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公开(公告)号：US09208376B2
公开(公告)日：2015-12-08
申请号：US14280353
申请日：2014-05-16
Applicant: TATA CONSULTANCY SERVICES LIMITED
Inventor： Kingshuk Chakravarty , Aniruddha Sinha , Diptesh Das , Rohan Banerjee , Amit Konar , Sudeepto Dutta
CPC classification number: G06K9/00348 , G06K9/44 , G06K9/627
Abstract: Method(s) and system(s) for identification of an unknown person are disclosed. The method includes receiving skeleton data comprises data of multiple skeleton joints of the unknown person from skeleton recording devices. The method further includes extracting G gait feature vectors from the skeleton data. Further, the method includes classifying each gait feature vector into one of N classes based on a training dataset for N known persons and computing a classification score for each class. The method also includes clustering the training dataset into M clusters based on M predefined characteristic attributes of the known persons, tagging each gait feature vector with one of the M clusters based on a distance between a respective gait feature vector and cluster centers of M clusters, and determining a clustering score for each M cluster. The method further includes identifying the unknown person based on clustering scores and classification scores.
Abstract translation: 公开了用于识别未知人的方法和系统。 该方法包括从骨架记录装置接收包括未知人的多个骨骼关节的数据的骨架数据。 该方法还包括从骨架数据中提取G步态特征向量。 此外，该方法包括基于N个已知人员的训练数据集将每个步态特征向量分类为N类中的一个，并计算每个类的分类分数。 该方法还包括基于已知人员的M个预定义特征属性将训练数据集聚类成M个群集，基于各个步态特征向量与M个群集的簇中心之间的距离来标记每个步态特征向量与M个群集中的一个， 以及确定每个M簇的聚类分数。 该方法还包括基于聚类分数和分类分数识别未知人。
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公开(公告)号：US20150070506A1
公开(公告)日：2015-03-12
申请号：US14386518
申请日：2013-03-12
Applicant: TATA CONSULTANCY SERVICES LIMITED
Inventor： Dhiman Chattopadhyay , Arpan Pal , Rohan Banerjee , Ranjan Dasgupta , Priyanka Sinha , Aniruddha Sinha , Chirabrata Bhaumik
IPC: G06K9/00 , G08B25/08 , G08B13/196
CPC classification number: G06K9/00771 , G06K9/00718 , G06K9/00979 , G06K2009/00738 , G06K2209/27 , G08B13/19613 , G08B25/08 , G08B27/006 , H04N7/181
Abstract: The present invention provides the multimodality filtration surveillance comprising of a plurality of filtration stages executed at the backend server to confirm nature of anomaly in an event, the filtration stages comprising: a first filter of a video anomalies detection in the event for a specified time-place value, a second filter of a city soundscape adapted to provide a localized decibel maps of a city, a third filter of a geocoded social network adapted to semantically read and analyze data from one or more social media corresponding to the specified time-place value, and a fourth filter of an event triggered or proactive local participatory surveillance adapted to provide augmented information on the detected anomalies.
Abstract translation: 本发明提供了多模过滤监视，其包括在后端服务器上执行的多个过滤阶段，以确认事件中异常的性质，所述过滤阶段包括：在特定时间段内的视频异常检测的第一过滤器， 位置值，适于提供城市的局部分贝地图的城市音景的第二滤波器，适于语义地读取和分析来自对应于指定时间位置值的一个或多个社交媒体的数据的地理编码社交网络的第三滤波器 以及适于提供关于检测到的异常的增强信息的事件触发或主动的本地参与式监视的第四过滤器。
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公开(公告)号：US12016705B2
公开(公告)日：2024-06-25
申请号：US17060009
申请日：2020-09-30
Applicant: Tata Consultancy Services Limited
Inventor： Rohan Banerjee , Avik Ghose
IPC: A61B5/00 , A61B5/02 , A61B7/04 , G06N3/00 , G06N3/02 , G06N3/045 , G06N3/08 , G06N3/088 , G16H40/67 , G16H50/20 , G16H50/30
CPC classification number: A61B5/7282 , A61B5/02028 , A61B5/725 , A61B5/7257 , A61B5/7267 , A61B5/7278 , A61B7/04 , G06N3/00 , G06N3/02 , G06N3/045 , G06N3/08 , G06N3/088 , G16H40/67 , G16H50/20 , G16H50/30
Abstract: The disclosure generally relates to methods and systems for identifying presence of abnormal heart sounds from heart sound signals of a subject being monitored. Conventional Artificial intelligence (AI) based abnormal heart sounds detection models with supervised learning requires a substantial amount of accurate training datasets covering all heart disease types for the training, which is quiet challenging. The present methods and systems solve the problem solves the problem of identifying presence of the abnormal heart sounds using an efficient semi-supervised learning model. The semi-supervised learning model is generated based on probability distribution of spectrographic properties obtained from heart sound signals of healthy subjects. A Kullback-Leibler (KL) divergence between a predefined Gaussian distribution and an encoded probability distribution of the semi-supervised learning model is determined as an anomaly score for identifying the abnormal heart sounds.
