Tata Consultancy Services Limited

Ramu Reddy Vempada

Arpan Pal

Aniruddha Sinha

Tanushyam Chattopadhyay

Parijat Dilip Deshpande

Tapas Chakravarty

Avik Ghose

Arijit Chowdhury

Arijit Sinharay

Rohan Banerjee

Anirban Dutta Choudhury

Ranjan Dasgupta

Kingshuk Chakravarty

Dibyanshu Jaiswal

Dibyendu Roy

Kayapanda Muthana Mandana

Nasimuddin Ahmed

Shalini Mukhopadhyay

Guruprasad Seshadri

A method and system for detection of coronary artery disease (CAD) in a person using a fusion approach has been described. The invention the detection of CAD in the person by capturing of a plurality of physiological signals such as phonocardiogram (PCG), photoplethysmograph (PPG), ECG, galvanic skin response (GSR) etc. from the person. A plurality of features are extracted from the physiological signals. The person is then classified as CAD or normal using the each of the features independently. The classification is done based on supervised machine learning technique. The output of the classification is then fused and used for the detection of the CAD in the person using a predefined criteria.

Method and system for detection of coronary artery disease in a person using a fusion approach

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

A system and method for identifying an unknown person based on a static posture of the unknown person is described. The method includes receiving data of N skeleton joints of the unknown person from a skeleton recording device. The method further includes identifying the static posture of the unknown person. The method includes dividing a skeleton structure of the unknown person in a plurality of body parts based on joint types of the skeleton structure. In addition, the method includes extracting feature vectors for each of the joint type from each of the plurality of body parts. The method further includes identifying the unknown person based on comparison of the feature vectors for the unknown person with one of a constrained feature dataset and an unconstrained feature dataset for a plurality of known persons.

Static posture based person identification

System(s) and method(s) for classifying noise data of human crowd are disclosed. Noise data is captured from one or more sources and features are extracted by using computation techniques. The features comprise spectral domain features and time domain features. Classification models are developed by using each of the spectral domain features and the time domain features. Discriminative information with respect to the noise data is extracted by using the classification models. A performance matrix is computed for each of the classification model. The performance matrix comprises classified noise elements with respect to the noise data. Each classified noise element is associated with a classification performance score with respect to a spectral domain feature, a time domain feature, and fusion of features and scores. The classified noise elements provide the classification of the noise data.

System and method to provide classification of noise data of human crowd

An acoustic array system for anomaly detection is provided. The acoustic array system () performs a scan (or a progressive scan of frequencies) of a given volume by transmitting one or more signals, and receives one or more reflected signals from objects within the volume. The reflected signals are then amplified and converted to a set of digital signals. Features of the set of digital signals are extracted both in time and frequency domains. The acoustic array system () further performs a comparison of these set of digital extracted features with the reflected signals via machine learning techniques. Based on the comparison, the acoustic array system detects one or more anomalies.

Anomaly detection system and method

A System and method for identifying one or more human activities in a human-computer interacting environment. Skeleton points associated with a human are received. A data variation factor for the skeleton points is calculated, and a set of skeleton points is selected based on the data variation factor. One or more features are defined from the set of skeleton points by identifying a variance in coordinates of the set of skeleton points by using one or more statistical parameters. The one or more features are used to identify the one or more human activities.

System and method for selecting features for identifying human activities in a human-computer interacting environment

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Kolkata, India

Ramu Reddy Vempada