根据不受控制的户外环境中的身体位置和身体运动情况,评估用于可穿戴式心率监测的光敏血压计的准确性

Manuel Meier, Christian Holz
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PPG measurements were obtained using a MAX86141 optical analog front-end coupled with a green LED and photodiode from an SFH7072 module. Motion was quantified using two accelerometers (LIS2DH, ADXL355). A Lead I ECG was obtained by the device at the sternum using a MAX30003 biopotential sensor connected to gel electrodes on the chest. All devices were synchronized by aligning recorded signals post-hoc (33ms accuracy, Meier & Holz, 2023). The HR was extracted from the ECG by time-domain peak detection. The HR extraction from PPG was both performed by time-domain peak detection and frequency-domain analysis. The HR was computed every 5 seconds (30 seconds window size) resulting in 152,000 HR measurements across the whole dataset. \nResults \nThe forehead and chest locations exhibited the highest HR measurement accuracy (median error 7.1% and 7.7%, respectively), while lower accuracies were observed for ankle and wrist placements (9.9% and 18.4% error). 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引用次数: 0

摘要

导言 反射式血压计(PPG)是消费类可穿戴设备进行心率(HR)监测的主要方法。然而,运动伪影和传感器位置会影响这些心率测量的准确性。在此,我们就这两个因素如何影响基于 PPG 的心率测量的准确性进行了研究,并将其与心电图(ECG)的地面实况测量结果进行了比较。我们的研究调查了在受控实验室环境之外的室外环境中进行的这些测量。方法 我们的研究收集了 16 位参与者的数据集,每位参与者佩戴四个反射式 PPG 传感装置 13 小时,分别置于前额、胸骨、脚踝(踝上)和手腕处。参与者乘坐火车从苏黎世市中心前往海拔 3460 米的少女峰火车站。PPG 测量是使用 MAX86141 光学模拟前端和一个绿色 LED 以及 SFH7072 模块的光电二极管进行的。通过两个加速度计(LIS2DH、ADXL355)对运动进行量化。胸骨处的设备通过与胸部凝胶电极相连的 MAX30003 生物电位传感器获取 I 导联心电图。所有设备均通过事后对齐记录信号实现同步(精确度为 33 毫秒,Meier & Holz,2023 年)。心率是通过时域峰值检测从心电图中提取的。通过时域峰值检测和频域分析从 PPG 中提取心率。心率每 5 秒计算一次(窗口大小为 30 秒),因此整个数据集的心率测量值为 152,000 次。结果 前额和胸部位置的心率测量准确率最高(中位数误差分别为 7.1% 和 7.7%),而脚踝和手腕位置的测量准确率较低(误差分别为 9.9% 和 18.4%)。在静息状态下,所有中位误差都低于 5%,而运动对所有位置的读数都有负面影响。经运动调整后,前额传感器获得的心率最为准确。就处理方法而言,时域分析在低运动时的准确性更高,而频域分析在运动时的准确性更高。讨论/结论 在不受控制的室外环境中,基于 PPG 的心率测量的准确性同时受到身体位置和运动伪影的影响,其适用部位有明显的排序:前额 >> 胸部 >> 脚踝 >> 手腕。这与之前在受控环境中进行的研究一致,尽管我们的研究发现运动比身体位置对心率准确性的影响更大(Longmore 等人,2019 年)。这可能是因为参与者在非受控环境中的运动更加不规则和多样化,导致信号质量下降。我们的研究表明,在可穿戴设备中进行更可靠的基于 PPG 的心率监测的道路上,在日常条件下开展进一步调查非常重要。参考文献 Longmore, S. K., Lui, G. Y., Naik, G., Breen, P. P., Jalaludin, B., & Gargiulo, G. D. (2019).用于检测不同解剖位置的心率、血氧饱和度和呼吸频率的反射式光心动图比较。https://doi.org/10.3390/s19081874 Meier, M., & Holz, C. (2023).BMAR:基于气压和运动的跨设备离线信号同步对齐和细化。ACM 交互、移动、可穿戴和泛在技术论文集》,7(2),第 69 条。 https://doi.org/10.1145/3596268
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing the accuracy of photoplethysmography for wearable heart rate monitoring based on body location and body motion in uncontrolled outdoor environments
Introduction Reflective photoplethysmography (PPG) is the dominant method for heart rate (HR) monitoring consumer wearables. However, motion artifacts and sensor placement impact the accuracy of these HR measurements. Here, we present a study on how these two factors affect the accuracy of PPG-based HR measurements and compare them to ground-truth measurements from electrocardiography (ECG). Our study investigated these measurements in outdoor environments outside controlled laboratory settings. Methods Our study collected a dataset of 16 participants, each for 13 hours wearing four reflective PPG sensing devices placed at the forehead, sternum, ankle (supramalleolar), and wrist. Participants traveled by train from downtown Zurich to the Jungfraujoch railway station at 3,460 m above sea level in the mountains. PPG measurements were obtained using a MAX86141 optical analog front-end coupled with a green LED and photodiode from an SFH7072 module. Motion was quantified using two accelerometers (LIS2DH, ADXL355). A Lead I ECG was obtained by the device at the sternum using a MAX30003 biopotential sensor connected to gel electrodes on the chest. All devices were synchronized by aligning recorded signals post-hoc (33ms accuracy, Meier & Holz, 2023). The HR was extracted from the ECG by time-domain peak detection. The HR extraction from PPG was both performed by time-domain peak detection and frequency-domain analysis. The HR was computed every 5 seconds (30 seconds window size) resulting in 152,000 HR measurements across the whole dataset. Results The forehead and chest locations exhibited the highest HR measurement accuracy (median error 7.1% and 7.7%, respectively), while lower accuracies were observed for ankle and wrist placements (9.9% and 18.4% error). At rest, all median errors were below 5% while movements influenced readings at all locations negatively. Adjusted for motion, the HR obtained from the forehead sensor was most accurate. In terms of processing method, time-domain analysis produced better accuracy during periods of low motion while frequency-domain analysis was more reliable during movements. Discussion/Conclusion The accuracy of PPG-based HR measurements in uncontrolled outdoor settings is affected both by body location and motion artifacts with a clear ranking of site suitability: forehead >> chest >> ankle >> wrist. This is consistent with prior studies in controlled environments, though our study found a higher impact of motion than body location on HR accuracy (Longmore et al., 2019). This may be because participants’ motions in uncontrolled environments are more irregular and diverse, resulting in deteriorated signal quality. Our study shows the importance of further investigations in everyday conditions on the path toward more reliable PPG-based HR monitoring in wearable devices. References Longmore, S. K., Lui, G. Y., Naik, G., Breen, P. P., Jalaludin, B., & Gargiulo, G. D. (2019). A comparison of reflective photoplethysmography for detection of heart rate, blood oxygen saturation, and respiration rate at various anatomical locations. Sensors, 19(8), Article 1874. https://doi.org/10.3390/s19081874 Meier, M., & Holz, C. (2023). BMAR: Barometric and Motion-based Alignment and Refinement for offline signal synchronization across devices. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 7(2), Article 69. https://doi.org/10.1145/3596268
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