US 12,412,671 B2
Method and system for classification of cognitive load using data obtained from wearable sensors
Debatri Chatterjee, Kolkata (IN); Dibyanshu Jaiswal, Kolkata (IN); Ramesh Kumar Ramakrishnan, Bangalore (IN); Rahul Dasharath Gavas, Bangalore (IN); and Arpan Pal, Kolkata (IN)
Assigned to TATA CONSULTANCY SERVICES LIMITED, Mumbai (IN)
Filed by Tata Consultancy Services Limited, Mumbai (IN)
Filed on Jun. 23, 2022, as Appl. No. 17/808,468.
Claims priority of application No. 202121028199 (IN), filed on Jun. 23, 2021.
Prior Publication US 2022/0415521 A1, Dec. 29, 2022
Int. Cl. G06N 20/00 (2019.01); G16H 40/67 (2018.01); G16H 50/70 (2018.01)
CPC G16H 50/70 (2018.01) [G06N 20/00 (2019.01); G16H 40/67 (2018.01)] 15 Claims
OG exemplary drawing
 
1. A processor implemented method comprising the steps of:
providing, via one or more hardware processors, a plurality of physiological signals collected from a plurality of subjects through a plurality of wearable sensors as training data for training a classification model for classification of cognitive load, wherein each of the plurality of physiological signals are prelabelled as one of (i) a first class or (ii) a second class, wherein the training data is a balanced dataset, and wherein the plurality of physiological signals comprises (i) Galvanic skin response, (ii) Heart rate, (iii) RR interval and (iv) skin temperature;
extracting, via the one or more hardware processors, (i) a plurality of domain specific features and (ii) a plurality of signal property based generic features as a plurality of features from the plurality of physiological signals using a multi-level approach, wherein the multi-level approach comprises:
extracting time domain features, short term Fourier transform (STFF) based features, and Discrete wavelet transform (DWT) based features in a first level,
extracting spectral, statistical, peak-trough features in a second level based on feature extracted in the first level, and
computing ratios and derivatives in a third level, from the features extracted in the second level;
selecting, via the one or more hardware processors, a set of optimal features from the plurality of features using a maximal information coefficient algorithm and a minimum redundancy maximum relevance algorithm;
augmenting, via the one or more hardware processors, the training data by artificially inducing class imbalance between the first class and the second class, wherein the class imbalance is induced by taking all instances of the first class with half of instances of the second class;
applying, via the one or more hardware processors, a synthetic minority over-sampling technique on the augmented training data to generate a set of synthetic data;
training, via the one or more hardware processors, the classification model using (i) the training data and (ii) the set of synthetic data to classify the cognitive load as one of (i) a low load or (ii) a high load;
obtaining, via a wearable device, a set of physiological signals from a subject for classifying in real time the cognitive load of the subject;
obtaining, via the one or more hardware processors, the set of optimal features from the set of physiological signals of the subject;
classifying, via the one or more hardware processors, the cognitive load using the trained classification model as one of (i) the low load or (ii) the high load; and
monitoring, in real time via the one or more hardware processors, the cognitive load of the subject based on the classification of the cognitive load using the trained classification model to analyze the cognitive load of the subject in real world scenarios including analyzing the cognitive load of the subject during one of an interview, an online meeting, a workshop, and a tutorial.