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智能面罩设计与测试:用于骑行运动中的呼吸监测

Design and Testing of a Smart Facemask for Respiratory Monitoring during Cycling Exercise.

机构信息

The Departmental Faculty of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy.

Department of Movement, Human and Health Sciences, University of Rome "Foro Italico", 00135 Rome, Italy.

出版信息

Biosensors (Basel). 2023 Mar 10;13(3):369. doi: 10.3390/bios13030369.

Abstract

Given the importance of respiratory frequency () as a valid marker of physical effort, there is a growing interest in developing wearable devices measuring in applied exercise settings. Biosensors measuring chest wall movements are attracting attention as they can be integrated into textiles, but their susceptibility to motion artefacts may limit their use in some sporting activities. Hence, there is a need to exploit sensors with signals minimally affected by motion artefacts. We present the design and testing of a smart facemask embedding a temperature biosensor for monitoring during cycling exercise. After laboratory bench tests, the proposed solution was tested on cyclists during a ramp incremental frequency test (RIFT) and high-intensity interval training (HIIT), both indoors and outdoors. A reference flowmeter was used to validate the extracted from the temperature respiratory signal. The smart facemask showed good performance, both at a breath-by-breath level (MAPE = 2.56% and 1.64% during RIFT and HIIT, respectively) and on 30 s average values (MAPE = 0.37% and 0.23% during RIFT and HIIT, respectively). Both accuracy and precision (MOD ± LOAs) were generally superior to those of other devices validated during exercise. These findings have important implications for exercise testing and management in different populations.

摘要

鉴于呼吸频率()作为体力活动有效标志物的重要性,人们越来越关注开发可在应用运动环境中测量的可穿戴设备。测量胸廓运动的生物传感器因其可集成到纺织品中而受到关注,但它们容易受到运动伪影的影响,可能会限制它们在某些运动中的使用。因此,需要利用受运动伪影影响最小的传感器信号。我们提出了一种智能面罩的设计和测试方案,该面罩嵌入了一个温度传感器,用于在骑自行车运动时监测呼吸频率。经过实验室 bench 测试后,我们在室内和室外的递增频率测试(RIFT)和高强度间歇训练(HIIT)中对该方案进行了测试。使用参考流量计来验证从温度呼吸信号中提取的呼吸频率。智能面罩在逐次呼吸水平(RIFT 和 HIIT 时的平均绝对百分比误差(MAPE)分别为 2.56%和 1.64%)和 30 秒平均值(RIFT 和 HIIT 时的 MAPE 分别为 0.37%和 0.23%)上均表现出良好的性能。准确性和精密度(MOD ± LOAs)通常优于在运动中验证的其他设备。这些发现对不同人群的运动测试和管理具有重要意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7884/10046471/f4372d01814f/biosensors-13-00369-g001.jpg

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