An Evaluation of Non-Contact Photoplethysmography-Based Methods for Remote Respiratory Rate Estimation

Abstract

The respiration rate (RR) is one of the physiological signals deserving monitoring for assessing human health and emotional states. However, traditional devices, such as the respiration belt to be worn around the chest, are not always a feasible solution (e.g., telemedicine, device discomfort). Recently, novel approaches have been proposed aiming at estimating RR in a less invasive yet reliable way, requiring the acquisition and processing of contact or remote Photoplethysmography (contact reference and remote-PPG, respectively). The aim of this paper is to address the lack of systematic evaluation of proposed methods on publicly available datasets, which currently impedes a fair comparison among them. In particular, we evaluate two prominent families of PPG processing methods estimating Respiratory Induced Variations (RIVs): the first encompasses methods based on the direct extraction of morphological features concerning the RR; and the second group includes methods modeling respiratory artifacts adopting, in the most promising cases, single-channel blind source separation. Extensive experiments have been carried out on the public BP4D+ dataset, showing that the morphological estimation of RIVs is more reliable than those produced by a single-channel blind source separation method (both in contact and remote testing phases), as well as in comparison with a representative state-of-the-art Deep Learning-based approach for remote respiratory information estimation.

An Optical Micro/Nano Fiber Sensor for Monitoring Respiration

“…In addition, the data showed four periodic changes in the optical power within 30 s of the monitoring time, according to the experimental results of 1, 3.5, and 6.3 µm diameters in Figure 8. Therefore, it can be calculated that the respiratory rate is 10-11 times/minute, and this result is consistent with the average human respiratory rate [6,24].…”

Assessment of Voice Disorders Using Machine Learning and Vocal Analysis of Voice Samples Recorded through Smartphones

“…In fact, smartphones are equipped with several sensors that could be useful for diagnostic purposes. For instance, the visual camera could provide pictures or videos able to detect the affective state of individuals, but it could also provide information regarding skin diseases and heart rate through algorithms for remote photoplethysmography (PPG) [7]. Moreover, the effectiveness of the continuous monitoring of the health condition of a subject can be improved by coupling the smartphone with wearable devices such as smartwatches, which can record several physiological signals based on PPG and accelerometry [2,[8][9][10][11].…”

Non-Contact Respiration Rate Measurement From Thermal Images Using Multi-Resolution Window and Phase-Sensitive Processing

“…Also, the method of chest impedance presents a limitation when detecting episodes of apnea or using electro surgical unit (ESU). Therefore, recently, to solve problems and not harm patients, studies in a non-contact method have been proposed [6][7][8][9][10][11][12][13][14][15]. As a method of acquiring respiration data in a contactless method, there are some cases using radar sensors [6][7][8].…”

“…So, it can only measure data accurately when the chest is exposed without the patient's clothing. In another way, respiration can be measured in a noncontact method using the camera [9][10][11][12][13][14][15]. The camera method is very convenient and has the advantage of measuring respiration using a smartphone or an inexpensive camera sensor.…”