公开(公告)号：US20180256029A1

公开(公告)日：2018-09-13

申请号：US15760709

申请日：2016-09-26

Applicant: Tata Consultancy Services Limited

Inventor： Parijat Dilip DESHPANDE ,  Arijit SINHARAY ,  Ranjan DASGUPTA ,  Arpan PAL

IPC: A61B5/00 ,  G16H10/60 ,  G16H70/60 ,  G06F17/30 ,  A61B5/145 ,  A61B34/32 ,  A61B5/0205

CPC classification number: A61B5/0022 ,  A61B5/002 ,  A61B5/0075 ,  A61B5/0077 ,  A61B5/0205 ,  A61B5/021 ,  A61B5/02405 ,  A61B5/0402 ,  A61B5/0816 ,  A61B5/14532 ,  A61B5/14542 ,  A61B5/6835 ,  A61B5/6898 ,  A61B5/7264 ,  A61B5/7275 ,  A61B34/32 ,  G06F16/951 ,  G06Q10/10 ,  G06Q50/22 ,  G16H10/60 ,  G16H40/67 ,  G16H50/20 ,  G16H70/60

Abstract: A system is provided for automated monitoring the health of a patient. The system is unifying the approach of multi-sensing, robotic platform and cloud computing to monitor the health of the patient with zero or very minimal human intervention. The plurality of physiological parameters and the pathological values is sensed using the plurality of physiological sensors and the plurality of pathological sensors or using a smart phone of the patient. The body of the patient is scanned using a robotic arm. The data sensed by the sensor is then identifies a set of anomalies and send the set of anomalies to cloud server. A cognitive engine present on the cloud server is then diagnoses a disease using cloud computing and send the report to caregiver and doctor. According to another embodiment, a method is also provided for automated monitoring the health of the person using the above mentioned system.

公开(公告)号：US20180153419A1

公开(公告)日：2018-06-07

申请号：US15828540

申请日：2017-12-01

Applicant: Tata Consultancy Services Limited

Inventor： Arijit UKIL ,  Soma BANDYOPADHYAY ,  Chetanya PURI ,  Rituraj SINGH ,  Arpan PAL ,  Debayan MUKHERJEE

IPC: A61B5/024 ,  A61B7/04

CPC classification number: A61B5/02405 ,  A61B5/02444 ,  A61B7/04 ,  A61B34/10 ,  G06F16/7834 ,  G16H50/20 ,  H04R29/00

Abstract: This disclosure relates generally to physiological monitoring, and more particularly to feature set optimization for classification of physiological signal. In one embodiment, a method for physiological monitoring includes identifying clean physiological signal training set from an input physiological signal based on a Dynamic Time Warping (DTW) of segments associated with the physiological signal. An optimal features set is extracted from a clean physiological signal training set based on a Maximum Consistency and Maximum Dominance (MCMD) property associated with the optimal feature set that strictly optimizes on the objective function, the conditional likelihood maximization over different selection criteria such that diverse properties of different selection parameters are captured and achieves Pareto-optimality. The input physiological signal is classified into normal signal components and abnormal signal components using the optimal features set.

公开(公告)号：US20180038954A1

公开(公告)日：2018-02-08

申请号：US15552225

申请日：2016-02-16

Applicant: Tata Consultancy Services Limited

Inventor： Parijat DESHPANDE ,  Ramu VEMPADA ,  Ranjan DASGUPTA ,  Arpan PAL ,  Dibyendu ROY

IPC: G01S15/52 ,  G08B29/18 ,  G08B13/16

CPC classification number: G08B13/1672 ,  G01N2291/045 ,  G01N2291/26 ,  G01S15/523 ,  G08B13/1636 ,  G08B13/1681 ,  G08B29/186

Abstract: An acoustic array system for anomaly detection is provided. The acoustic array system (100) 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 (100) 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.

