验证socialbit作为智能手表算法,用于在临床人群中检测社交互动
Amar Dhand1,2,3, Samuel Tate4, Cade Mack4
1Department of Neurology, Brigham and Women's Hospital, Boston, MA, 02115, USA. adhand@bwh.harvard.edu.
Scientific reports
|February 4, 2026
概括
一个新的智能手表算法SocialBit使用环境音频准确地检测社交互动. 该工具提供了一种维护隐私的方法,用于测量神经病患者的社会参与,帮助监测康复.
科学领域:
- 数字健康数字健康
- 神经科学是一个神经科学.
- 机器学习 机器学习
背景情况:
- 社会互动对于大脑健康和神经损伤恢复至关重要.
- 目前用于测量社会互动的方法缺乏实时能力,特别是对于神经缺陷的人来说.
研究的目的:
- 开发和验证SocialBit,这是一种新的机器学习算法,用于使用智能手表环境音频实时检测社交互动.
- 评估SocialBit在住院中风患者的准确性和实用性,包括那些有认知和语言障碍的人.
主要方法:
- 开发了SocialBit,这是一个使用商业智能手表环境音频功能的隐私保护算法.
- 在8天内对153名住院中风患者进行了前性验证研究,将SocialBit与人类编码的地面真相进行了比较.
- 评估了不同类型的中风严重程度,认知水平和失语亚型的表现.
主要成果:
- 社交比特表现出高的整体性能 (AUC 0.94),并保持了语言缺陷患者的准确性 (AUC 0.93).
- 该算法准确地捕获了社交互动的频率分布,与人类编码的分钟到分钟密切匹配.
- 结果显示,中风严重程度与社会互动减少之间存在相关性,与人类编码数据一致.
结论:
- 社交比特是一种经过验证,准确的社交互动数字生物标志物,适合多样化的患者群体.
- 该算法为远程监控,临床试验和理解神经疾病对社会参与的影响提供了潜力.
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