TY - GEN
T1 - Early Detection of Cognitive Decline Using Voice Assistant Commands
AU - Kurtz, Eli
AU - Zhu, Youxiang
AU - Driesse, Tiffany
AU - Tran, Bang
AU - Batsis, John A.
AU - Roth, Robert M.
AU - Liang, Xiaohui
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Early detection of Alzheimer's Disease and Related Dementias (ADRD) is critical in treating the progression of the disease. Previous studies have shown that ADRD can be detected and classified using machine learning models trained on samples of spontaneous speech. We propose using Voice-Assistant Systems (VAS), e.g., Amazon Alexa, to monitor and collect data from at-risk adults, and we show that this data can be used to achieve functional accuracy in classifying their cognitive status. In this paper, we develop multiple unique feature sets from VAS data that can be used in the training of machine learning models. We then perform multi-class classification, binary classification, and regression using these features on our dataset of older adults with three varying stages of cognitive decline interacting with VAS. Our results show that the VAS data can be used to classify Dementia (DM), Mild Cognitive Impairment (MCI), and Healthy Control (HC) participants with an accuracy up to 74.7%, and classify between HC and MCI with accuracy up to 62.8%.
AB - Early detection of Alzheimer's Disease and Related Dementias (ADRD) is critical in treating the progression of the disease. Previous studies have shown that ADRD can be detected and classified using machine learning models trained on samples of spontaneous speech. We propose using Voice-Assistant Systems (VAS), e.g., Amazon Alexa, to monitor and collect data from at-risk adults, and we show that this data can be used to achieve functional accuracy in classifying their cognitive status. In this paper, we develop multiple unique feature sets from VAS data that can be used in the training of machine learning models. We then perform multi-class classification, binary classification, and regression using these features on our dataset of older adults with three varying stages of cognitive decline interacting with VAS. Our results show that the VAS data can be used to classify Dementia (DM), Mild Cognitive Impairment (MCI), and Healthy Control (HC) participants with an accuracy up to 74.7%, and classify between HC and MCI with accuracy up to 62.8%.
KW - cognitive decline
KW - early detection
KW - linguistic and acoustic features
KW - machine learning
KW - Voice assistant
UR - https://www.scopus.com/pages/publications/85177597916
UR - https://www.scopus.com/pages/publications/85177597916#tab=citedBy
U2 - 10.1109/ICASSP49357.2023.10095825
DO - 10.1109/ICASSP49357.2023.10095825
M3 - Conference contribution
AN - SCOPUS:85177597916
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
BT - ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Y2 - 4 June 2023 through 10 June 2023
ER -