Contact and Remote Breathing Rate Monitoring Techniques: A Review

Ali, Mohamed; Elsayed, Ali; Méndez, Arnaldo; Savaria, Yvon; Sawan, Mohamad

doi:10.1109/jsen.2021.3072607

Cited by 56 publications

(20 citation statements)

References 136 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3. The resistance of the sensor varies with the bending of the sensor surface, which is directly proportional to the amount of the bend, as shown in (1). This is because with the bend, the voltage and current change, consequently resistances change [20].…”

Section: B Proposed Sensormentioning

confidence: 99%

“…I N THE current pandemic scenario, when COVID-19 hits the world, sensors connected with artificial intelligence proved beneficial for remotely monitoring the health of isolated patients. Remote monitoring helps to track the health of the patients by measuring various readings related to vitals such as body temperature, blood oxygen saturation, and heart rate [1], [2]. There are different wearable devices and systems using a diverse range of sensors present in the market that help in the remote monitoring of patients.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

i-Sheet: A Low-Cost Bedsheet Sensor for Remote Diagnosis of Isolated Individuals

2023

View full text Add to dashboard Cite

In this article, we propose a smart bedsheeti-Sheet-for remotely monitoring the health of COVID-19 patients. Typically, real-time health monitoring is very crucial for COVID-19 patients to prevent their health from deteriorating. Conventional healthcare monitoring systems are manual and require patient input to start monitoring health. However, it is difficult for the patients to give input in critical conditions as well as at night. For instance, if the oxygen saturation level decreases during sleep, then it is difficult to monitor. Furthermore, there is a need for a system that monitors post-COVID effects as various vitals get affected, and there are chances of their failure even after the recovery. i-Sheet exploits these features and provides the health monitoring of COVID-19 patients based on their pressure on the bedsheet. It works in three phases: 1) sensing the pressure exerted by the patient on the bedsheet; 2) categorizing the data into groups (comfortable and uncomfortable) based on the fluctuations in the data; and 3) alerting the caregiver about the condition of the patient. Experimental results demonstrate the effectiveness of i-Sheet in monitoring the health of the patient. i-Sheet effectively categorizes the condition of the patient with an accuracy of 99.3% and utilizes 17.5 W of the power. Furthermore, the delay involved in monitoring the health of patients using i-Sheet is 2 s which is very diminutive and is acceptable.

show abstract

Section: B Proposed Sensormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

i-Sheet: A Low-Cost Bedsheet Sensor for Remote Diagnosis of Isolated Individuals

2023

View full text Add to dashboard Cite

show abstract

“…As evident, the systems in [33,41] offer excellent performances in minimum detection limits, but lack wearability since both sensing systems are thought for fixed or portable instruments. Also, the solutions proposed in [32,35,41,44] show good reliability but involve particular invasiveness for the users since they require a band on the upper part of the human body. According to us, the sensing system in [52] represents the best solution in terms of performance and wearability since it can be applied on the septa of a pair of glasses, thus ensuring continuous monitoring during daily life.…”

Section: ∆Rmentioning

confidence: 99%

“…are analyzed for detecting the breathing movements and thus extracting the respiration rate [40][41][42]. Several embedded systems are proposed in the scientific literature, including one or more inertial sensors, a processing unit, and a communication module for wirelessly transmits the acquired data toward a host device or cloud platform, allowing remote monitoring of user's conditions [43,44]. Furthermore, an overview of the main algorithms for extracting the respiratory rate from the raw inertial data is reported.…”

mentioning

confidence: 99%

An Overview of Wearable Piezoresistive and Inertial Sensors for Respiration Rate Monitoring

et al. 2021

View full text Add to dashboard Cite

The demand for wearable devices to measure respiratory activity is constantly growing, finding applications in a wide range of scenarios (e.g., clinical environments and workplaces, outdoors for monitoring sports activities, etc.). Particularly, the respiration rate (RR) is a vital parameter since it indicates serious illness (e.g., pneumonia, emphysema, pulmonary embolism, etc.). Therefore, several solutions have been presented in the scientific literature and on the market to make RR monitoring simple, accurate, reliable and noninvasive. Among the different transduction methods, the piezoresistive and inertial ones satisfactorily meet the requirements for smart wearable devices since unobtrusive, lightweight and easy to integrate. Hence, this review paper focuses on innovative wearable devices, detection strategies and algorithms that exploit piezoresistive or inertial sensors to monitor the breathing parameters. At first, this paper presents a comprehensive overview of innovative piezoresistive wearable devices for measuring user’s respiratory variables. Later, a survey of novel piezoresistive textiles to develop wearable devices for detecting breathing movements is reported. Afterwards, the state-of-art about wearable devices to monitor the respiratory parameters, based on inertial sensors (i.e., accelerometers and gyroscopes), is presented for detecting dysfunctions or pathologies in a non-invasive and accurate way. In this field, several processing tools are employed to extract the respiratory parameters from inertial data; therefore, an overview of algorithms and methods to determine the respiratory rate from acceleration data is provided. Finally, comparative analysis for all the covered topics are reported, providing useful insights to develop the next generation of wearable sensors for monitoring respiratory parameters.

show abstract

“…Traditionally, reliable electrocardiography sensors or respiration belts have been adopted to measure RR, but in some cases, these approaches are not feasible due to several reasons, from physical discomfort, to the need of employing dedicated equipment and expertise, up to the actual impossibility of placing the required sensors [ 3 ]. Alternative solutions rely on the adoption of photoplethysmography (PPG) sensors, such as pulse oximeters, being less invasive and augmenting the portability [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

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

Boccignone

D’Amelio

Ghezzi

et al. 2023

Sensors

View full text Add to dashboard Cite

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.

show abstract

Contact and Remote Breathing Rate Monitoring Techniques: A Review

Cited by 56 publications

References 136 publications

i-Sheet: A Low-Cost Bedsheet Sensor for Remote Diagnosis of Isolated Individuals

i-Sheet: A Low-Cost Bedsheet Sensor for Remote Diagnosis of Isolated Individuals

An Overview of Wearable Piezoresistive and Inertial Sensors for Respiration Rate Monitoring

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

Contact Info

Product

Resources

About