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公开(公告)号：US11887730B2
公开(公告)日：2024-01-30
申请号：US16526340
申请日：2019-07-30
Applicant: Tata Consultancy Services Limited
Inventor： Avik Ghose , Arpan Pal , Sundeep Khandelwal , Rohan Banerjee , Sakyajit Bhattacharya , Soma Bandyopadhyay , Arijit Ukil , Dhaval Satish Jani
Abstract: This disclosure relates generally to methods and systems for unobtrusive digital health assessment of high risk subjects, wherein bio-markers pertaining to a disease are identified automatically using physical activity and physiology monitoring on a continuous basis. Identification of bio-markers in the medical domain is conventionally dependent on insights derived from medical tests which are obtrusive in nature. Systems and methods of the present disclosure integrate physical characteristics, lifestyle habits and prevailing medical conditions with monitored physical activities and physiological measurements to assess health of high risk subjects. Systems and methods of the present disclosure also enable automatic generation of control class and treatment class that may be effectively used for health assessment.
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25.发明授权公开(公告)号：US11494415B2
公开(公告)日：2022-11-08
申请号：US16421372
申请日：2019-05-23
Applicant: Tata Consultancy Services Limited
Inventor： Ishan Sahu , Ayan Mukherjee , Arijit Ukil , Soma Bandyopadhyay , Chetanya Puri , Rituraj Singh , Arpan Pal , Rohan Banerjee
IPC: G06V10/771 , G06F16/28 , G06F16/245 , G06K9/62 , G06V30/19
Abstract: A method and system for a feature subset-classifier pair for a classification task. The classification task corresponds to automatically classifying data associated with a subject(s) or object(s) of interest into an appropriate class based on a feature subset selected among a plurality of features extracted from the data and a classifier selected from a set of classifier types. The method proposed includes simultaneously determining the feature subset-classifier pair based on a relax-greedy {feature subset, classifier} approach utilizing sub-greedy search process based on a patience function, wherein the feature subset-classifier pair provides an optimal combination for more accurate classification. The automatic joint selection is time efficient solution, effectively speeding up the classification task.
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Patent Agency Ranking
公开(公告)号：US11419542B2
公开(公告)日：2022-08-23
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
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.
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27.发明授权公开(公告)号：US11083416B2
公开(公告)日：2021-08-10
申请号：US15895353
申请日：2018-02-13
Applicant: Tata Consultancy Services Limited
Inventor： Rohan Banerjee , Anirban Dutta Choudhury , Arpan Pal , Parijat Dilip Deshpande , Kayapanda Muthana Mandana , Ramu Reddy Vempada
IPC: A61B5/00 , A61B5/024 , A61B7/04 , G16H50/20 , A61B5/0533 , G06N7/00 , G06N20/00 , A61B5/0205 , A61B5/318 , A61B5/349
Abstract: 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.
