Smartwatch based Respiratory Rate and Breathing Pattern Recognition in an End-consumer Environment

John Trimpop, Hannes Schenk, G. Bieber, Friedrich Lämmel, Paul Burggraf
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引用次数: 11

Abstract

Smartwatches as wearables became part of social life and practically and technically offer the possibility to collect medical body parameters next to usual fitness data. In this paper, we present an evaluation of the respiratory rate detection of the &gesund system. &gesund is a health assistance system, which automatically records detailed long-term health data with end-consumer smartwatches. The &gesund core is based on technology exclusively licensed from the Fraunhofer Institute of applied research. In our study, we compare the &gesund algorithms for respiration parameter detection in low-amplitude activities against data recorded from actual sleep laboratory patients. The results show accuracies of up to 89%. We are confident that wearable technologies will be used for medical health assistance in the near future.
终端消费者环境中基于智能手表的呼吸频率和呼吸模式识别
智能手表作为可穿戴设备成为社会生活的一部分,在实际和技术上提供了收集常规健身数据之外的医疗身体参数的可能性。本文对超声系统的呼吸频率检测进行了评价。&gesund是一个健康辅助系统,它可以通过终端消费者的智能手表自动记录详细的长期健康数据。&gesund核心是基于夫琅和费应用研究所独家授权的技术。在我们的研究中,我们比较了&gesund算法在低幅度活动中的呼吸参数检测与实际睡眠实验室患者记录的数据。结果表明,准确率高达89%。我们相信,可穿戴技术将在不久的将来用于医疗卫生援助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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