慢性阻塞性肺病
计算机科学
背景(考古学)
呼吸频率
恶化
移动设备
肺病
实时计算
人工智能
医学
心率
地理
内科学
万维网
血压
考古
作者
Md. Mahbubur Rahman,Ebrahim Nemati,Viswam Nathan,Jilong Kuang
出处
期刊:EAI/Springer Innovations in Communication and Computing
日期:2018-10-02
被引量:6
标识
DOI:10.1007/978-3-030-29897-5_22
摘要
Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death both in the USA and worldwide. Respiratory rate is an important predictor for acute COPD exacerbation and an indicator of overall well-being for healthy individuals. Current methods to measure respiratory rate either involve uncomfortable, specialized sensors such as a chestband or are less resilient to varying real-life situations. In this paper, we present a novel context-aware framework that can reliably estimate respiratory rate using data from sensors embedded in users’ existing mobile devices such as smartphones and smartwatches. Our approach takes current contexts, such as device placement, user’s social interaction, and user’s pulmonary health condition into consideration, and finds the optimal fusion across sensor streams and algorithms. We show that our approach can handle varying user contexts (e.g., detecting device placement with an accuracy of 97%) and reliably estimate respiratory rate with errors as low as 0.85 breaths per minute.
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