Abstract
Virtual reality (VR) headsets, with embedded micro-electromechanical systems, have the potential to assess the mechanical heart’s functionality and respiratory activity in a non-intrusive way and without additional sensors by utilizing the ballistocardiographic principle. To test the feasibility of this approach for opportunistic physiological monitoring, thirty healthy volunteers were studied at rest in different body postures (sitting (SIT), standing (STAND) and supine (SUP)) while accelerometric and gyroscope data were recorded for 30 s using a VR headset (Oculus Go, Oculus, Microsoft, USA) simultaneously with a 1-lead electrocardiogram (ECG) signal for mean heart rate (HR) estimation. In addition, longer VR acquisitions (50 s) were performed under controlled breathing in the same three postures to estimate the respiratory rate (RESP). Three frequency-based methods were evaluated to extract from the power spectral density the corresponding frequency. By the obtained results, the gyroscope outperformed the accelerometer in terms of accuracy with the gold standard. As regards HR estimation, the best results were obtained in SIT, with Rs2 (95% confidence interval) = 0.91 (0.81−0.96) and bias (95% Limits of Agreement) −1.6 (5.4) bpm, followed by STAND, with Rs2 = 0.81 (0.64−0.91) and −1.7 (11.6) bpm, and SUP, with Rs2 = 0.44 (0.15−0.68) and 0.2 (19.4) bpm. For RESP rate estimation, SUP showed the best feasibility (98%) to obtain a reliable value from each gyroscope axis, leading to the identification of the transversal direction as the one containing the largest breathing information. These results provided evidence of the feasibility of the proposed approach with a degree of performance and feasibility dependent on the posture of the subject, under the conditions of keeping the head still, setting the grounds for future studies in real-world applications of HR and RESP rate measurement through VR headsets.
Highlights
Virtual reality (VR) and augmented reality headsets represent state-of-the-art technologically advanced systems able to simulate real-word interactive experience through a combination of technologies [1]
Our aim was to test the feasibility of deriving heart rate (HR) and respiratory (RESP) rate from tri-axial GYR and ACC sensors embedded in a head-worn VR device while wearing it in three different body postures in order to investigate the performance compared to relevant gold standards (ECG and imposed breathing frequency, respectively) and to verify how each body posture might limit the ability to detect the physiological information of interest
(3) The accuracy of GYR in estimating the HR varied according to the different posture and method of analysis, with the best results obtained while the subject was sitting using the Adjusted Fast Fourier Transform (FFT) method (LoA in a range between 5 and 7 bpm), while in standing a worsening of the performance was found (LoA between 12 and 15 bpm), with both the FFT and STFT methods performing and better than the Adjusted-FFT one, probably due to the presence of additional noisy components in the power spectral densities (PSDs), relevant to body stabilization movements that were not adequately compensated
Summary
Virtual reality (VR) and augmented reality headsets represent state-of-the-art technologically advanced systems able to simulate real-word interactive experience through a combination of technologies [1]. In order to validate VR applications in a clinical setting, it is important to assess their impact in terms of quality of experience (QoE) defined as “the degree of delight or annoyance of the user of an application or service” [15]. Pre-defined rating scales [21], resulting in a subjective metric of the user immersion level or of the VR-induced stress. The latter is a bio-inspired approach based on the acquisition of physiological signals from the VR user, such as electroencephalography (EEG), heart rate (HR) and respiratory rate, facilitating real-time monitoring of QoE without subjective biases [19]. There have been efforts to measure brain activity in order to understand QoE by using various types of EEG headsets [19], but their level of intrusiveness turned out to have a bad impact on the user’s QoE
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