Tata Consultancy Services Limited

Ramesh Kumar Ramakrishnan

Arpan Pal

Rahul Dasharath Gavas

Mithun Basaralu Sheshachala

Balamuralidhar Purushothaman

Aniruddha Sinha

Soma Bandyopadhyay

Dibyanshu Jaiswal

Debatri Chatterjee

Venkata Subramanian Viraraghavan

Kartik Muralidharan

Achanna Anil Kumar

Shreyasi Datta

Girish Mariswamy Chandra

Kayapanda Muthana Mandana

Srinivasan Jayaraman

Tanushree Banerjee

Sachin Patel

Gauri Deshpande

Anish Datta

Midhun Parakkal Unni

Tanushree Bannerjee

Shakil Ahmad

Priya Singh

Tince Varghese

Rolif Lima

Somnath Karmakar

Balakumar Kanagasabapathy

Loknath Tiwari

Tanmay Acharia

Rajesh Ranjan

Ratna Ghosh

Madhurima Moulick

Mithun Basaraku Sheshachala

Lalit Kumar Hissaria

Midhun P Unni

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.

Method and system for predicting distance of gazed objects using infrared (IR) camera

This disclosure relates generally to a method and system for classification of cognitive load (CL) using data obtained from wearable sensors. The disclosed method uses a multi-modal based approach using wrist-worn sensors for real time monitoring of CL in real world scenarios and improves the accuracy of detection of CL. A set of distinguishing features are selected from physiological signals received from the wrist-worn sensors. These features are used for training a classification model for classifying the CL of a patient into a no load or a high load. The set of distinguishing features are selected from domain specific features and signal property based generic features of the physiological signals. The disclosed method is used for classification of CL in scenarios such as to check how the cognitive load of a candidate varies during interviews, to assess the participants workload during online meetings and so on.

Method and system for classification of cognitive load using data obtained from wearable sensors

The present invention relates to a method and system for early detection of COVID-19. Existing methods require data from multiple sensors for training a prediction model whose output is considered as final prediction which is actually the prediction for a particular day or time instance. However, this prediction doesn't detect actual infection of COVID-19 since it requires monitoring the change in health of the user over consecutive days. Embodiments of present disclosure overcome these challenges by a prediction model for COVID-19 which requires only data from Photoplethysmography (PPG) sensor seamlessly collected from a wearable device still able to provide accurate COVID-19 prediction with application of a post processing technique on the predictions of the prediction model. Since COVID-19 symptoms have an effect on heartrate and oxygen saturation which are effectively captured by PPG sensor data, studying these dynamics during infection period gives insights to perform early detection of COVID-19.

Method and system for early detection of COVID-19

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.

Selection of speech segments for training classifiers for detecting emotional valence from input speech signals

This disclosure relates generally to a method and system for assessment of sustained visual attention of a target. The conventional methods utilize various markers for assessment of attention, however, blink rate variability (BRV) series signal is not explored yet. In an embodiment, the disclosed method utilizes BRV series signal for assessing sustained visual attention of a target. A gaze data of the target is recorded using an eye tracker and a set of uniformly sampled BRV series signal is reconstructed from each of the BRV series. One or more frequency domain features, including pareto frequency, are extracted from the set of uniformly sampled BRV series signal. The values of frequency domain features extracted from the set of BRV series signals are compared with corresponding threshold values to determine visual sustained attention of the target.

System and method for eye-blink based assessment of sustained visual attention of a target

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.

Method and system for assessment of cognitive load from bio-potentials measured using wearable endosomatic device

Embodiments herein provide a method and system for continuously validating a user during an established authenticated session using Photoplethysmogram (PPG) and accelerometer data. State of the art approaches are mostly based on feature extraction and ML modelling for PPG based continuous session validation, while a template based approach in the art follows a complicated approach. The method disclosed herein utilizes less computation intensive template based approach to continuously validate the user across the session. The method comprises preprocessing a PPG data or PPG signal acquired from a wearable device worn by the user to identify segments of negligible motion. A first segment, after authentication using conventional authentication mechanism, serves as the initial reference. The chosen segments are then tested one by one with respect to the reference. If the templates in a segment match those of the reference, it is updated as the new reference, else a re-authentication is triggered.

Continuously validating a user during an established authenticated session using photoplethysmogram and accelerometer data

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.

Method and device for continuous blood pressure monitoring and estimation

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Bengaluru, India

Ramesh Kumar Ramakrishnan