A NOVEL ALGORITHM TO DETECT NON-WEAR TIME FROM RAW ACCELEROMETER DATA USING DEEP CONVOLUTIONAL NEURAL NETWORKS

A novel algorithm to detect non-wear time from raw accelerometer data using deep convolutional neural networks

Abstract To date, non-wear detection algorithms commonly employ a 30, 60, or even 90 mins interval or window in which acceleration values need to be below a threshold value.A major drawback of such intervals is that they need to be long enough to prevent false positives (type I errors), while short enough to prevent false negatives (type II errors)

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Host Physiologic Changes Induced by Influenza A Virus Lead to Staphylococcus aureus Biofilm Dispersion and Transition from Asymptomatic Colonization to Invasive Disease

ABSTRACT Staphylococcus aureus is a ubiquitous opportunistic human pathogen and a major health concern worldwide, causing a wide variety of diseases from mild skin infections to systemic disease.S.aureus is a major source of severe secondary bacterial pneumonia after influenza A virus infection, which causes widespread morbidity and mortality.While

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