Lightweight wrist photoplethysmography for heavy exercise: motion robust heart rate monitoring algorithm.

The challenge of heart rate monitoring based on wrist photoplethysmography (PPG) during heavy exercise is addressed. PPG is susceptible to motion artefacts, which have to be mitigated for accurate heart rate estimation. Motion artefacts are particularly apparent for wrist devices, for example, a smart watch, because of the high mobility of the arms. Proposed is a low complexity highly accurate heart rate estimation method for continuous heart rate monitoring using wrist PPG. The proposed method achieved 2.57% mean absolute error in a test data set where subjects ran for a maximum speed of 17 km/h.

Challenges in wearable personal health monitoring systems.

Wearable sensors give the users convenience in daily health monitoring, though several challenges in such sensor systems should be overcome. This paper discusses the challenges in wearable health monitoring sensors and solutions for multi-modal and multi-functional wrist-worn devices based on novel circuit design techniques to reject DC offset. This paper also presents a novel sophisticated algorithm to reject motion artifacts. The system has the capability to simultaneously acquire several biomedical signals (i.e. electrocardiogram, PPG, and body-electrode impedance). The system can also help patients who want to monitor their psychological signals to mitigate health risks.

New phases found in reverse micelle systems with high concentrations of AOT.

This paper discusses the phase behavior, rheology, and structure of self-assembled sodium bis(2-ethylhexyl) sulfosuccinate (AOT) reverse micelle systems at high AOT concentrations. When the amount of AOT and w(o) (the molar ratio of water to AOT) were changed, many different phases were found, a fact which is not discussed in the literature. Opaque gel-like phase (phase separation) occurred with high concentrations of AOT in organic solvents without water. When the AOT concentration and w(o) were increased to 18-72 m and 2, respectively, the samples were gel-like and translucent. Dynamic rheological results indicate that the viscoelastic transition agreed with a multirelaxation time model. Small-angle X-ray scattering (SAXS) results imply that these samples showed a hexagonally close-packed cylindrical structure in which the diameter of a cylinder was ~2.5-3.0 nm, depending on the water contents. Moreover, these AOT cylinders self-assembled into fiber bundles with a diameter of 1-10 µm, as determined using a polarized optical microscope. As w(o) was increased to 2-6 in 72 m AOT samples, similar rheological and SAXS results were obtained. However, a different type of viscoelastic transition occurred, from multirelaxation to single-relaxation, when w(o) was increased to 7-11. The samples were in the transparent gel-like phase, and the structures determined by SAXS were a combination of hexagonally packed cylindrical and lamellar structure. The close-packed cylindrical structures had larger radii and shorter lengths with increasing w(o). Furthermore, when w(o) was increased to 12, the gel-like phase disappeared and a highly viscous solution was observed. This is because all the cylindrical structures collapsed and transformed into lamellar structures when the amount of water was further increased.

Hidden state models for noncontact measurements of the carotid pulse using a laser Doppler vibrometer.

The method of laser Doppler vibrometry (LDV) is used to sense movements of the skin overlying the carotid artery. When pointed at the skin overlying the carotid artery, the mechanical movements of the skin disclose physiological activity relating to the blood pressure pulse over the cardiac cycle. In this paper, signal modeling is addressed, with close attention to the underlying physiology. Segments of the LDV signal corresponding to single heartbeats, called LDV pulses, are extracted. Hidden Markov models (HMMs) are used to capture the dynamics of the LDV pulses from beat to beat based on pulse morphology; under resting conditions these dynamics are primarily due to respiration-related effects. LDV pulses are classified according to state, by computing the optimal state path through the data using trained HMMs. HMM state dynamics are examined within the context of respiratory effort using strain gauges placed around the abdomen. This study presented here provides a graphical model approach to modeling the dependence of the LDV pulse on latent states.

Hidden state dynamics in laser Doppler vibrometery measurements of the carotid pulse under resting conditions.

A laser Doppler vibrometer (LDV) is used to sense movements of the skin overlying the carotid artery. Fluctuations in carotid artery diameter due to variations in the underlying blood pressure are sensed at the surface of the skin. Portions of the LDV signal corresponding to single heartbeats, called the LDV pulses, are extracted. This paper introduces the use of hidden Markov models (HMMs) to model the dynamics of the LDV pulse from beat to beat based on pulse morphology, which under resting conditions are primarily due to breathing effects. LDV pulses are classified according to state, by computing the optimal state path through the data using trained HMMs. HMM state dynamics are compared to simultaneous recordings of strain gauges placed on the abdomen. The work presented here provides a robust statistical approach to modeling the dependence of the LDV pulse on latent states.