster of year 6 children identified in the PCA plot. Black-filled circles and number identifiers indicate the 6 children. The data points for child 6, for all 5 days are identified by green-filled circles.PCA plot in figure 6 of paper -re-presented. child 6 child 2 child 4 child 7 child 8 child 9 child 11Friday acceleration traces corresponding to the children identified in the PCA plot

Backed by more powerful computational resources and optimized training routines, deep learning models have proven unprecedented performance and several benefits to extract information from chest X-ray data. This is one of the most common imaging exams, whose increasing demand is reflected in the aggravated radiologists’ workload. Consequently, healthcare would benefit from computer-aided diagnosis systems to prioritize certain exams and further identify possible pathologies. Pioneering work in chest X-ray analysis has focused on the identification of specific diseases, but to the best of the authors' knowledge no paper has specifically reviewed relevant work on abnormality detection and multi-label thoracic pathology classification. This paper focuses on those issues, selecting the leading chest X-ray based deep learning strategies for comparison. In addition, the paper discloses the current annotated public chest X-ray databases, covering the common thorax diseases.