@文章{信息:doi/10.2196/17803，作者=“Lee, JeeEun and Yoo, Sun K”，标题=“基于加速度计数据独立分量分析的呼吸速率估计:单臂干预试验研究”，期刊=“JMIR Mhealth Uhealth”，年=“2020”，月=“8”，日=“10”，卷=“8”，号=“8”，页=“e17803”，关键词=“呼吸速率”;加速度计;智能手机;独立成分分析;同态频率;，摘要=“背景:随着移动环境的发展，人们开始对连续呼吸监测进行研究。然而，对于一般用户来说，使用通常用于测量呼吸的传感器并不容易。当在移动环境中使用非接触方法测量呼吸时，还存在由各种环境变量引起的随机噪声。目的:在本研究中，我们旨在使用智能手机中的加速度计传感器来估计呼吸速率。方法:首先，通过智能手机从加速度计传感器获取数据，方便公众访问。 Second, an independent component was extracted to calibrate the three-axis accelerometer. Lastly, the respiration rate was estimated using quefrency selection reflecting the harmonic component because respiration has regular patterns. Results: From April 2018, we enrolled 30 male participants. When the independent component and quefrency selection were used to estimate the respiration rate, the correlation with respiration acquired from a chest belt was 0.7. The statistical results of the Wilcoxon signed-rank test were used to determine whether the differences in the respiration counts acquired from the chest belt and from the accelerometer sensor were significant. The P value of the difference in the respiration counts acquired from the two sensors was .27, which was not significant. This indicates that the number of respiration counts measured using the accelerometer sensor was not different from that measured using the chest belt. The Bland-Altman results indicated that the mean difference was 0.43, with less than one breath per minute, and that the respiration rate was at the 95{\%} limits of agreement. Conclusions: There was no relevant difference in the respiration rate measured using a chest belt and that measured using an accelerometer sensor. The accelerometer sensor approach could solve the problems related to the inconvenience of chest belt attachment and the settings. It could be used to detect sleep apnea through constant respiration rate estimation in an internet-of-things environment. ", issn="2291-5222", doi="10.2196/17803", url="https://mhealth.www.mybigtv.com/2020/8/e17803", url="https://doi.org/10.2196/17803", url="http://www.ncbi.nlm.nih.gov/pubmed/32773384" }