Tata Consultancy Services Limited

Dibyanshu Jaiswal

Arpan Pal

Avik Ghose

Ramesh Kumar Ramakrishnan

Debatri Chatterjee

Kartik Muralidharan

Shalini Mukhopadhyay

Tapas Chakravarty

Arijit Chowdhury

Rahul Dasharath Gavas

Mithun Basaralu Sheshachala

Dhaval Satish Jani

Balamuralidhar Purushothaman

Arijit Sinharay

Sanjay Madhukar Kimbahune

Vivek Chandel

Andrew Gigie

Sundeep Khandelwal

Sujit Shinde

Kayapanda Muthana Mandana

Nasimuddin Ahmed

Tanushree Banerjee

Ramu Reddy Vempada

Venkata Subramanian Viraraghavan

Swapna Agarwal

Archan Misra

Tanushree Bannerjee

Madhurima Moulick

Ratna Ghosh

Rajesh Ranjan

Tanmay Acharia

Loknath Tiwari

Balakumar Kanagasabapathy

Shakil Ahmad

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

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 real time ROI detection in thermal face images based on a heuristic approach. The ROI of the thermal images, once detected, is then further used to detect temperature of a subject corresponding to the ROI. Unlike state of the art techniques, the heuristic approach is computationally less intensive and provides fast and accurate ROI detection even in case of occluded faces in a crowd with a single thermal image having a plurality of subject being scanned. The heuristics applied does not focus on face detection but directly on point of interest detection. Once the point of interest (ROI) is detected, it may be used for plurality of applications such as subject tracking and the like, not limited to subject or object temperature sensing since the method disclosed herein is easily implementable on low power devices.

Real time region of interest (ROI) detection in thermal face images based on heuristic approach

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

This disclosure relates generally to assessment of cognitive workload using breathing pattern of a person, where cognitive workload is the amount of mental effort required while doing a task. The method and system provides assessment of cognitive workload based on breathing pattern extracted from photoplethysmograph (PPG) signal, which is collected from the person using a wearable device. The PPG signal collected using the wearable device are processed in multiple stages that include breathing signal extraction to extract breathing pattern. The extracted breathing pattern is used for assessment of cognitive workload using a generated personalized training model, wherein the personalized training model is generated and dynamically updated for each person based on selection of a sub-set of breathing pattern features using feature selection and classification techniques that include maximal information coefficient (MIC) techniques. Finally based on personalized training model, the extracted breathing pattern is classified as high cognitive workload or low cognitive workload.

Method and system for assessment of cognitive workload using breathing pattern of a person

A system and method for extracting breathing patterns from PPG signals are provided. The method includes designing a filter for extracting breathing patterns from PPG signals. Designing the filter includes defining filter specifications for extraction of breathing pattern from the PPG signals. Herein, the filter specifications includes a type, an order and a cut-off frequency of the filter. Designing the filter further includes generating a transfer function associated with the filter specifications, and computing a plurality of filter coefficients using filtfilt function for allowing filtering of the PPG signals. Using the filter comprising the plurality of filter coefficients, a filtered PPG signal is generated by removing DC component from PPG signals obtained from a wearable device being worn by a subject. The filtered PPG signal is indicative of the breathing pattern of the subject.

System and method for breathing pattern extraction from PPG signals

This disclosure relates generally to classification of cardiopulmonary fatigue. The method and system provides a longitudinal monitoring platform to classify cardiopulmonary fatigue of a subject using a wearable device worn by the subject. The activities of the subject is continuous monitored by plurality of sensors embedded in a wearable device. The received sensor signals are processed in multiple stages to classify cardiopulmonary fatigue as healthy or unhealthy based on respiratory, heart rate and recovery duration parameters extracted from the received sensor data. Further using the classified cardiopulmonary fatigue level, the C2P also performs longitudinal analysis to detect potential cardiopulmonary disorders.

System and method to classify cardiopulmonary fatigue

Techniques for heart rate estimation are disclosed. In an embodiment, synchronized photoplethysmograph (PPG) and 3-axis acceleration signals are received. Further, the PPG and acceleration signals are partitioned into windows. Furthermore, it is determined whether motion is present in a window of the acceleration signal. Moreover, Fourier transform is performed on the signals to obtain power spectra of the signals in the window when there is motion. Also, it is determined whether a peak of the acceleration signal is present in a range around first highest PPG peak. Further, it is determined whether the peak of the acceleration signal affects heart rate of the user when the peak of the acceleration signal is in the range around the highest PPG peak. The heart rate of the user in the window is then estimated using second highest PPG peak when the peak of the acceleration signal affects heart rate of the user.

System and method for heart rate estimation

Energy remains a critical challenge for continuous sensing: with low-capacity batteries, wearable devices require frequent charging. In contrast, installing sensors in everyday 'smart objects', such as kitchen cabinets, household appliances and office equipment, supports ADL detection via indirect observations on human interaction with such objects, but cannot provide individual-specific insights in multi-tenanted environments. The embodiments herein provide a method and system for energy efficient activity recognition and behavior analysis. Architecture disclosed utilizes a hybrid mode of inexpensive, battery-free sensing of physical activities performed by a subject been monitored during his Activities for Daily Living (ADLs). The sensing combines object interaction sensing with person-specific wearable sensing to recognize individual activities in smart spaces. The method and system disclosed quantifies a probabilistic approach that uses longitudinal observations of user-item interactions, over each individual episode, to compute the anomalous behavior of the subject.

Method and system for activity recognition and behaviour analysis

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Kolkata, India

Dibyanshu Jaiswal