公开(公告)号：US20170340211A1

公开(公告)日：2017-11-30

申请号：US15467789

申请日：2017-03-23

Applicant: Tata Consultancy Services Limited

Inventor： Soma BANDYOPADHYAY ,  Arijit UKIL ,  Chetanya PURI ,  Rituraj SINGH ,  Arpan PAL ,  C. A. MURTHY ,  Kayapanda MANDANA

IPC: A61B5/024 ,  A61B5/046 ,  A61B5/0245 ,  A61B5/0402 ,  A61B5/00 ,  A61B5/0468 ,  A61B5/0456

CPC classification number: A61B5/024 ,  A61B5/0002 ,  A61B5/02108 ,  A61B5/02405 ,  A61B5/02416 ,  A61B5/02438 ,  A61B5/0245 ,  A61B5/0402 ,  A61B5/0456 ,  A61B5/046 ,  A61B5/0468 ,  A61B5/7203

Abstract: This disclosure relates generally to biomedical signal processing, and more particularly to method and system for physiological parameter derivation from pulsating signals with reduced error. In this method, pulsating signals are extracted, spurious perturbations in the extracted pulsating signals are removed for smoothening, local minima points in the smoothened pulsating signal are derived, systolic maxima point between two derived local minima are derived, most probable pulse duration and most probable peak-to-peak distance are derived, dicrotic minima is removed while ensuring that every dicrotic minima is preceded by a systolic maxima point and followed by a beat start point of said systolic maxima, diastolic peak is derived while ensuring that every dicrotic maxima is preceded by a diastolic notch followed by next beat start point of that maxima, and physiological parameters are derived from the derived local minima points, systolic maxima points, dicrotic notch and diastolic peak.

公开(公告)号：US20170273632A1

公开(公告)日：2017-09-28

申请号：US15456112

申请日：2017-03-10

Applicant: TATA CONSULTANCY SERVICES LIMITED

Inventor： Arijit UKIL ,  Soma BANDYOPADHYAY ,  Chetanya PURI ,  Arpan PAL ,  Kayapanda MANDANA

IPC: A61B5/00 ,  A61B5/024

CPC classification number: A61B5/7207 ,  A61B5/0004 ,  A61B5/02416 ,  A61B5/6815 ,  A61B5/6826 ,  A61B5/6829 ,  A61B5/6898 ,  A61B5/7246 ,  A61B5/725 ,  A61B5/7264 ,  A61B5/7278 ,  A61B2576/00 ,  G16H50/20 ,  G16H50/30

Abstract: A method and system for removing corruption in photoplethysmogram (PPG) signals for monitoring cardiac health of patients is provided. The method is performed by extracting photoplethysmogram signals from the patient, detecting and eliminating corruption caused by larger and transient disturbances in the extracted photoplethysmogram signals, segmenting photoplethysmogram signals post detection and elimination of corruption caused by larger and transient disturbances, identifying of inconsistent segments from the segmented photoplethysmogram signals, detecting anomalies from the identified inconsistent segments of the photoplethysmogram signals, analysing the detected anomalies of the photoplethysmogram signals and identifying photoplethysmogram signal segments corrupted by smaller and prolonged disturbances.

公开(公告)号：US20170055913A1

公开(公告)日：2017-03-02

申请号：US15208230

申请日：2016-07-12

Applicant: Tata Consultancy Services Limited

Inventor： Soma BANDYOPADHYAY ,  Arpan PAL ,  Arijit UKIL ,  Tulika BOSE ,  Chetanya PURI

IPC: A61B5/00 ,  A61B5/0402 ,  A61B5/0476 ,  A61B5/024

Abstract: The present subject matter discloses a system and a method for identifying information from sensor data in a sensor agnostic manner. The system may receive sensor data provided by a sensor and may determine statistical features of the sensor data. The system may determine signal dynamics of the sensor data based on at least one of the statistical features, signal processing features, and a data distribution model. The system may select at least one outlier class based on the signal dynamics, number of streams of the sensor data, and dimensions of the sensor data. The system may select at least one outlier detection method associated with an outlier class for detecting outliers in the sensor data. The system may determine information content of the sensor data based on the outliers, the signal dynamics, the statistical features, and information theoretic features, and similarity or dissimilarity measure.

