早期检测认知能力下降使用语音助理命令
Eli Kurtz1, Youxiang Zhu1, Tiffany Driesse2
1Department of Computer Science, University of Massachusetts Boston, MA, USA.
概括
语音助理系统可以使用语音检测阿尔茨海默病和相关痴呆症 (ADRD). 这项技术在分类认知衰退阶段方面达到74.7%的准确性,有助于早期诊断.
科学领域:
- 计算神经科学是一种计算神经科学.
- 老年学是指老年学的学科.
- 语音处理 语音处理
背景情况:
- 早期发现阿尔茨海默病和相关痴呆症 (ADRD) 对于有效的治疗和疾病管理至关重要.
- 在自发言语上训练的机器学习模型在检测和分类ADRD方面表现出很有前途.
- 语音助理系统 (VAS) 提供了一个新的平台,用于持续监测和收集风险人群的数据.
研究的目的:
- 调查使用语音助理系统 (VAS) 数据用于认知状态分类的可行性.
- 从VAS交互中开发独特的功能集,用于机器学习模型培训.
- 用VAS数据评估机器学习模型在分类认知衰退不同阶段的准确性.
主要方法:
- 收集了与VAS相互作用的不同认知状态 (痴呆症,轻度认知障碍,健康控制) 的老年人的语音数据.
- 从收集的VAS数据开发了新的功能集.
- 采用多类分类,二进制分类和回归模型,使用开发的功能.
主要成果:
- VAS数据成功地将参与者分为痴呆症 (DM),轻度认知障碍 (MCI) 和健康控制 (HC) 类别,精度高达74.7%.
- 这些模型在区分健康控制 (HC) 和轻度认知障碍 (MCI) 参与者方面取得了高达62.8%的准确性.
- 来自VAS相互作用的特征集被证明对认知状态分类有效.
结论:
- 语音助理系统 (VAS) 提供了一种可行且准确的方法来收集用于认知衰退评估的语音数据.
- 使用VAS数据的机器学习模型可以显著帮助早期检测和分类ADRD.
- 这种方法具有远程,非侵入性认知监测风险人群的潜力.
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