Polytechnic Department of Engineering and Architecture, University of Udine, 33100 Udine, Italy.
Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture, University of Genoa, 16145 Genoa, Italy.
Sensors (Basel). 2024 Sep 12;24(18):5908. doi: 10.3390/s24185908.
Monitoring heart rate (HR) through photoplethysmography (PPG) signals is a challenging task due to the complexities involved, even during routine daily activities. These signals can indeed be heavily contaminated by significant motion artifacts resulting from the subjects' movements, which can lead to inaccurate heart rate estimations. In this paper, our objective is to present an innovative necklace sensor that employs low-computational-cost algorithms for heart rate estimation in individuals performing non-abrupt movements, specifically drivers. Our solution facilitates the acquisition of signals with limited motion artifacts and provides acceptable heart rate estimations at a low computational cost. More specifically, we propose a wearable sensor necklace for assessing a driver's well-being by providing information about the driver's physiological condition and potential stress indicators through HR data. This innovative necklace enables real-time HR monitoring within a sleek and ergonomic design, facilitating seamless and continuous data gathering while driving. Prioritizing user comfort, the necklace's design ensures ease of wear, allowing for extended use without disrupting driving activities. The collected physiological data can be transmitted wirelessly to a mobile application for instant analysis and visualization. To evaluate the sensor's performance, two algorithms for estimating the HR from PPG signals are implemented in a microcontroller: a modified version of the mountaineer's algorithm and a sliding discrete Fourier transform. The goal of these algorithms is to detect meaningful peaks corresponding to each heartbeat by using signal processing techniques to remove noise and motion artifacts. The developed design is validated through experiments conducted in a simulated driving environment in our lab, during which drivers wore the sensor necklace. These experiments demonstrate the reliability of the wearable sensor necklace in capturing dynamic changes in HR levels associated with driving-induced stress. The algorithms integrated into the sensor are optimized for low computational cost and effectively remove motion artifacts that occur when users move their heads.
通过光电容积脉搏波（PPG）信号监测心率（HR）是一项具有挑战性的任务，因为即使在日常常规活动中，也涉及到许多复杂性。这些信号确实可能受到来自受试者运动的显著运动伪影的严重污染，这可能导致不准确的心率估计。在本文中，我们的目标是介绍一种创新的项链传感器，该传感器采用低计算成本算法，用于估计进行非突然运动的个体（特别是驾驶员）的心率。我们的解决方案促进了具有有限运动伪影的信号的获取，并以低计算成本提供可接受的心率估计。更具体地说，我们提出了一种可穿戴传感器项链，通过 HR 数据为驾驶员的生理状况和潜在的压力指标提供信息，从而评估驾驶员的健康状况。这种创新的项链通过提供有关驾驶员生理状况和潜在压力指标的信息，实现了 HR 的实时监测，使驾驶员能够在驾驶过程中舒适、方便地佩戴，实现无缝、连续的数据采集。项链的设计优先考虑用户的舒适度，确保佩戴方便，允许长时间使用而不会干扰驾驶活动。收集到的生理数据可以通过无线方式传输到移动应用程序进行即时分析和可视化。为了评估传感器的性能，在微控制器中实现了两种用于从 PPG 信号估计 HR 的算法：登山者算法的修改版本和滑动离散傅里叶变换。这些算法的目标是通过使用信号处理技术去除噪声和运动伪影来检测与每个心跳相对应的有意义的峰值。通过在我们实验室的模拟驾驶环境中进行的实验，对所开发的设计进行了验证，在此期间，驾驶员佩戴了传感器项链。这些实验证明了可穿戴传感器项链在捕捉与驾驶引起的压力相关的 HR 水平的动态变化方面的可靠性。集成到传感器中的算法经过优化，以实现低计算成本，并有效地去除用户移动头部时出现的运动伪影。
