A sensor device, comprising: one or more sensors configured to: sense physiological changes associated with cardiovascular function and provide outputs corresponding to the sensed physiological changes, and detect (404, 406, 408) an electrocardiograph, ECG signal, a photoplethysmograph, PPG, signal, and a phonocardiograph, PCG, signal of a patient from the outputs provided by the one or more sensors; and a memory storing a program, in response to execution, causes a processor to perform operations comprising: filtering the detected ECG, PPG, and PCG signals by detecting and rejecting artifacts from the detected ECG, PPG, and PCG signals, the artifacts including at least one presence of: arrhythmia, motion, anomalies, and outliers to produce filtered ECG, PPG, and PCG signals; and extracting multivariate signal features including magnitudes and cardiac timing intervals from the filtered ECG, PPG, and PCG signals; wherein the sensor device is characterized in that the program, in response to execution, causes the processor to perform operations comprising: averaging time series of the multivariate signal features over a predetermined time window; creating a feature vector with the averaged signal features and incorporated with subject-specific information; processing the feature vector to determine measurements of Noninvasive Blood Pressure, NIBP, including systolic blood pressure, SBP, diastolic blood pressure, DBP, and mean blood pressure, MBP, values, using an application including an independent Blood Pressure, BP, prediction model, wherein the independent BP prediction model is derived by machine-learning in large group individuals including demographics and disease conditions, wherein with the independent BP prediction model, the SBP, DBP, and MBP values are mapped according to the feature vector; and displaying or continuously logging the SBP, DBP, and MBP values. one or more sensors configured to: sense physiological changes associated with cardiovascular function and provide outputs corresponding to the sensed physiological changes, and detect (404, 406, 408) an electrocardiograph, ECG signal, a photoplethysmograph, PPG, signal, and a phonocardiograph, PCG, signal of a patient from the outputs provided by the one or more sensors; and sense physiological changes associated with cardiovascular function and provide outputs corresponding to the sensed physiological changes, and detect (404, 406, 408) an electrocardiograph, ECG signal, a photoplethysmograph, PPG, signal, and a phonocardiograph, PCG, signal of a patient from the outputs provided by the one or more sensors; and a memory storing a program, in response to execution, causes a processor to perform operations comprising: filtering the detected ECG, PPG, and PCG signals by detecting and rejecting artifacts from the detected ECG, PPG, and PCG signals, the artifacts including at least one presence of: arrhythmia, motion, anomalies, and outliers to produce filtered ECG, PPG, and PCG signals; and extracting multivariate signal features including magnitudes and cardiac timing intervals from the filtered ECG, PPG, and PCG signals; filtering the detected ECG, PPG, and PCG signals by detecting and rejecting artifacts from the detected ECG, PPG, and PCG signals, the artifacts including at least one presence of: arrhythmia, motion, anomalies, and outliers to produce filtered ECG, PPG, and PCG signals; and extracting multivariate signal features including magnitudes and cardiac timing intervals from the filtered ECG, PPG, and PCG signals; wherein the sensor device is characterized in that the program, in response to execution, causes the processor to perform operations comprising: averaging time series of the multivariate signal features over a predetermined time window; creating a feature vector with the averaged signal features and incorporated with subject-specific information; processing the feature vector to determine measurements of Noninvasive Blood Pressure, NIBP, including systolic blood pressure, SBP, diastolic blood pressure, DBP, and mean blood pressure, MBP, values, using an application including an independent Blood Pressure, BP, prediction model, wherein the independent BP prediction model is derived by machine-learning in large group individuals including demographics and disease conditions, wherein with the independent BP prediction model, the SBP, DBP, and MBP values are mapped according to the feature vector; and displaying or continuously logging the SBP, DBP, and MBP values. averaging time series of the multivariate signal features over a predetermined time window; creating a feature vector with the averaged signal features and incorporated with subject-specific information; processing the feature vector to determine measurements of Noninvasive Blood Pressure, NIBP, including systolic blood pressure, SBP, diastolic blood pressure, DBP, and mean blood pressure, MBP, values, using an application including an independent Blood Pressure, BP, prediction model, wherein the independent BP prediction model is derived by machine-learning in large group individuals including demographics and disease conditions, wherein with the independent BP prediction model, the SBP, DBP, and MBP values are mapped according to the feature vector; and displaying or continuously logging the SBP, DBP, and MBP values.