Redmond S J, Lovell N H, Yang G Z, Horsch A, Lukowicz P, Murrugarra L, Marschollek M
Stephen Redmond,, Graduate School of Biomedical Engineering,, Level 5, Samuels Building,, Gate 11, Botany Street, UNSW Australia,, Kensington, NSW 2052,, Sydney, Australia, E-mail:
Yearb Med Inform. 2014 Aug 15;9(1):135-42. doi: 10.15265/IY-2014-0019.
The aim of this paper is to discuss how recent developments in the field of big data may potentially impact the future use of wearable sensor systems in healthcare.
The article draws on the scientific literature to support the opinions presented by the IMIA Wearable Sensors in Healthcare Working Group.
The following is discussed: the potential for wearable sensors to generate big data; how complementary technologies, such as a smartphone, will augment the concept of a wearable sensor and alter the nature of the monitoring data created; how standards would enable sharing of data and advance scientific progress. Importantly, attention is drawn to statistical inference problems for which big datasets provide little assistance, or may hinder the identification of a useful solution. Finally, a discussion is presented on risks to privacy and possible negative consequences arising from intensive wearable sensor monitoring.
Wearable sensors systems have the potential to generate datasets which are currently beyond our capabilities to easily organize and interpret. In order to successfully utilize wearable sensor data to infer wellbeing, and enable proactive health management, standards and ontologies must be developed which allow for data to be shared between research groups and between commercial systems, promoting the integration of these data into health information systems. However, policy and regulation will be required to ensure that the detailed nature of wearable sensor data is not misused to invade privacies or prejudice against individuals.
本文旨在探讨大数据领域的最新发展可能如何潜在地影响可穿戴传感器系统在医疗保健领域的未来应用。
本文借鉴科学文献来支持国际医学信息学协会医疗保健可穿戴传感器工作组提出的观点。
讨论了以下内容：可穿戴传感器生成大数据的潜力；诸如智能手机等互补技术将如何增强可穿戴传感器的概念并改变所创建监测数据的性质；标准将如何实现数据共享并推动科学进步。重要的是，要注意到大数据集对其几乎没有帮助或可能阻碍找到有用解决方案的统计推断问题。最后，讨论了隐私风险以及密集的可穿戴传感器监测可能产生的负面后果。
可穿戴传感器系统有可能生成目前我们难以轻松组织和解释的数据集。为了成功利用可穿戴传感器数据推断健康状况并实现主动健康管理，必须制定标准和本体，以便研究小组之间以及商业系统之间能够共享数据，促进这些数据融入健康信息系统。然而，需要政策和法规来确保可穿戴传感器数据的详细性质不会被滥用，从而侵犯隐私或对个人造成偏见。
