Summary Photoplethysmography (PPG) is the optical sensing technology behind almost every wearable vital-sign sensor, but its light-absorption principle depends on adequate pulsatile blood volume at the measurement site. At anatomical locations with characteristically low peripheral perfusion ‘most notably the toes and the sole of the foot’ PPG amplitude is well known to become weak and unreliable, a long-standing obstacle to continuous monitoring at sites that matter most for diabetic-foot and peripheral-vascular care. Praxa Sense’s ALIS® (Absorption & Laser Interference Sensor) adds a second physiological dimension to conventional PPG: blood-flow dynamics. Using speckle plethysmography (SPG) ‘in which a miniature coherent laser and image sensor capture dynamic speckle patterns generated by blood flow beneath the skin’ ALIS® simultaneously extracts a PPG-equivalent blood-volume signal and a Blood Flow Index (BFi) from a single optical measurement. In an exploratory feasibility study, an ALIS® prototype was positioned beneath the ball of the foot in an approximately 1-inch opening in a modified weighing scale. The sensor was supported within the opening by a simple 3D-printed spring suspension, allowing measurements to be taken from three healthy volunteers … Lees meer
Novel ALIS™ Blood Flow index for improved health monitoring
Summary Non-invasive technologies in wearable health devices have become increasingly important due to the rising demand for remote health monitoring (RHM). Among these, photoplethysmography (PPG) is a widely adopted method that measures light absorption, mainly reflecting fluctuations in peripheral blood volume. Praxa Sense has developed a novel sensor, ALIS™, that integrates the measurement of both light absorption and scattering. Alongside the traditional PPG signal, it provides an additional Blood Flow index (BFi). Establishing the relationship between flow velocity and the BFi signal is key for understanding and advancing this novel scattering-based technique. Controlled in-vitro experiments demonstrate a predictable alignment between the BFi signal and physiologically representative flow velocities, with an average mean percentage error (MPE) of −0.80±6.73%. By combining BFi and PPG signals, ALIS™ offers a more comprehensive assessment of peripheral blood circulation, establishing a foundation for advanced algorithm development in RHM.