Figure 1 - available via license: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
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Overview of the Beam AI SDK. First, the user's pulse wave is extracted by processing subtle color variations across the skin regions of the user's face. The user's pulse is then processed by the SDK's proprietary peak detection algorithm which produces the inter-beat intervals used to determine the user's stress (according to Baevsky Stress Index), heart rate and heart rate variability.
Source publication
Stress is considered to be the epidemic of the 21st-century. Yet, mobile apps cannot directly evaluate the impact of their content and services on user stress. We introduce the Beam AI SDK to address this issue. Using our SDK, apps can monitor user stress through the selfie camera in real-time. Our technology extracts the user's pulse wave by analy...
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Citations
... 33 Mathematical models for the time-varying analysis of both linear and NL HRV parameters have enhanced the ability to assess CAM responses to clinical or real-world stressors, even in real time. [34][35][36][37][38][39] Recently, the software has been developed to automatically compute time-varying parasympathetic (PNSi), sympathetic (SNSi), and Baevsky stress (BSTRi) indexes, enabling faster assessments of CAM, particularly in acute stress situations. 40 Although HRV parameters are well-established, the normal ranges for these new indexes, especially BSTRi, remain unclear, particularly in stressful conditions. ...
Cardiac autonomic modulation (CAM), which is regulated by the balance between the sympathetic and parasympathetic nervous systems, is involved in various physiological and pathological conditions. Heart rate variability (HRV) analysis has been used to explore the complex relationship between the brain and heart, as described by Porges’ polyvagal theory and Thayer’s neurovisceral integration model. Recently, an automated calculation of new parasympathetic, sympathetic, and Baevsky stress indexes based on HRV parameters has been introduced for faster and more comprehensive CAM assessment, though their normal ranges remain undefined. This study aimed to determine the average values of these indexes in a healthy population of different ages during rest, daily activities, non-rapid eye movement sleep, graded physical effort, and acute psychophysiological stress. At rest, the parasympathetic and sympathetic indexes were consistently within the proposed normal range and inversely related. However, Baevsky stress index values from Kubios were higher than expected, conflicting with the assumption that they are simply the square root of those calculated using the original formula. Despite this, time-varying assessment of all indexes can provide valuable insights into CAM adaptation during physical effort and acute psychophysiological stress in real-world critical situations. Notably, our novel finding shows that the inverse correlation between parasympathetic and sympathetic/stress indexes under stress is better explained by non-linear functions, offering a potential new measure of brain–heart interaction during real-life critical events.
