Summary The increasing demand for remote health monitoring (RHM) has highlighted the importance of reliable sensors in wearable devices. Photoplethysmography (PPG), a widely used technique, measures light absorption to monitor vital signs but faces challenges such as susceptibility to external light fluctuations. 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). Experimental results show that the ALIS™ scattering-based BFi signal is robust to external light fluctuations, enhancing reliability in ambulatory settings. This positions ALIS™ as a powerful tool for advancing RHM. Background As the healthcare system faces increasing pressure, the demand for remote health monitoring (RHM) methods is rising. In recent years, optical technologies have become essential in the field of RHM. One of the most widely used optical methods is photoplethysmography (PPG), a simple, low-cost, non-invasive technique that utilizes a light emitting diode (LED) and photodiode to monitor vital parameters like heart rate and peripheral oxygen saturation. PPGs simple configuration allows a high degree of miniaturization and … 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. Background As the healthcare system faces increasing pressure, the demand for remote health monitoring (RHM) methods is rising. In recent years, optical technologies have become essential in the field of RHM. One of the most widely used optical … Lees meer