用语音助手自由指令检测轻度认知障碍，准确率达82%。
Analyzing Multimodal Features of Spontaneous Voice Assistant Commands for Mild Cognitive Impairment Detection
- 设计自由生成指令任务，比读指令更反映认知能力。
- 多模态融合特征使分类准确率达82%，优于读指令任务。
- 适合关注老年认知健康与智能设备辅助筛查的研究者。
轻度认知障碍（MCI）是导致痴呆的重要风险因素，具有重大公共卫生意义。本研究在受控环境下，通过35名老年人使用语音助手自由生成指令，探索其在MCI检测中的潜力。设计了基于预设意图的指令生成任务，相比朗读指令，更反映认知能力。构建了基于音频、文本、意图及多模态融合特征的分类与回归模型。结果表明，指令生成任务平均分类准确率达82%，优于朗读任务；且生成指令与记忆、注意力子领域相关性更强。研究验证了指令生成任务的有效性，提示长期居家语音指令可用于MCI早期检测。
原文摘要 · Abstract (English)
Mild cognitive impairment (MCI) is a major public health concern due to its high risk of progressing to dementia. This study investigates the potential of detecting MCI with spontaneous voice assistant (VA) commands from 35 older adults in a controlled setting. Specifically, a command-generation task is designed with pre-defined intents for participants to freely generate commands that are more associated with cognitive ability than read commands. We develop MCI classification and regression models with audio, textual, intent, and multimodal fusion features. We find the command-generation task outperforms the command-reading task with an average classification accuracy of 82%, achieved by leveraging multimodal fusion features. In addition, generated commands correlate more strongly with memory and attention subdomains than read commands. Our results confirm the effectiveness of the command-generation task and imply the promise of using longitudinal in-home commands for MCI detection.
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