Speaker
Description
Healthcare acoustic environments (HAEs) are commonly characterised using sound level descriptors (e.g. LAeq, L10, Lmax, etc). These quantify acoustic magnitude and temporal variation but provide limited information about modelled loudness or acoustic context. Complementary approaches are often analysed separately, making their temporal relationships difficult to examine. This conceptual paper proposes a human-centred multilayer framework for continuous HAE characterisation. It organises signal-derived information into three layers: (1) a Physical Layer describing measured acoustic characteristics; (2) a Perceptual Layer representing the temporal evolution of loudness using the Moore-Glasberg-Schlittenlacher method; and (3) a Context Layer assigning each interval to the broad acoustic category it most closely resembles. Timestamps align the outputs while preserving their definitions, temporal resolutions and uncertainties. Local processing retains descriptors and assignments without storing raw audio or extracting linguistic content. Intended users, clinical activities and care objectives guide monitoring design and interpretation, while associations with human responses or clinical outcomes require independent evidence. The framework does not combine the layers into a single score or treat them as direct measures of experience or clinical effect. It provides a traceable basis for examining complementary acoustic evidence over time. Technical and ecological validation are required to establish its accuracy, generalisability and practical value