Abstract:
Direct usage of endosomatic EDA has multiple challenges for practical cognitive load assessment. Embodiments of the method and system disclosed provide a solution to the technical challenges in the art by directly using the bio-potential signals to implement endosomatic approach for assessment of cognitive load. The method utilizes a multichannel wearable endosomatic device capable of acquiring and combining multiple bio-potentials, which are biomarkers of cognitive load experienced by a subject performing a cognitive task. Further, extracts information for classification of the cognitive load, from the acquired bio-signals using a set of statistical and a set of spectral features. Furthermore, utilizes a feature selection approach to identify a set of optimum features to train a Machine Learning (ML) based task classifier to classify the cognitive load experienced by a subject into high load task and low load task.
Abstract:
An important task in several wellness applications is detection of emotional valence from speech. Two types of features of speech signals are used to detect valence: acoustic features and text features. Acoustic features are derived from short frames of speech, while text features are derived from the text transcription. Present disclosure provides systems and methods that determine the effect of text on acoustic features. Acoustic features of speech segments carrying emotion words are to be treated differently from other segments that do not carry such words. Only specific speech segments of the input speech signal are considered based on a dictionary specific to a language to assess emotional valence. A model trained (or trained classifier) for specific language either by including the acoustic features of the emotion related words or by omitting it is used by the system for determining emotional valence in an input speech signal.
Abstract:
This disclosure relates generally to method and system for predicting distance of gazed objects using IR camera. Eye tracking technology is widely used to study human behavior and patterns in eye movements. Existing gaze trackers focus on predicting gaze point and hardly analyzes distance of the gazed object from the gazer or directly classify region of focus. The method of the present disclosure predicts gazed objects distance using a pair of IR cameras placed on either side of a smart glass. The gaze predictor ML model predicts distance at least one gazed object positioned from eye of each subject during systematic execution of a set of tasks. From each pupillary information of each pupil a set of features are extracted which are utilized to classify the gazed object of the subject based on the distance into at least one of a near class, an intermediate class, and a far class.
Abstract:
The embodiments of the present disclosure herein address unresolved problems of quality of signals in real time for wearables to provide optimal signals which can be used for brain signal based applications. Further, conventional techniques fail to provide real-time calibration of wearable devices, to understand the quality of the signals from the wearable device. Embodiments herein provide a method and system for a real-time calibration of one or more Electroencephalography (EEG) signals received from a wearable Ear-EEG device. The system is leveraging quality of signals in real time for wearables to provide optimal signals which can be used for early detection of neurodegenerative disease and brain-computer interface (BCI) applications. Further, the system is able to detect electrodes in the wearable device where the EEG signals have not been collected because the contact was not established.
Abstract:
Wellness estimation allows tracking of various health parameters and estimating health condition(s) of the user being monitored. Photophlethysmogram (PPG) based health monitoring systems exist. This disclosure relates generally to PPG based wellness monitoring, and more specifically to a pulse harmonics based wellness estimation. The system collects PPG signals from a user being monitored, as input. The system calculates 12 pulse harmonics from a fundamental frequency of the PPG signal. From the pulse harmonics, further a plurality of key features are extracted, and in turn a wellness metric and a wellness index are calculated, which represents health of the user.
Abstract:
A method and device is provided for the continuous estimation of the blood pressure using a noninvasive technique. The method involves sensing of the displacement signal generated by the palpation of the radial artery. The radial artery is modelled as a cylindrical voight type viscoelastic tissue for the estimation of the personalized blood pressure. The model includes the displacement signal and a set of parameters as an input. The set of parameters include a mean radius of the artery, a radius at zero mmHg, a viscoelastic damping parameter, an elasticity of the artery and a thickness of wall of artery. The method involves the optimization of the set of parameters using heuristic optimization techniques, which helps in the estimation of the systolic and diastolic blood pressure. The method and device can also be personalized for individualized monitoring and estimation of the blood pressure of the person.