Abstract: The present disclosure provides a wearable device, which includes: a frame adapted to be worn on a body part of a user. The frame includes: one or more sensors configured in a housing and operative to sense one or more parameters associated with the body of the user to generate one or more signals indicative of the one or more sensed parameters when the wearable device is worn; a processor configured in the housing and operatively coupled with the one or more sensors, the processor operative to analyze the one or more signals to extract one or more attributes associated with the user from the one or more signals, wherein the one or more attributes being indicative of emotional states of the user; and a display device configured at a portion of the housing and operatively coupled with the processor, wherein the display device is configured to indicate, based on the extracted one or more attributes of the user, the emotional states of the user.

Abstract: Various embodiments of the present disclosure provide a system and method for real-time estimation and changing emotional state of a user. The system comprises a memory configured to store executable instructions and a processor configured to execute the executable instructions stored in the memory, the processor configured to: collect bio physiological data of the user from a smart wearable device of the user, validate the bio physiological data, extract features based on the validated bio physiological data, and train a machine learning model using the extracted features for the real-time estimation of emotional state of the user, and provide personalized recommendations for changing the emotional state of the user.

Abstract: Systems and methods for determining cross-talk coefficients in curves, such as sigmoid-type or growth curves, and PCR curves and nucleic acid melting curves in particular, as well as for applying the cross-talk coefficients to produce cross-talk corrected data sets using a linear subtractive model. Cross-talk signal coefficients are determined using cross-talk data acquired across the entire signal acquisition range. Analyzing across all of the signal curve data provides for a more robust cross-talk correction across the entire data acquisition range. A linear subtractive model is used to correct data sets having cross-talk components.

Abstract: Systems and methods for determining whether the data for a growth curve represents or exhibits valid or significant growth. A data set representing a sigmoid or growth-type curve, such as a PCR curve, is processed to determine whether the data exhibits significant or valid growth. A first or a second degree polynomial curve that fits the data is determined, and a statistical significance value for the curve fit is determined. If the significance value exceeds a significance threshold, the data is considered to not represent significant or valid growth. If the data does not represent significant or valid growth, the data set may be discarded. If the significance value does not exceed the significance threshold, the data is considered to represent significant or valid growth.

Abstract: Systems and methods for determining cross-talk coefficients in curves, such as sigmoid-type or growth curves, and PCR curves and nucleic acid melting curves in particular, as well as for applying the cross-talk coefficients to produce cross-talk corrected data sets using a linear subtractive model. Cross-talk signal coefficients are determined using cross-talk data acquired across the entire signal acquisition range. Analyzing across all of the signal curve data provides for a more robust cross-talk correction across the entire data acquisition range. A linear subtractive model is used to correct data sets having cross-talk components.