Regular monitoring of common physiological signs, including heart rate, blood pressure, and oxygen saturation, can be an effective way to either prevent or detect many kinds of chronic conditions. In particular, cardiovascular diseases (CVDs) are a worldwide concern. According to the World Health Organization, 32% of all deaths worldwide are from CVDs. In addition, stress-related illnesses cost $190 billion in healthcare costs per year. Currently, contact devices are required to extract most of an individual's physiological information, which can be uncomfortable for users and can cause discomfort. However, in recent years, remote photoplethysmography (rPPG) technology is gaining interest, which enables contactless monitoring of the blood volume pulse signal using a regular camera, and ultimately can provide the same physiological information as a contact device. In this paper, we propose a benchmark comparison using a new multimodal database consisting of 56 subjects where each subject was submitted to three different tasks. Each subject wore a wearable device capable of extracting photoplethysmography signals and was filmed to allow simultaneous rPPG signal extraction. Several experiments were conducted, including a comparison between information from contact and remote signals and stress state recognition. Results have shown that in this dataset, rPPG signals were capable of dealing with motion artifacts better than contact PPG sensors and overall had better quality if compared to the signals from the contact sensor. Moreover, the statistical analysis of the variance method had shown that at least two heart-rate variability (HRV) features, NNi 20 and SAMPEN, were capable of differentiating between stress and non-stress states. In addition, three features, inter-beat interval (IBI), NNi 20, and SAMPEN, were capable of differentiating between tasks relating to different levels of difficulty. Furthermore, using machine learning to classify a "stressed" or "unstressed" state, the models were able to achieve an accuracy score of 83.11%.

Atrial Fibrillation (AFIB) is a common atrial arrhythmia that affects millions of people worldwide. However, most of the time, AFIB is paroxysmal and can pass unnoticed in medical exams therefore regular screening is required. This paper proposes machine learning methods to detect AFIB from short-term ECG and PPG signals. Several experiments were conducted across five different databases with three of them containing ECG signals and the other two consisting of only PPG signals. A total of 269,842 signal segments were analyzed across all datasets (212,266 were of normal sinus rhythm (NSR) and 57,576 corresponded to AFIB segments). Experiments were conducted to investigate the hypothesis that a machine learning model trained to predict AFIB from ECG segments, could be used to predict AFIB from PPG segments. A random forest machine learning algorithm achieved the best accuracy and achieved a 90% accuracy rate on the UMMC dataset (216 samples) and a 97% accuracy rate on the MIMIC-III dataset (2,134 samples). The ability to detect AFIB with significant accuracy using machine learning algorithms from PPG signals, which can be acquired via non-invasive contact or contactless, is a promising step forward toward the goal of achieving large-scale screening for AFIB.

Regular monitoring of common physiological signs, including heart rate, blood pressure, and oxygen saturation, can be an effective way to either prevent or detect many kinds of chronic conditions. In particular, cardiovascular diseases (CVDs) are a worldwide concern. According to the World Health Organization, 32% of all deaths worldwide are from CVDs. In addition, stress-related issues cost $190 billion in healthcare costs per year. Currently, contact devices are required to extract most of an individuals physiological information, which can be uncomfortable for users and can cause discomfort. However, in recent years, remote photoplethysmography (rPPG) technology is gaining growing interest, which enables contactless monitoring of the blood volume pulse signal using a regular camera, and ultimately can provide the same physiological information as a contact device. In this paper, we propose a benchmark comparison using a new multimodal database consisting of 56 subjects where each subject was submitted to three different tasks. Each subject wore a wearable device capable of extracting photoplethysmography signals and was filmed to allow simultaneous rPPG signal extraction. Several experiments were conducted, including a comparison between information from contact and remote signals and stress state recognition. Results have shown that in this dataset, rPPG signals were capable of dealing with motion artifacts better than contact PPG sensors and overall had better quality if compared to the signals from the contact sensor. Moreover, the statistical analysis of the variance method had shown that at least two HRV features, NNi 20 and SAMPEN were capable of differentiating between Stress and Non-Stress states. In addition, three features, IBI, NNi 20, and SAMPEN were capable of differentiating between tasks relating to different levels of difficulty. Furthermore, using machine learning to classify a stressed or unstressed state, the models were able to achieve an accuracy score of 83.11%.

BackgroundRegularly monitoring common physiological signs, including heart rate, blood pressure, and oxygen saturation, can effectively prevent or detect several potential conditions. In particular, cardiovascular diseases (CVDs) are a worldwide concern. According to the World Health Organization, 31% of all deaths worldwide are from CVDs. Recently, the coronavirus disease 2019 pandemic has increased the interest in remote monitoring. At present, contact devices are required to extract most of an individual's physiological information, which can be inconvenient for users and may cause discomfort. MethodologyHowever, remote photoplethysmography (rPPG) technology offers a solution for this issue, enabling contactless monitoring of the blood volume pulse signal using a regular camera. Ultimately, it can provide the same physiological information as a contact device. In this paper, we propose an evaluation of Vastmindz's rPPG technology against medical devices in a clinical environment with a variety of subjects in a wide range of age, height, weight, and baseline vital signs. ResultsThis study confirmed the findings that the contactless technology for the estimation of vitals proposed by Vastmindz was able to estimate heart rate, respiratory rate, and oxygen saturation with a mean error of ±3 units as well as ±10 mmHg for systolic and diastolic blood pressure. ConclusionsReported results have shown that Vastmindz's rPPG technology was able to meet the initial hypothesis and is acceptable for users who want to understand their general health and wellness.

Regular monitoring of common physiological signs, including heart rate, blood pressure, and oxygen saturation, can be an effective way to either prevent or detect several potential conditions. In particular, cardiovascular diseases (CVDs) are a worldwide concern. According to the World Health Organization, 31% of all deaths worldwide are from CVDs. Recently, the COVID-19 pandemic has increased the interest in remote monitoring. At present, contact devices are required to extract most of an individual’ s physiological information, which can be inconvenient for users and may cause discomfort. However, remote photoplethysmography (rPPG) technology offers a solution for this issue, which enables contactless monitoring of the blood volume pulse signal using a regular camera, and ultimately can provide the same physiological information as a contact device. In this paper we propose an evaluation of rPPG technology against medical devices in a clinical environment, with a variety of subjects in a wide range of age, height, weight, and baseline vital signs. Results have shown that rPPG technology was able to meet the initial hypothesis of mean error of +/-3 units for Heart rate, Respiration Rate, and SpO2 estimation, as well as +/-10 mmHg for Systolic and Diastolic Blood Pressure.