公开(公告)号：US20240225134A1

公开(公告)日：2024-07-11

申请号：US18542846

申请日：2023-12-18

Applicant: Tata Consultancy Services Limited

Inventor： SHALINI MUKHOPADHYAY ,  VARSHA SHARMA ,  SWARNAVA DEY ,  SAKYAJIT BHATTACHARYA ,  AVIK GHOSE

IPC: A24F47/00 ,  A61B5/00 ,  A61B5/08 ,  A61B5/107 ,  A61B5/11 ,  G16H50/30

CPC classification number: A24F47/00 ,  A61B5/0022 ,  A61B5/0806 ,  A61B5/1073 ,  A61B5/1123 ,  A61B5/6802 ,  A61B5/725 ,  A61B5/7264 ,  A61B5/7275 ,  G16H50/30 ,  A61B2562/0219

Abstract: This disclosure relates generally to method and system for estimating smoking episodes from smoke puffs using a wearable device. Since the expense of treating diseases is rising, a digital smoking cessation improves healthcare systems such as cardiovascular issues. To achieve an optimum model given the platform limitations a very compact model is built specifically for the target microcontroller platform. The method of the present disclosure generates an optimum model for deployment on the wearable device using a pretrained deep neural network (DNN). A set of sensor signals are inputted to a convolutional neural network (CNN) smoke detection model to detect smoke puffs. Gesture classifier determines whether the user of the wearable device is engaged/engaging in a smoking session. Further, the method provides users of the wearable device with a cloud estimated smoking behavior analysis based on a set of smoking episodes to generate a set of user risk scores.

公开(公告)号：US20240321450A1

公开(公告)日：2024-09-26

申请号：US18393358

申请日：2023-12-21

Applicant: Tata Consultancy Services Limited

Inventor： VARSHA SHARMA ,  AVIK GHOSE ,  SUNDEEP KHANDELWAL ,  SAKYAJIT BHATTACHARYA

IPC: G16H50/20 ,  G06F18/2415

CPC classification number: G16H50/20 ,  G06F18/2415

Abstract: Improvement in the accuracy of disease diagnosis associated with cardiac abnormalities is an open research area. Appropriate feature selection to capture the underlying signs of a disease is critical in Machine Learning (ML) based approaches. A method and system for, determining cardiac abnormalities using chaos-based classification model from multi-lead ECG signals, is disclosed. The method combines the commonly used chaos parameter with other set of chaos-related statistical parameters like non-linearity, self-similarity, Chebyshev distance and spectral flatness for a holistic approach to the study of cardiac abnormalities. The method disclosed thus attempts to use above ML based measures for disease classification. The set of chaos-related features used herein contribute to improving the accuracy of detection of various cardiac diseases arising due to cardiac abnormalities such as Atrial Fibrillation (AF) and the like. The improved accuracy in the detection of AF effectively improves the accuracy in percentage of AF burden.

公开(公告)号：US20230404461A1

公开(公告)日：2023-12-21

申请号：US18329855

申请日：2023-06-06

Applicant: Tata Consultancy Services Limited

Inventor： VARSHA SHARMA ,  AYAN MUKHERJEE ,  MURALI PODUVAL ,  SUNDEEP KHANDELWAL ,  ANIRBAN DUTTA CHOUDHURY ,  CHIRAYATA BHATTACHARYYA

IPC: A61B5/346 ,  A61B5/00 ,  G06T1/00

CPC classification number: A61B5/346 ,  A61B5/7267 ,  G06T1/00 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: State of art techniques hardly provide data balancing for multi-label multi-class data. Embodiments of the present disclosure provide a method and system for identifying cardiac abnormality in multi-lead ECGs using a Hybrid Neural Network (HNN) with fulcrum based data re-balancing for data comprising multiclass-multilabel cardiac abnormalities. The fulcrum based dataset re-balancing disclosed enables maintaining natural balance of the data, control the re-sample volume, and still support the lowly represented classes there by aiding proper training of the DL architecture. The HNN disclosed by the method utilizes a hybrid approach of pure CNN, a tuned-down version of ResNet, and a set of handcrafted features from a raw ECG signal that are concatenated prior to predicting the multiclass output for the ECG signal. The number of features is flexible and enables adding additional domain-specific features as needed.

公开(公告)号：US20250031966A1

公开(公告)日：2025-01-30

申请号：US18752973

申请日：2024-06-25

Applicant: Tata Consultancy Services Limited

Inventor： BHASKAR RAMCHANDRA PAWAR ,  SAKYAJIT BHATTACHARYA ,  KARAN RAJESH BHAVSAR ,  AVIK GHOSE ,  VARSHA SHARMA

IPC: A61B5/00 ,  G16Y20/20 ,  G16Y40/10

Abstract: This disclosure relates generally to method and system for monitoring human parameters using hierarchical human activity sensing. The method is based on sensing as service (SEAS) model which processes continuous mobility data from multiple sensors on the client edge-device by optimizing the on-device processing pipelines. The method requests a subject to select a human parameter of the human body to be monitored using a master device and capture the plurality of signals by recognizing sensors corresponding to the health parameter. The master device transmits to the server the subject selected human parameter of the human body to be monitored and requesting the server to recommend a hierarchical classifier structure. Further, the human body is monitored based on the on-device hierarchical sensing pipeline by executing a plurality of algorithms. In addition, the system is suitable for remote monitoring and flexible edge cloud arbitration, optimizing costs, infrastructure, and energy.