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

IPC: G06K9/00 ,  G06K9/44 ,  G06K9/62

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。

公开(公告)号：US20160242699A1

公开(公告)日：2016-08-25

申请号：US15023314

申请日：2014-09-12

Applicant: TATA CONSULTANCY SERVICES LIMITED

Inventor： Diptesh Das ,  Debatri Chatterjee ,  Arijit Sinharay ,  Aniruddha Sinha

IPC: A61B5/00 ,  A61B5/0476 ,  A61B5/048

Abstract: System and method for evaluating a cognitive load on a user, corresponding to a stimulus is disclosed. Electroencephalogram (EEG) data corresponding to the stimulus of a user is received. The stimulus corresponds to a mental task performed by the user. The EEG data is split into a plurality of slots. A slot of the plurality of slots comprises a subset of the EEG data. One or more EEG features are extracted from the subset of the EEG data. The one or more EEG features are represented in one of a frequency domain and a time domain. A plurality of data points present in the one or more EEG features is grouped into two or more clusters using an unsupervised learning technique. The two or more clusters comprise one or more data points of the plurality of data points. The one or more data points correspond to a level of the cognitive load.

Abstract translation: 公开了用于评估对应于刺激的用户的认知负荷的系统和方法。 接收与用户的刺激对应的脑电图（EEG）数据。 刺激对应于用户执行的心理任务。 EEG数据被分成多个时隙。 多个时隙中的时隙包括EEG数据的子集。 从EEG数据的子集中提取一个或多个EEG特征。 一个或多个EEG特征在频域和时域之一中表示。 存在于一个或多个EEG特征中的多个数据点使用无监督学习技术被分组为两个或更多个簇。 两个或更多个簇包括多个数据点中的一个或多个数据点。 一个或多个数据点对应于认知负荷的水平。

公开(公告)号：US20160128593A1

公开(公告)日：2016-05-12

申请号：US14665476

申请日：2015-03-23

Applicant: Tata Consultancy Services Limited

Inventor： Arijit Sinharay ,  Aniruddha Sinha ,  Diptesh Das ,  Debatri Chatterjee

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

CPC classification number: A61B5/04014 ,  A61B5/0006 ,  A61B5/0476 ,  A61B5/165 ,  A61B5/4088 ,  A61B5/7221 ,  A61B2560/0475

Abstract: Disclosed is a method and system for selection of Electroencephalography (EEG) channels valid for determining cognitive load of subject. According to one embodiment, EEG signals are obtained from EEG channels associated with subject performing cognitive tasks are received. Time-frequency features of EEG signals are extracted for a frequency band comprise maximum energy value, minimum energy value, average energy value, maximum frequency value, minimum frequency value, and average frequency value. Weight of an EEG channel associated with time-frequency feature is derived using statistical learning technique. Binary values for EEG channels corresponding to time-frequency feature are assigned using weight of EEG channel associated with time-frequency feature. Intersections of binary values of EEG channels corresponding to maximum energy value and average energy value, minimum energy value and average energy value, maximum frequency value and average frequency value, and minimum frequency value and average frequency value are computed. Unions of intersections are computed, wherein the unions represent EEG channels valid to determine cognitive load of subject.

Abstract translation: 公开了用于选择有效用于确定受试者的认知负荷的脑电图（EEG）通道的方法和系统。 根据一个实施例，从接收到执行认知任务的对象相关联的EEG信道获得EEG信号。 为包括最大能量值，最小能量值，平均能量值，最大频率值，最小频率值和平均频率值的频带提取EEG信号的时频特征。 使用统计学习技术导出与时频特征相关的脑电信道的权重。 对应于时频特征的EEG频道的二进制值使用与时频特征相关的EEG频道的权重来分配。 计算与最大能量值和平均能量值，最小能量值和平均能量值，最大频率值和平均频率值以及最小频率值和平均频率值对应的EEG频道的二进制值的交点。 计算交叉点的联合，其中工会代表有效确定受试者的认知负荷的EEG通道。

公开(公告)号：US10827981B2

公开(公告)日：2020-11-10

申请号：US15023314

申请日：2014-09-12

Applicant: Tata Consultancy Services Limited

Inventor： Diptesh Das ,  Debatri Chatterjee ,  Arijit Sinharay ,  Aniruddha Sinha

IPC: A61B5/0484 ,  A61B5/00 ,  A61B5/0476 ,  A61B5/048

Abstract: System and method for evaluating a cognitive load on a user, corresponding to a stimulus is disclosed. Electroencephalogram (EEG) data corresponding to the stimulus of a user is received. The stimulus corresponds to a mental task performed by the user. The EEG data is split into a plurality of slots. A slot of the plurality of slots comprises a subset of the EEG data. One or more EEG features are extracted from the subset of the EEG data. The one or more EEG features are represented in one of a frequency domain and a time domain. A plurality of data points present in the one or more EEG features is grouped into two or more clusters using an unsupervised learning technique. The two or more clusters comprise one or more data points of the plurality of data points. The one or more data points correspond to a level of the cognitive load.