A method for remote animal surveillance and distress monitoring comprising: detecting one or more biometric parameters of the animal, including detecting one or more of a respiratory rate and heart rate of the animal using data from an ultrawide band impulse radar (UWB-IR); detecting one or more behavioral parameters of the animal, comprising monitoring motion data from one or more of an accelerometer, gyroscope, magnetometer, and barometric pressure sensor over a period of time; determining, using a one-class classifier, occurrence of a novel event based on comparison of the motion data to a range of predefined parameter values and qualifications contained in model representing motion data obtained under normal conditions for the animal, wherein determining a novel event comprises: pre-processing the motion data to generate a feature vector for classification, including: filtering the motion data to remove noise; transforming the motion data into the frequency domain to obtain a plurality of frequency coefficients; computing the power of individual frequency bands based on the frequency coefficient values with in the respective frequency band; concatenating the frequency power bands to generate a feature vector; and reducing the dimensionality of the feature vector using a principle component matrix; inputting the reduced feature vector to the one-class classifier; computing a composite value of relative distress of the animal for a combination of detected biometric and behavioral parameters if occurrence of a novel event is determined; determining, at the point of care, whether the composite value exceeds a predefined composite threshold value indicative of possible distress in the animal; and notifying one or more remote caretakers of possible distress in the animal based on the composite value exceeding the predefined composite threshold value. detecting one or more biometric parameters of the animal, including detecting one or more of a respiratory rate and heart rate of the animal using data from an ultrawide band impulse radar (UWB-IR); detecting one or more behavioral parameters of the animal, comprising monitoring motion data from one or more of an accelerometer, gyroscope, magnetometer, and barometric pressure sensor over a period of time; determining, using a one-class classifier, occurrence of a novel event based on comparison of the motion data to a range of predefined parameter values and qualifications contained in model representing motion data obtained under normal conditions for the animal, wherein determining a novel event comprises: pre-processing the motion data to generate a feature vector for classification, including: filtering the motion data to remove noise; transforming the motion data into the frequency domain to obtain a plurality of frequency coefficients; computing the power of individual frequency bands based on the frequency coefficient values with in the respective frequency band; concatenating the frequency power bands to generate a feature vector; and reducing the dimensionality of the feature vector using a principle component matrix; inputting the reduced feature vector to the one-class classifier; pre-processing the motion data to generate a feature vector for classification, including: filtering the motion data to remove noise; transforming the motion data into the frequency domain to obtain a plurality of frequency coefficients; computing the power of individual frequency bands based on the frequency coefficient values with in the respective frequency band; concatenating the frequency power bands to generate a feature vector; and filtering the motion data to remove noise; transforming the motion data into the frequency domain to obtain a plurality of frequency coefficients; computing the power of individual frequency bands based on the frequency coefficient values with in the respective frequency band; concatenating the frequency power bands to generate a feature vector; and reducing the dimensionality of the feature vector using a principle component matrix; inputting the reduced feature vector to the one-class classifier; computing a composite value of relative distress of the animal for a combination of detected biometric and behavioral parameters if occurrence of a novel event is determined; determining, at the point of care, whether the composite value exceeds a predefined composite threshold value indicative of possible distress in the animal; and notifying one or more remote caretakers of possible distress in the animal based on the composite value exceeding the predefined composite threshold value.