ArticleProceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)2023
EARLY DETECTION OF COGNITIVE DECLINE USING VOICE ASSISTANT COMMANDS.
Article in Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Article
- Multimodal Multitask Learning for Predicting Depression Severity and Suicide Risk Using Pretrained Audio and Text Embeddings: Methodology Development and Application.JMIR medical informatics · 2025Article
- Analyzing Multimodal Features of Spontaneous Voice Assistant Commands for Mild Cognitive Impairment Detection.Interspeech · 2024Article
- Article
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Authors and funding
7 authors.
Funding
Abstract
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%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.