Rapid Vital Sign Extraction for Real-Time Opto-Physiological Monitoring at Varying Physical Activity Intensity Levels.

Robustness of physiological parameters obtained from photoplethysmographic (PPG) signals is highly dependent on a signal quality that is often affected by the motion artefacts (MAs) generated during physical activity. This study aims to suppress MAs and obtain reliable physiological readings using the part of the pulsatile signal, captured by a multi-wavelength illumination optoelectronic patch sensor (mOEPS), that minimizes the residual between the measured signal and the motion estimates obtained from an accelerometer. The minimum residual (MR) method requires the simultaneous collection of (1) multiple wavelength data from the mOEPS, and (2) motion reference signals from a triaxial accelerometer attached to the mOEPS. The MR method suppresses those frequencies associated with motion in a manner that is easily embedded on a microprocessor. The performance of the method in reducing both in-band and out-of-band frequencies of MAs is evaluated through two protocols with 34 subjects engaged in the study. The MA-suppressed PPG signal, obtained through MR, enables the calculation of the heart rate (HR) with an average absolute error of 1.47 beats/min for the IEEE-SPC datasets, and the calculation of HR and respiration rate (RR) to 1.44 beats/min and 2.85 breaths/min respectively for our in-house datasets. Oxygen saturation (SpO 2) levels calculated from the minimum residual wave forms were consistently [Formula: see text]. The comparison with the reference HR and RR show errors with an absolute accuracy of [Formula: see text] and the Pearson correlation ( R) for HR and RR are 0.9976 and 0.9118, respectively. These outcomes demonstrate that MR is capable of effective suppression of MAs for a range of physical activity intensities and to achieve real-time signal processing for wearable health monitoring.

Frame Registration for Motion Compensation in Imaging Photoplethysmography.

Imaging photoplethysmography (iPPG) is an emerging technology used to assess microcirculation and cardiovascular signs by collecting backscattered light from illuminated tissue using optical imaging sensors. An engineering approach is used to evaluate whether a silicone cast of a human palm might be effectively utilized to predict the results of image registration schemes for motion compensation prior to their application on live human tissue. This allows us to establish a performance baseline for each of the algorithms and to isolate performance and noise fluctuations due to the induced motion from the temporally changing physiological signs. A multi-stage evaluation model is developed to qualitatively assess the influence of the region of interest (ROI), system resolution and distance, reference frame selection, and signal normalization on extracted iPPG waveforms from live tissue. We conclude that the application of image registration is able to deliver up to 75% signal-to-noise (SNR) improvement (4.75 to 8.34) over an uncompensated iPPG signal by employing an intensity-based algorithm with a moving reference frame.

Assessing blood vessel perfusion and vital signs through retinal imaging photoplethysmography.

One solution to the global challenge of increasing ocular disease is a cost-effective technique for rapid screening and assessment. Current ophthalmic imaging techniques, e.g. scanning and ocular blood flow systems, are expensive, complex to operate and utilize invasive contrast agents during assessment. The work presented here demonstrates a simple retinal imaging photoplethysmography (iPPG) system with the potential to provide screening, diagnosis, monitoring and assessment that is non-invasive, painless and radiationless. Time series of individual retinal blood vessel images, captured with an eye fundus camera, are processed using standard filtering, amplitude demodulation and principle component analysis (PCA) methods to determine the values of the heart rate (HR) and respiration rate (RR), which are in compliance with simultaneously obtained measurements using commercial pulse oximetry. It also seems possible that some information on the dynamic changes in oxygen saturation levels (SpO2) in a retinal blood vessel may also be obtained. As a consequence, the retinal iPPG modality system demonstrates a potential avenue for rapid ophthalmic screening, and even early diagnosis, against ocular disease without the need for fluorescent or contrast agents.

Smart Garment Fabrics to Enable Non-Contact Opto-Physiological Monitoring.

Imaging photoplethysmography (iPPG) is an emerging technology used to assess microcirculation and cardiovascular signs by collecting backscattered light from illuminated tissue using optical imaging sensors. The aim of this study was to study how effective smart garment fabrics could be capturing physiological signs in a non-contact mode. The present work demonstrates a feasible approach of, instead of using conventional high-power illumination sources, integrating a grid of surface-mounted light emitting diodes (LEDs) into cotton fabric to spotlight the region of interest (ROI). The green and the red LEDs (525 and 660 nm) placed on a small cotton substrate were used to locally illuminate palm skin in a dual-wavelength iPPG setup, where the backscattered light is transmitted to a remote image sensor through the garment fabric. The results show that the illuminations from both wavelength LEDs can be used to extract heart rate (HR) reaching an accuracy of 90% compared to a contact PPG probe. Stretching the fabric over the skin surface alters the morphology of iPPG signals, demonstrating a significantly higher pulsatile amplitude in both channels of green and red illuminations. The skin compression by the fabric could be potentially utilised to enhance the penetration of illumination into cutaneous microvascular beds. The outcome could lead a new avenue of non-contact opto-physiological monitoring and assessment with functional garment fabrics.

A Study of the Dynamic Relation between Physiological Changes and Spontaneous Expressions.

Recent progress in Affective Computing (AC) has enabled integration of physiological cues and spontaneous expressions to reveal a subject's emotional state. Due to the lack of an effective technique for evaluating multimodal correlations, experience and intuition play a main role in present AC studies when fusing affective cues or modalities, resulting in unexpected outcomes. This study seeks to demonstrate a dynamic correlation between two such affective cues, physiological changes and spontaneous expressions, which were obtained by a combination of stereo vision based tracking and imaging photoplethysmography (iPPG), with a designed protocol involving 20 healthy subjects. The two cues obtained were sampled into a Statistical Association Space (SAS) to evaluate their dynamic correlation. It is found that the probability densities in the SAS increase as the peaks in two cues are approached. Also the complex form of the high probability density region in the SAS suggests a nonlinear correlation between two cues. Finally the cumulative distribution on the zero time-difference surface is found to be small (<0.047) demonstrating a lack of simultaneity. These results show that the two cues have a close interrelation, that is both asynchronous and nonlinear, in which a peak of one cue heralds a peak in the other.

ClinMicroNet - Sharing experiences and building knowledge virtually.

ClinMicroNet is a closed Internet discussion group, where doctoral-level clinical and public health microbiologists from many countries share their knowledge and experience. This collaborative approach to resolving issues and questions in the field of clinical microbiology transcends organizational, institutional, state and national boundaries. Based upon observations of list communications during 10 weeks and a small group user survey, this study analyzed the nature of communications and member's perceptions of the network. An explicit-tacit knowledge quadrant identifies distinct ways in which knowledge is transferred and created. Empirical evidence shows that ClinMicroNet complements other resources by encouraging members to share experiences and collaborate in establishing the best practices. Driven by a core group of active members, the network is highly participative and strongly supported. In turn, members maintain professional relationships beyond the list, which reinforces the network and its members' capacity to confront new threats and challenges in clinical microbiology.