Suchergebnisse

Prologue: Making Movement History -- Practicing Movement History -- Discovering Movement History with the "Radical Americans" -- "Bringing the Boundaries of History Closer to People's Lives": The Massachusetts History Workshop -- Learning to Teach Movement History to Workers -- Telling Movement Stories in Public -- Commemorating Moments of Solidarity in Massachusetts Labor History -- Remembering Haymarket: Chicago's Labor Martyrs and Their Legacy -- Releasing Silenced Voices and Uncovering Forgotten Places in the American South -- Seeing the Past with "Movement Eyes": Making Documentary Films about People in Struggle -- Learning from Movement History -- Why Movement History Matters: The Politics of Class and Race in Boston -- Planting the Seeds of Resurgence: The United Mine Workers Strike Pittston Coal in 1989 -- On Becoming a Movement Again: The Labor Union Revival in the 1990s

Recent mobile and automated audiometry technologies have allowed for the democratization of hearing healthcare and enables non-experts to deliver hearing tests. The problem remains that a large number of such users are not trained to interpret audiograms. In this work, we outline the development of a data-driven audiogram classification system designed specifically for the purpose of concisely describing audiograms. More specifically, we present how a training dataset was assembled and the development of the classification system leveraging supervised learning techniques. We show that three practicing audiologists had high intra- and inter-rater agreement over audiogram classification tasks pertaining to audiogram configuration, symmetry and severity. The system proposed here achieves a performance comparable to the state of the art, but is significantly more flexible. Altogether, this work lays a solid foundation for future work aiming to apply machine learning techniques to audiology for audiogram interpretation.