Abstract translation: 本主题公开了一种用于以传感器不可知方式从传感器数据识别信息的系统和方法。 系统可以接收由传感器提供的传感器数据，并且可以确定传感器数据的统计特征。 该系统可以基于统计特征，信号处理特征和数据分布模型中的至少一个确定传感器数据的信号动态。 系统可以基于信号动态，传感器数据的流数和传感器数据的尺寸来选择至少一个离群类。 该系统可以选择与异常值相关联的至少一个异常值检测方法，用于检测传感器数据中的异常值。 该系统可以基于异常值，信号动力学，统计特征和信息理论特征以及相似性或不相似性度量来确定传感器数据的信息内容。

公开(公告)号：US20160140359A1

公开(公告)日：2016-05-19

申请号：US14900061

申请日：2014-06-09

Applicant: TATA CONSULTANCY SERVICES LIMITED

Inventor： Arijit MUKHERJEE ,  Soma BANDYOPADHYAY ,  Arijit UKIL ,  Abhijan BHATTACHARYYA ,  Swarnava DEY ,  Arpan PAL ,  Himadri Sekhar PAUL

IPC: G06F21/62 ,  H04L12/26 ,  H04L29/08

CPC classification number: G06F21/6245 ,  H04W12/02 ,  G06F21/6263 ,  H04L43/08 ,  H04L63/0407 ,  H04L67/10

Abstract: This disclosure relates generally to the use of distributed system for computation, and more particularly, relates to a method and system for optimizing computation and communication resource while preserving security in the distributed device for computation. In one embodiment, a system and method of utilizing plurality of constrained edge devices for distributed computation is disclosed. The system enables integration of the edge devices like residential gateways and smart phone into a grid of distributed computation. The edged devices with constrained bandwidth, energy, computation capabilities and combination thereof are optimized dynamically based on condition of communication network. The system further enables scheduling and segregation of data, to be analyzed, between the edge devices. The system may further be configured to preserve privacy associated with the data while sharing the data between the plurality of devices during computation.

Abstract translation: 本公开一般涉及分布式系统用于计算的用途，更具体地，涉及一种用于优化计算和通信资源同时保持分布式设备中的安全性进行计算的方法和系统。 在一个实施例中，公开了一种利用多个约束边缘装置进行分布式计算的系统和方法。 该系统使边缘设备（如住宅网关和智能手机）集成到分布式计算网格中。 基于通信网络的条件，具有约束带宽，能量，计算能力及其组合的边缘设备被动态优化。 该系统进一步实现边缘设备间的数据分析和分析。 该系统还可以被配置为在计算期间在多个设备之间共享数据的同时保留与数据相关联的隐私。

公开(公告)号：US20160113539A1

公开(公告)日：2016-04-28

申请号：US14627334

申请日：2015-02-20

Applicant: Tata Consultancy Services Limited

Inventor： Arijit SINHARAY ,  Debatri CHATTERJEE ,  Arpan PAL

IPC: A61B5/04 ,  A61B5/00 ,  A61B5/0476

CPC classification number: A61B5/04017 ,  A61B5/0476 ,  A61B5/16 ,  A61B5/7203 ,  A61B5/7253 ,  A61B5/7257 ,  A61B5/7264 ,  A61B5/7267

Abstract: Disclosed is a method and system for determining a cognitive load of a subject from Electroencephalography (EEG) signals. EEG signals are received from EEG channels associated with a left-frontal brain lobe. EEG signals are associated with a subject performing cognitive task. EEG signals are received from a low resolution EEG device. EEG channels comprise four EEG channels associated with the left-frontal brain lobe. EEG signals are preprocessed using a Hilbert-Huang Transform (HHT) filter to remove a noise corresponding to one or more non-cerebral artifacts to generate preprocessed EEG signals. Features comprising Fast Fourier Transform (FFT) based alpha and theta band power are extracted from the preprocessed EEG signals. Feature vector is generated from the features. The feature vector is classified using a Support Vector Machine (SVM) classifier to determine the cognitive load of the subject.

Abstract translation: 公开了一种用于从脑电图（EEG）信号确定受试者的认知负荷的方法和系统。 从与左额脑脑相关的EEG通道接收EEG信号。 EEG信号与执行认知任务的对象相关联。 EEG信号从低分辨率EEG设备接收。 脑电信道包括与左额脑脑相关的四个脑电信道。 使用希尔伯特 - 黄变换（HHT）滤波器对EEG信号进行预处理，以去除与一个或多个非脑神经元相对应的噪声，以产生预处理的脑电信号。 从预处理的EEG信号中提取包括基于快速傅里叶变换（FFT）的α和θ带功率的特征。 特征向量是从特征生成的。 使用支持向量机（SVM）分类器对特征向量进行分类，以确定受试者的认知负荷。