Smartwatch-derived Acoustic Markers for Deficits in Cognitively Relevant Everyday Functioning
Yasunori Yamada, Kaoru Shinkawa, Masatomo Kobayashi, Miyuki Nemoto,, Miho Ota, Kiyotaka Nemoto, Tetsuaki Arai

TL;DR
This study demonstrates that acoustic features extracted from smartwatch-recorded voice data can effectively detect deficits in everyday functioning related to cognitive impairment, outperforming standard neuropsychological tests.
Contribution
It introduces a smartwatch-based method for collecting acoustic markers that reliably identify deficits in everyday functioning in older adults.
Findings
Acoustic features achieved up to 77.8% detection accuracy.
Outperformed standard neuropsychological assessments in accuracy.
Identified common acoustic markers across different voice data types.
Abstract
Detection of subtle deficits in everyday functioning due to cognitive impairment is important for early detection of neurodegenerative diseases, particularly Alzheimer's disease. However, current standards for assessment of everyday functioning are based on qualitative, subjective ratings. Speech has been shown to provide good objective markers for cognitive impairments, but the association with cognition-relevant everyday functioning remains uninvestigated. In this study, we demonstrate the feasibility of using a smartwatch-based application to collect acoustic features as objective markers for detecting deficits in everyday functioning. We collected voice data during the performance of cognitive tasks and daily conversation, as possible application scenarios, from 54 older adults, along with a measure of everyday functioning. Machine learning models using acoustic features could…
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