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Using Speech Data From Interactions With a Voice Assistant to Predict the Risk of Future Accidents for Older Drivers: Prospective Cohort Study

BACKGROUND: With the rapid growth of the older adult population worldwide, car accidents involving this population group have become an increasingly serious problem. Cognitive impairment, which is assessed using neuropsychological tests, has been reported as a risk factor for being involved in car a...

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Autores principales: Yamada, Yasunori, Shinkawa, Kaoru, Kobayashi, Masatomo, Takagi, Hironobu, Nemoto, Miyuki, Nemoto, Kiyotaka, Arai, Tetsuaki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8063093/
https://www.ncbi.nlm.nih.gov/pubmed/33830066
http://dx.doi.org/10.2196/27667
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author Yamada, Yasunori
Shinkawa, Kaoru
Kobayashi, Masatomo
Takagi, Hironobu
Nemoto, Miyuki
Nemoto, Kiyotaka
Arai, Tetsuaki
author_facet Yamada, Yasunori
Shinkawa, Kaoru
Kobayashi, Masatomo
Takagi, Hironobu
Nemoto, Miyuki
Nemoto, Kiyotaka
Arai, Tetsuaki
author_sort Yamada, Yasunori
collection PubMed
description BACKGROUND: With the rapid growth of the older adult population worldwide, car accidents involving this population group have become an increasingly serious problem. Cognitive impairment, which is assessed using neuropsychological tests, has been reported as a risk factor for being involved in car accidents; however, it remains unclear whether this risk can be predicted using daily behavior data. OBJECTIVE: The objective of this study was to investigate whether speech data that can be collected in everyday life can be used to predict the risk of an older driver being involved in a car accident. METHODS: At baseline, we collected (1) speech data during interactions with a voice assistant and (2) cognitive assessment data—neuropsychological tests (Mini-Mental State Examination, revised Wechsler immediate and delayed logical memory, Frontal Assessment Battery, trail making test-parts A and B, and Clock Drawing Test), Geriatric Depression Scale, magnetic resonance imaging, and demographics (age, sex, education)—from older adults. Approximately one-and-a-half years later, we followed up to collect information about their driving experiences (with respect to car accidents) using a questionnaire. We investigated the association between speech data and future accident risk using statistical analysis and machine learning models. RESULTS: We found that older drivers (n=60) with accident or near-accident experiences had statistically discernible differences in speech features that suggest cognitive impairment such as reduced speech rate (P=.048) and increased response time (P=.040). Moreover, the model that used speech features could predict future accident or near-accident experiences with 81.7% accuracy, which was 6.7% higher than that using cognitive assessment data, and could achieve up to 88.3% accuracy when the model used both types of data. CONCLUSIONS: Our study provides the first empirical results that suggest analysis of speech data recorded during interactions with voice assistants could help predict future accident risk for older drivers by capturing subtle impairments in cognitive function.
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spelling pubmed-80630932021-04-30 Using Speech Data From Interactions With a Voice Assistant to Predict the Risk of Future Accidents for Older Drivers: Prospective Cohort Study Yamada, Yasunori Shinkawa, Kaoru Kobayashi, Masatomo Takagi, Hironobu Nemoto, Miyuki Nemoto, Kiyotaka Arai, Tetsuaki J Med Internet Res Original Paper BACKGROUND: With the rapid growth of the older adult population worldwide, car accidents involving this population group have become an increasingly serious problem. Cognitive impairment, which is assessed using neuropsychological tests, has been reported as a risk factor for being involved in car accidents; however, it remains unclear whether this risk can be predicted using daily behavior data. OBJECTIVE: The objective of this study was to investigate whether speech data that can be collected in everyday life can be used to predict the risk of an older driver being involved in a car accident. METHODS: At baseline, we collected (1) speech data during interactions with a voice assistant and (2) cognitive assessment data—neuropsychological tests (Mini-Mental State Examination, revised Wechsler immediate and delayed logical memory, Frontal Assessment Battery, trail making test-parts A and B, and Clock Drawing Test), Geriatric Depression Scale, magnetic resonance imaging, and demographics (age, sex, education)—from older adults. Approximately one-and-a-half years later, we followed up to collect information about their driving experiences (with respect to car accidents) using a questionnaire. We investigated the association between speech data and future accident risk using statistical analysis and machine learning models. RESULTS: We found that older drivers (n=60) with accident or near-accident experiences had statistically discernible differences in speech features that suggest cognitive impairment such as reduced speech rate (P=.048) and increased response time (P=.040). Moreover, the model that used speech features could predict future accident or near-accident experiences with 81.7% accuracy, which was 6.7% higher than that using cognitive assessment data, and could achieve up to 88.3% accuracy when the model used both types of data. CONCLUSIONS: Our study provides the first empirical results that suggest analysis of speech data recorded during interactions with voice assistants could help predict future accident risk for older drivers by capturing subtle impairments in cognitive function. JMIR Publications 2021-04-08 /pmc/articles/PMC8063093/ /pubmed/33830066 http://dx.doi.org/10.2196/27667 Text en ©Yasunori Yamada, Kaoru Shinkawa, Masatomo Kobayashi, Hironobu Takagi, Miyuki Nemoto, Kiyotaka Nemoto, Tetsuaki Arai. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 08.04.2021. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Yamada, Yasunori
Shinkawa, Kaoru
Kobayashi, Masatomo
Takagi, Hironobu
Nemoto, Miyuki
Nemoto, Kiyotaka
Arai, Tetsuaki
Using Speech Data From Interactions With a Voice Assistant to Predict the Risk of Future Accidents for Older Drivers: Prospective Cohort Study
title Using Speech Data From Interactions With a Voice Assistant to Predict the Risk of Future Accidents for Older Drivers: Prospective Cohort Study
title_full Using Speech Data From Interactions With a Voice Assistant to Predict the Risk of Future Accidents for Older Drivers: Prospective Cohort Study
title_fullStr Using Speech Data From Interactions With a Voice Assistant to Predict the Risk of Future Accidents for Older Drivers: Prospective Cohort Study
title_full_unstemmed Using Speech Data From Interactions With a Voice Assistant to Predict the Risk of Future Accidents for Older Drivers: Prospective Cohort Study
title_short Using Speech Data From Interactions With a Voice Assistant to Predict the Risk of Future Accidents for Older Drivers: Prospective Cohort Study
title_sort using speech data from interactions with a voice assistant to predict the risk of future accidents for older drivers: prospective cohort study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8063093/
https://www.ncbi.nlm.nih.gov/pubmed/33830066
http://dx.doi.org/10.2196/27667
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