公开(公告)号：US20200000360A1

公开(公告)日：2020-01-02

申请号：US16190800

申请日：2018-11-14

Applicant: Tata Consultancy Services Limited

Inventor： Tanuka BHATTACHARJEE ,  Shreyasi DATTA ,  Deepan DAS ,  Anirban DUTTA CHOUDHURY ,  Arpan PAL ,  Prasanta Kumar GHOSH

IPC: A61B5/0456 ,  G16H40/63 ,  A61B5/04 ,  A61B5/16 ,  A61B5/024

Abstract: Traditionally arousal classification has been broadly done in multiple classes but have been insufficient to provide information about how arousal level of user changes over time. Present disclosure propose a continuous and unsupervised approach of monitoring the arousal trend of individual from his/her heart rate by obtaining instantaneous HR for time windows from a resampled time series of RR intervals obtained from ECG signal. A measured average heart rate (a measured HR) is computed from instantaneous HR specific to user for each time window thereby estimating apriori state based on a last instance of an aposteriori state initialized and observation of a state space model of Kalman Filter is determined for computing error and normalizing thereof which gets compared with a threshold for continuous monitoring of arousal trend of the user. The aposterior state is further updated using Kalman gain computed based on measurement noise determined for state space model.

公开(公告)号：US20200012665A1

公开(公告)日：2020-01-09

申请号：US16506505

申请日：2019-07-09

Applicant: Tata Consultancy Services Limited

Inventor： Deepan DAS ,  Shreyasi DATTA ,  Tanuka BHATTACHARJEE ,  Anirban DUTTA CHOUDHURY ,  Arpan PAL

IPC: G06F16/28 ,  G16H15/00

Abstract: A method and system for clustering users using cognitive stress report for classifying stress levels is provided. Detection and monitoring of cognitive stress experienced by users while performing a task is very crucial. The method includes receiving, user evaluated cognitive stress reports and the physiological signals of the user during the performance of the task. A normalized cognitive report is generated from the user evaluated cognitive stress report by computing mode and range value. The normalized cognitive stress reports of the users are used to cluster the users into a primary cluster and a secondary cluster. Feature sets are extracted from the physiological signals of the said users associated with the primary cluster. Using the said feature sets a classifier model is trained to classify the cognitive stress levels of the users as stressful class or stressless class.

公开(公告)号：US20200093425A1

公开(公告)日：2020-03-26

申请号：US16578270

申请日：2019-09-20

Applicant: Tata Consultancy Services Limited

Inventor： Tanuka BHATTACHARJEE ,  Deepan DAS ,  Shahnawaz ALAM ,  Rohan BANERJEE ,  Anirban DUTTA CHOUDHURY ,  Arpan PAL ,  Achuth RAO MELAVARIGE VENKATAGIRI ,  Prasanta Kumar GHOSH ,  Ayush Ranjan LOHANI

IPC: A61B5/00 ,  G06K9/46 ,  G06K9/62 ,  G06K9/00

Abstract: Monitoring the quality of sleep of an individual is essential for ensuring one's overall well-being. The existing methods for non-apnea sleep arousal detection are manual. A system and method for the non-apnea sleep arousal detection has been provided. The method uses a feature engineering based binary classification approach for distinguishing non-apnea arousal and non-arousal. A training data set is prepared using a plurality of physiological signals. A plurality of features are derived from the training data set. Out of those only a set of features are selected for training a plurality of random forest classifier models. A test sample is then provided to the plurality of random forest classifier models in the instances of fixed duration. This results in generation of prediction probabilities for each instances. The prediction probabilities are then used to predict the probabilities of non-apnea sleep arousal in the test sample.

公开(公告)号：US20190175091A1

公开(公告)日：2019-06-13

申请号：US15913731

申请日：2018-03-06

Applicant: Tata Consultancy Services Limited

Inventor： Deepan DAS ,  Tanuka BHATTACHARJEE ,  Shreyasi DATTA ,  Anirban Dutta CHOUDHURY ,  Pratyusha DAS ,  Arpan PAL

IPC: A61B5/16 ,  A61B5/0476 ,  A61B5/024 ,  A61B5/053 ,  A61B5/00 ,  A61B5/11 ,  G06N5/02 ,  G06N99/00 ,  G16H50/20

Abstract: This disclosure relates generally to stress classification and quantification, and more particularly to system and method for classification and quantitative estimation of cognitive stress from analysis of keystrokes and signals derived from physiological sensors. In one embodiment, a method includes obtaining, while a user is engaged in performance of a task, physiological signals from physiological sensors accessible to the user. Feature sets are identified from the physiological signals which correlate with cognitive stress experienced by the user. Using a regression model, a stress indicator metric comprising a quantitative estimate of the cognitive stress is predicted. The regression model is trained using the feature sets and independently determined quantitative estimates of cognitive stress used as a ground truth to output the value of the stress indicator metric. The ground truth is determined from keystroke data associated with the performance of keyboard-based tasks comprising navigation of moving objects to the target.