Abstract: Techniques for determining heart rate variability (HRV) and subject data polar images are described. The techniques include determining data of a subject over a period of time that includes time segments and generating HRV feature values from the HRV data. Each one of the time segments is associated with at least one of the HRV feature values. The techniques also include generating, based on the HRV feature values and the time segments, a polar representation of the HRV data and outputting the polar representation as an image. The techniques further include inputting subject data (e.g., demographic data and/or clinical data of a subject) and using them to generate a filled-in color coded image. The generated image can be used further in a deep learning model to predict a heart failure category and decide on time segments or regions within the image that contribute toward the decision.

Abstract: Examples of the disclosure provide for calibrating a virtual reality environment based on data input in response to initial calibration prompts to provide a customized detection phase for a behavior analysis session. User interaction data are received during the customized detection phase and is dynamically pushed through a trained machine learning component to generate a dynamic behavior vector for the behavior analysis session, the dynamic behavior vector updating during the customized detection phase. The virtual reality environment is dynamically modified during the customized detection phase using the dynamic behavior vector.

Abstract: Examples of the disclosure provide for calibrating a virtual reality environment based on data input in response to initial calibration prompts to provide a customized detection phase for a behavior analysis session. User interaction data are received during the customized detection phase and is dynamically pushed through a trained machine learning component to generate a dynamic behavior vector for the behavior analysis session, the dynamic behavior vector updating during the customized detection phase. The virtual reality environment is dynamically modified during the customized detection phase using the dynamic behavior vector.