Abstract: The present disclosure provides a method for assessing cardiorespiratory health.

Abstract: A system and a method for efficiently identifying a subject is provided. The invention provides for segmenting micro-voltage digital signals into intervals of a pre-defined time period. Further, the invention provides for transforming the segmented micro-voltage digital signals into a frequency domain for computing on a Mel's scale. The Mel's scale provides a unique signature of the subject in the form of a Melspectrogram image. Lastly, the invention provides for passing the Melspectrogram image through a trained deep learning model. The features associated with the Melspectrogram image are extracted into a feature map for obtaining predicted labels associated with the subject based on labels used during training of the deep learning model for identifying the subject.

Abstract: A system and method for creating and mapping vibrations for micro-vibration assessment is described. The system has one or more vibration creators configured to generate one or more vibration signals with at least one of predetermined patterns and intensities in a medium. The one or more sensors are configured to detect and capture one or more vibration signals propagated in the medium. Furthermore, a vibration mapping unit is configured to analyse the captured vibration signals to generate a vibration map. Thereafter, an artificial intelligence processing unit is configured to isolate and identify one or more of individual signals, signal patterns, and signatures of the captured vibrations signals by using the vibration map communicated by the vibration mapping unit, for providing an analysis of the micro-vibrations to a user device.

Abstract: System and Method for Efficiently Identifying a Subject A system and a method for efficiently identifying a subject is provided. The invention provides for segmenting micro-voltage digital signals into intervals of a pre-defined time period. Further, the invention provides for transforming the segmented micro-voltage digital signals into a frequency domain for computing on a Mel's scale. The Mel's scale provides a unique signature of the subject in the form of a Melspectrogram image. Lastly, the invention provides for passing the Melspectrogram image through a trained deep learning model. The features associated with the Melspectrogram image are extracted into a feature map for obtaining predicted labels associated with the subject based on labels used during training of the deep learning model for identifying the subject.

Abstract: A system and method for myocardial performance determination is provided. The present invention provided for generating a first dataset representing a set of events associated with a pre-defined parameter of a biomarker extracted from physiological parameters of a subject. The set of events is determined by processing the pre-defined parameter at a first level and a second level of a multi-level artificial neural network architecture recursively for a pre-defined number of times. Further, generating second dataset representing characteristics associated with the set of events by processing first dataset at third level and fourth level of multi-level artificial neural network architecture. Further, computing set of values associated with set of events by processing second dataset at fifth level of multi-level artificial neural network architecture. Further, computing myocardial performance index based on set of values. The myocardial performance index is representative of myocardial performance of the subject.

Abstract: System and method for determining breathing rate as biofeedback is provided. First biomarker, second biomarker, third biomarker and fourth biomarker is extracted by computation engine from physiological parameters associated with subject by applying pre-defined set of rules. A first value is computed by feedback unit as a function of second biomarker, third biomarker and fourth biomarker. A correlation between first value and time domain parameter of fourth biomarker and frequency domain parameter of fourth biomarker is determined. First value indicates stress level of subject. Second value is computed by maximizing time domain parameter of fourth biomarker and minimizing frequency domain parameter of fourth biomarker based on correlation. Second value indicates reduced stress level of subject. Biofeedback is transmitted by feedback unit to cue generation unit which represents quantified data determined based on second value. The quantified data is indicative of modified second biomarker.