1. A method of developing classification models for classifying one or more physical conditions of a subject, the method comprises: receiving, from a sensor equipment configured to monitor the subject, raw signal data indicative of a physical state of the subject; applying, in a signal processor, a windowing filter to the raw signal data to create a plurality of windows of the raw signal data; applying, in the signal processor, a pre-processing to each of the plurality of windows of the raw signal data to filter each window by removing spurious noise induced on the raw signal data by the sensor equipment; performing, in the signal processor, a signal decomposition on each window, where the signal decomposition comprises applying a Taut-string transformation to each window to produce Taut-string transformed signal data for each window; performing, in the signal processor, a feature extraction on the Taut-string transformed signal data for each window, wherein the feature extraction is configured to identify one or more features for the raw signal data; and based on the one or more features, developing at least one classification model for classifying the one or more physical conditions of the subject.