Simple Nanoimprinted Polymer Nanostructures for Uncooled Thermal Detection by Direct Surface Plasmon Resonance Imaging.

We experimentally demonstrate the uncooled detection of long wavelength infrared (IR) radiation by thermal surface plasmon sensing using an all optical readout format. Thermal infrared radiation absorbed by an IR-sensitive material with high thermo-optic coefficient coated on a metal grating creates a refractive index change detectable by the shift of the supported surface plasmon resonance (SPR) measured optically in the visible spectrum. The interface localization of SPR modes and optical readout allow for submicrometer thin film transducers and eliminate complex readout integrated circuits, respectively, reducing form factor, leveraging robust visible detectors, and enabling low-cost imaging cameras. We experimentally present the radiative heat induced thermo-optic action detectable by SPR shift through imaging of a thermal source onto a bulk metal grating substrate with IR-absorptive silicon nitride coating. Toward focal plane array integration, a route to facile fabrication of pixelated metal grating structures by nanoimprint lithography is developed, where a stable polymer, parylene-C, serves as an IR-absorptive layer with a high thermo-optic coefficient. Experimental detection of IR radiation from real thermal sources imaged at infinity is demonstrated by our nanoimprinted polymer-SPR pixels with an estimated noise equivalent temperature difference of 21.9 K.

Heart rate variability estimation in photoplethysmography signals using Bayesian learning approach.

Heart rate variability (HRV) has become a marker for various health and disease conditions. Photoplethysmography (PPG) sensors integrated in wearable devices such as smart watches and phones are widely used to measure heart activities. HRV requires accurate estimation of time interval between consecutive peaks in the PPG signal. However, PPG signal is very sensitive to motion artefact which may lead to poor HRV estimation if false peaks are detected. In this Letter, the authors propose a probabilistic approach based on Bayesian learning to better estimate HRV from PPG signal recorded by wearable devices and enhance the performance of the automatic multi scale-based peak detection (AMPD) algorithm used for peak detection. The authors' experiments show that their approach enhances the performance of the AMPD algorithm in terms of number of HRV related metrics such as sensitivity, positive predictive value, and average temporal resolution.