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
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 detect
individuals with deficits in everyday functioning with up to 77.8% accuracy,
which was higher than the 68.5% accuracy with standard neuropsychological
tests. We also identified common acoustic features for robustly discriminating
deficits in everyday functioning across both types of voice data (cognitive
tasks and daily conversation). Our results suggest that common acoustic
features extracted from different types of voice data can be used as markers
for deficits in everyday functioning.