Heart rate detection from the supratrochlear vessels using a virtual reality headset integrated PPG sensor

An increasing amount of virtual reality (VR) research is carried out to support the vast number of applications across mental health, exercise and entertainment fields. Often, this research involves the recording of physiological measures such as heart rate recordings with an electrocardiogram (ECG). One challenge is to enable remote, reliable and unobtrusive VR and heart rate data collection which would allow a wider application of VR research and practice in the field in future. To address the challenge, this work assessed the viability of replacing standard ECG devices with a photoplethysmography (PPG) sensor that is directly integrated into a VR headset over the branches of the supratrochlear vessels. The objective of this study was to investigate the reliability of the PPG sensor for heart-rate detection. A total of 21 participants were recruited. They were asked to wear an ECG belt as ground truth and a VR headset with the embedded PPG sensor. Signals from both sensors were captured in free standing and sitting positions. Results showed that VR headset with an integrated PPG sensor is a viable alternative to an ECG for heart rate measurements in optimal conditions with limited movement. Future research will extend on this finding by testing it in more interactive VR settings

Detection and Removal of Motion Artifacts in PPG Signals

With the rise of wearable devices, which integrate myriad of health-care and fitness procedures into daily life, a reliable method for measuring various bio-signals in a daily setup is more desired than ever. Many of these physiological parameters, such as Heart rate (HR) and Respiratory Rate (RR), are extracted indirectly and using other signals such as Photoplethysmograph (PPG). Part of the reason is that in some cases, such as RR measurements, the devices which directly measure them are cumbersome to wear and thus, rather impractical. On the other hand, signals, such as PPG from which the RR can be extracted, are not very clean. This poses a challenge on reliable extraction of these metrics. The most important problem is that they are corrupted by motion artifacts. In this paper, we review the state of the art algorithms which are used to detect and filter motion artifacts in PPG signals and compare them in terms of their performance. The insight provided by this paper can help the scientists and engineers to obtain a better understanding of the field and be able to use the most suitable technique for their work, or come up with innovative solutions based on existing ones