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公开(公告)号：US20190013102A1
公开(公告)日：2019-01-10
申请号：US15913682
申请日：2018-03-06
Applicant: Tata Consultancy Services Limited
Inventor： Deepan Das , Rohan Banerjee , Anirban Dutta Choudhury , Parijat Dilip Deshpande , Nital Shah , Vijay Anil Date , Arpan Pal , Kayapanda Muthana Mandana
Abstract: A system and method for classifying the phonocardiogram (PCG) signal quality has been described. The system is configured to identify the quality of the PCG signal recording and accepting only diagnosable quality recordings for further cardiac analysis. The system includes the derivation of plurality features of the PCG signal from the training dataset. The extracted features are preprocessed and are then ranked using mRMR algorithm. Based on the ranking the irrelevant and redundant features are rejected if their mRMR strength is less. A training model is generated using the relevant set of features. The PCG signal of the person under test is captured using a digital stethoscope and a smartphone. The PCG signal is preprocessed and only the relevant set of features are extracted. And finally the PCG signal is classified into diagnosable or non-diagnosable using the relevant set of features and a random forest classifier.
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公开(公告)号：US09855012B2
公开(公告)日：2018-01-02
申请号：US15073232
申请日：2016-03-17
Applicant: Tata Consultancy Services Limited
Inventor： Rohan Banerjee , Anirban Dutta Choudhury , Aniruddha Sinha , Avik Ghose , Arpan Pal
CPC classification number: A61B5/7235 , A61B5/0059 , A61B5/0077 , A61B5/021 , A61B5/02116 , A61B5/02438 , A61B5/14551 , A61B5/6898 , A61B5/7203 , A61B5/7246 , A61B5/7264 , A61B5/7278 , G06T5/002 , G06T5/20 , G06T7/0012 , G06T2207/10016 , G06T2207/20182 , G06T2207/30104
Abstract: A method and system is provided for noise cleaning of photoplethysmogram signals. The method and system is disclosed for noise cleaning of photoplethysmogram signals for estimating blood pressure of a user; wherein photoplethysmogram signals are extracting from the user; the extracted photoplethysmogram signals are up sampled; the up sampled photoplethysmogram signals are filtered; uneven baseline drift of each cycle is removed from the up sampled and filtered photoplethysmogram signals; outlier cycles of the photoplethysmogram signals are removed and remaining cycles of the photoplethysmogram signals are modeled; and time domain features are extracted from originally extracted and modeled photoplethysmogram signals for estimating blood pressure of the user.
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公开(公告)号：US20160310084A1
公开(公告)日：2016-10-27
申请号：US15073232
申请日：2016-03-17
Applicant: Tata Consultancy Services Limited
Inventor： Rohan Banerjee , Anirban Dutta Choudhury , Aniruddha Sinha , Avik Ghose , Arpan Pal
CPC classification number: A61B5/7235 , A61B5/0059 , A61B5/0077 , A61B5/021 , A61B5/02116 , A61B5/02438 , A61B5/14551 , A61B5/6898 , A61B5/7203 , A61B5/7246 , A61B5/7264 , A61B5/7278 , G06T5/002 , G06T5/20 , G06T7/0012 , G06T2207/10016 , G06T2207/20182 , G06T2207/30104
Abstract: A method and system is provided for noise cleaning of photoplethysmogram signals. The method and system is disclosed for noise cleaning of photoplethysmogram signals for estimating blood pressure of a user; wherein photoplethysmogram signals are extracting from the user; the extracted photoplethysmogram signals are up sampled; the up sampled photoplethysmogram signals are filtered; uneven baseline drift of each cycle is removed from the up sampled and filtered photoplethysmogram signals; outlier cycles of the photoplethysmogram signals are removed and remaining cycles of the photoplethysmogram signals are modeled; and time domain features are extracted from originally extracted and modeled photoplethysmogram signals for estimating blood pressure of the user.
Abstract translation: 提供了一种用于光电体积描记图信号的噪声清洁的方法和系统。 公开了用于估计使用者的血压的光电容积图信号的噪声清洁的方法和系统; 其中光电体积描图信号从用户提取; 提取的光电容积图信号被上采样; 上采样的photoplethysmogram信号被过滤; 从上采样和滤波的光电容积图中消除每个周期的不均匀基线漂移; 除去光电容积图信号的异常周期，并对光电容积图的信号的剩余周期进行建模; 并且从用于估计用户血压的原始提取和建模的光电容积图信号中提取时域特征。
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