Method and system for body temperature estimation using a wearable biosensor

A non-transitory computer-readable medium, associated with a wearable sensor to determine patient body temperature, storing instructions that, when executed, cause one or more processors to perform operations comprising:

• measuring at least one of ambient, instantaneous, or patient condition sensor data;
• extracting from the at least one of ambient, instantaneous, or patient condition sensor data, two classes of parameters including:
• instantaneous parameters that are entities that are derived based on the sensor data, and
• contextual parameters that are time series of the instantaneous parameters;
• processing the contextual parameters and instantaneous parameters by utilizing a plurality of trained models by a plurality of estimators to determine a plurality of body temperature estimates, respectively;
• implementing bias and trend correction procedures to correct the plurality of body temperature estimates by improving trend matching and accuracy of the plurality of body temperature estimates;
• determining a confidence level of each of the plurality of body temperature estimates based on meta-features of a feature set that each of the plurality of estimators uses;
• and
• determining a final body temperature based on a combination of the plurality of body temperature estimates weighted based on the confidence level of each of the body temperature estimates.

A wearable sensor including a memory storing instructions to determine patient body temperature, and that, when executed, cause one or more processors to perform operations comprising:

• measuring at least one of ambient, instantaneous, or patient condition sensor data;
• extracting from the at least one of ambient, instantaneous, or patient condition sensor data, two classes of parameters including:
• instantaneous parameters that are entities that are derived based on the sensor data, and
• contextual parameters that are time series of the instantaneous parameters determined by smoothing or filtering with different kernels including at least one of:
• low pass filtering, band pass filtering, high pass filtering, or wavelets to determine short-term dynamics of the instantaneous parameters, and wherein as increased sensor data is accumulated, the smoothing is performed over longer time frames than the smoothing for the short-term dynamics to determine long-term dynamics of the instantaneous parameters;
• processing the contextual parameters and instantaneous parameters by utilizing a plurality of trained models by a plurality of estimators to determine a plurality of body temperature estimates, respectively;
• implementing bias and trend correction procedures to correct the plurality of body temperature estimates by improving trend matching and accuracy of the plurality of body temperature estimates;
• determining a confidence level of each of the plurality of body temperature estimates based on meta-features of a feature set that each of the plurality of estimators uses;
• and
• determining a final body temperature based on a combination of the plurality of body temperature estimates weighted based on the confidence level of each of the body temperature estimates.