Tata Consultancy Services Limited

Tanushree Banerjee

Arijit Chowdhury

Balamuralidhar Purushothaman

Tapas Chakravarty

Debatri Chatterjee

Arpan Pal

Rohan Banerjee

Anirban Dutta Choudhury

Anwesha Khasnobish

Rahul Dasharath Gavas

Shreyasi Datta

Dibyanshu Jaiswal

Kayapanda Muthana Mandana

Ramesh Kumar Ramakrishnan

Mithun Basaraku Sheshachala

Tapas Chakravar

Recognizing mental states from physiological signal is a concern not only for medical diagnostics, but also for cognitive science, behavioral studies as well as brain machine interfaces. Embodiments of the present disclosure utilize respiration signals to decipher mental states wherein non-linear baseline drifts in signal is implemented to extract the respiratory features in most effective way. Presence of class imbalance, is effectively rectified using Synthetic Minority Oversampling Technique (SMOTE) to resolve class imbalance problem, which not only increased the classification accuracy, but also reduced classifier bias towards the majority class, which in turn exceedingly enhanced the classifier sensitivity.

Stress level monitoring of users using a respiratory signal and alerting thereof

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 estimating blood pressure (BP) using photoplethysmogram (PPG) has been explained. The PPG is captured from a PPG sensor (). For preparing training model, a pulse oximeter is used for capturing PPG. For testing, a smartphone camera is used for capturing the PPG signal. A plurality of features are extracted from the preprocessed PPG signal. A BP distribution is then generated using the plurality of features and the training model. The BP distribution is part of a set of BP distributions generated from different subjects. Finally, a post-processing methodology have been used to reject inconsistent data out of the set of BP distributions and BP value is estimated only for the remaining BP distributions and a statistical average is provided as the blood pressure estimate.

Non-invasive method and system for estimating blood pressure from photoplethysmogram using statistical post-processing

A method and system is provided for vehicle speed profile generation. The method is performed by receiving data pertaining to driver characteristics and characteristics of trips taken by said driver, creating driver profile by generating skill and aggression parameters for said driver, constructing trip parameters pertaining to said trips taken by the driver by processing the skill and aggression parameters, constructing acceleration dataset for said trips, constructing speed values from the acceleration dataset and processing the speed values for anomalies.

Method and system for vehicle speed profile generation

The disclosure generally relates to vehicle maintenance and more particularly to methods and systems for automatic vehicle maintenance scheduling. The method comprises obtaining acceleration data at a fixed sampling rate of a moving vehicle when the moving vehicle attains at least a pre-determined speed for a pre-determined time from start of the moving vehicle. The obtained acceleration data is processed to generate jerk energy values at predetermined intervals. Speed normalized jerk energy is computed based on an average of the generated jerk energy values and average speed of the moving vehicle. A relationship between journey number and the computed speed normalized jerk energy is computed. A vehicle maintenance indicator based on the computed relationship between the journey number and the computed speed normalized jerk energy is finally computed.

Methods and systems for automatic vehicle maintenance scheduling

This disclosure relates generally to data processing, and more particularly to a system and method for monitoring driving behavior of a driver. In one embodiment, a system () for monitoring driving behavior of a driver is disclosed. The system () may configure a processor () to execute computer-readable instructions () stored in a memory () in order to: capture a plurality of acceleration samples; compute Kurtosis values and Skewness values corresponding to a set of acceleration samples; filter the Kurtosis values; determine a probability distribution function of the filtered Kurtosis values; compute a mean and a standard deviation associated with the filtered Kurtosis values; determine a first threshold for each driver based upon the mean and the standard deviation; compute a first score for each driver based upon the first threshold and the number of trips; determine a second threshold; compute a second score for each driver based upon the second threshold and the number of trips; and evaluate driving behavior of a driver based upon the first score and the second score.

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Kolkata, India

Tanushree Banerjee