Personalising pollution exposure estimates using wearable activity sensors

Ke Hu, Yan Wang, Ashfaqur Rahman, V. Sivaraman
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引用次数: 21

Abstract

In recent years several research groups, including ours, have demonstrated participatory systems that use wearable or vehicle-mounted portable units coupled with smartphones to crowdsource urban air pollution data from lay users. These systems have shown remarkable improvement in spatial granularity over government-operated monitoring systems, leading to better mapping and understanding of urban air pollution, at relatively low cost. In this paper we extend the paradigm to personalize the consumption of data by individuals. Specifically, we combine the pollution concentrations obtained from participatory systems with the individual's on-body activity monitors to estimate the personal inhalation dosage of air pollution. We show that the individual's activity, such as jogging, cycling, or driving, impacts their dosage, and develop an app that gives them this personalised information. Our system is a step towards enabling medical inferencing of the impact of air pollution on individual health.
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使用可穿戴活动传感器进行个性化污染暴露评估
近年来,包括我们在内的几个研究小组已经展示了参与式系统,该系统使用可穿戴或车载便携式设备与智能手机相结合,从非专业用户那里众包城市空气污染数据。与政府运营的监测系统相比,这些系统在空间粒度上有了显著改善,从而以相对较低的成本更好地绘制和了解城市空气污染。在本文中,我们将范式扩展到个性化的个人数据消费。具体而言,我们将从参与式系统获得的污染浓度与个人身体活动监测仪相结合,以估计个人吸入空气污染的剂量。我们展示了个人的活动,如慢跑、骑自行车或开车,会影响他们的剂量,并开发了一个应用程序,为他们提供这些个性化信息。我们的系统朝着从医学角度推断空气污染对个人健康的影响迈出了一步。
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