Recent progress in printed optoelectronics and their integration in wearable sensors have created new avenues for research in reflectance photoplethysmography (PPG) and oximetry. The reflectionmode sensor, which consists of light emitters and detectors, is a vital component of reflectance oximeters. Here, we report a systematic study of the reflectance oximeter sensor design in terms of component geometry, light emitter and detector spacing, and the use of an optical barrier between the emitter and the detector to maximize sensor performance. Printed red and near-infrared (NIR) organic light-emitting diodes (OLEDs) and organic photodiodes (OPDs) are used to design three sensor geometries: (1) Rectangular geometry, where square OLEDs are placed at each side of the OPD; (2) Bracket geometry, where the OLEDs are shaped as brackets and placed around the square OPD; (3) Circular geometry, where the OLEDs are shaped as block arcs and placed around the circular OPD. Utilizing the bracket geometry, we observe 39.7% and 18.2% improvement in PPG signal magnitude in the red and NIR channels compared to the rectangular geometry, respectively. Using the circular geometry, we observe 48.6% and 9.2% improvements in the red and NIR channels compared to the rectangular geometry. Furthermore, a wearable two-channel PPG sensor is utilized to add redundancy to the measurement. Finally, inverse-variance weighting and template matching algorithms are implemented to improve the detection of heart rate from the multi-channel PPG signals.INDEX TERMS Reflection photoplethysmography sensor, organic optoelectronics, pulse oximetry, wearable sensors, printed electronics, flexible electronics.
Human skin perceives external mechanical stimuli by sensing the variation in the membrane potential of skin sensory cells. Many scientists have attempted to recreate skin functions and develop electronic skins (e-skins) based on active and passive sensing mechanisms. Inspired by the skin sensory behavior, we investigated materials and electronic devices that allow us to encode mechanical stimuli into potential differences measured between two electrodes, resulting in a potentiometric mechanotransduction mechanism. We present here a potentiometric mechanotransducer that is fabricated through an all-solution processing approach. This mechanotransducer shows ultralow-power consumption, highly tunable sensing behavior, and capability to detect both static and low-frequency dynamic mechanical stimuli. Furthermore, we developed two novel classes of sensing devices, including strain-insensitive sensors and single-electrode-mode e-skins, which are challenging to achieve using the existing methods. This mechanotransduction mechanism has broad impact on robotics, prosthetics, and health care by providing a much improved human-machine interface.
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