移动健康传感主动和被动数据收集的当前挑战和机遇:范围审查
Christopher Slade1,2, Yinan Sun1, Wei Cheng Chao2
1Department of Information and Computer Science, University of Hawaii, Honolulu, HI 96822, United States.
JAMIA open
|July 21, 2025
概括
移动传感收集健康数据,但面临用户合规性和数据一致性挑战. 机器学习和用户界面创新为有效的移动健康数据收集提供了解决方案.
科学领域:
- 移动健康 (mHealth) 服务提供商
- 无处不在的计算是无处不在的
- 机器学习 (ML) 是指机器学习.
背景情况:
- 移动和无处不在的设备促进现实世界的健康数据收集,用于远程监测和数字干预.
- 在用户合规性,被动数据一致性和移动健康数据收集授权方面存在重大挑战.
研究的目的:
- 识别和分析用于健康应用的移动传感数据收集挑战.
- 探索改善移动健康研究数据收集的机会.
主要方法:
- 使用机器学习与主动和被动移动传感的研究范围审查.
- 在IEEE,ACM和Web of Science数据库中进行了搜索.
- 包括收集主动和被动数据的研究;不包括现有数据集.
主要成果:
- 77项研究使用智能手机,智能手表,可穿戴设备和环境传感器.
- 积极数据收集面临参与者合规问题.
- 被动数据收集遇到了数据一致性和授权方面的挑战.
结论:
- 移动传感具有智能移动健康应用的潜力.
- 在合规性,一致性和授权方面的挑战仍然存在.
- 机器学习和用户界面设计创新可以解决这些障碍.
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