Telfer Brian A, Williamson James R, Weed Lara, Bursey Max, Frazee Royce, Galer Meghan, Moore Charles, Buller Mark, Friedl Karl E
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:4636-4639. doi: 10.1109/EMBC44109.2020.9175669.
Breathing rate was estimated from chest-worn accelerometry collected from 1,522 servicemembers during training by a wearable physiological monitor. A total of 29,189 hours of training and sleep data were analyzed. The primary purpose of the monitor was to assess thermal-work strain and avoid heat injuries. The monitor design was thus not optimized to estimate breathing rate. Since breathing rate cannot be accurately estimated during periods of high activity, a qualifier was applied to identify sedentary time periods, totaling 8,867 hours. Breathing rate was estimated for a total of 4,179 hours, or 14% of the total collection and 47% of the sedentary total, primarily during periods of sleep. The breathing rate estimation method was compared to an FDA 510(K)-cleared criterion breathing rate sensor (Zephyr, Annapolis MD, USA) in a controlled laboratory experiment, which showed good agreement between the two techniques. Contributions of this paper are to: 1) provide the first analysis of accelerometry-derived breathing rate on free-living data including periods of high activity as well as sleep, along with a qualifier that effectively identifies sedentary periods appropriate for estimating breathing rate; 2) test breathing rate estimation on a data set with a total duration that is more than 60 times longer than that of the largest previously reported study, 3) test breathing rate estimation on data from a physiological monitor that has not been expressly designed for that purpose.
呼吸频率是通过佩戴在胸部的加速度计进行估算的，该加速度计由一款可穿戴生理监测仪在训练期间收集了1522名军人的数据。总共分析了29189小时的训练和睡眠数据。该监测仪的主要目的是评估热工作负荷并避免热损伤。因此，监测仪的设计并非针对估算呼吸频率进行优化。由于在高活动期间无法准确估算呼吸频率，所以应用了一个限定条件来识别久坐时间段，总计8867小时。呼吸频率的估算时长总计为4179小时，占总收集时长的14%，占久坐总时长的47%，主要是在睡眠期间。在一项受控实验室实验中，将呼吸频率估算方法与一款获得美国食品药品监督管理局（FDA）510(K)许可的标准呼吸频率传感器（美国马里兰州安纳波利斯市的Zephyr公司生产）进行了比较，结果表明这两种技术之间具有良好的一致性。本文的贡献在于：1）首次对来自自由生活数据（包括高活动期和睡眠期）的加速度计衍生呼吸频率进行分析，并给出一个能有效识别适合估算呼吸频率的久坐时间段的限定条件；2）在一个总时长比之前报道的最大研究时长多60多倍的数据集上测试呼吸频率估算；3）在一个并非专门为此目的设计的生理监测仪的数据上测试呼吸频率估算。
