Abstract: State of art techniques, need a decoder following the encoder to encode EEG signals, whose morphology is undefined. Embodiments herein disclose a method and system for a Spiking Neural Network (SNN) based low power cognitive load analysis using electroencephalogram (EEG) signal. The method receives a raw EEG signal from multichannel EEG set up, wherein each of the raw EEG signal is re-referenced and encoded into a spike train using a Light-Weight-Lossless-Decoder less-Peak-based (LWDLP) encoding. Further, the spike trains are processed by the SNN architecture using backpropagation based supervised approach, wherein the spatial information and the temporal information are learnt by the SNN in form of neuronal activity and synaptic weights. Post learning the SNN architecture applies an activation function on the neuronal activity for classifying a cognitive load level experienced by a subject from among a plurality of predefined cognitive load levels using a SNN classifier.

Abstract: Unlike state of art eye blink detection techniques that are generalized for usage across individuals affecting accuracy of eye blink prediction from subject to subject, embodiments of the present disclosure provide a method and system for personalized eye blink detection using passive camera-based approach. The method first generates a subject specific annotation data, which is then further processed to derive subject specific personalized blink threshold values. The method disclosed provides three unique approaches to compute the personalized blink threshold values which is one time calibration process. The personalized blink threshold values are then used to generate a binary decision vector (D) while analyzing input test images (video sequences) of the subject of interest. Further, values taken by elements of the decision vector (D) are analyzed for a predefined time period to predict possible eye blinks of the subject.

Abstract: This disclosure relates generally to a method and system for classification of cognitive load (CL) using data obtained from wearable sensors. The disclosed method uses a multi-modal based approach using wrist-worn sensors for real time monitoring of CL in real world scenarios and improves the accuracy of detection of CL. A set of distinguishing features are selected from physiological signals received from the wrist-worn sensors. These features are used for training a classification model for classifying the CL of a patient into a no load or a high load. The set of distinguishing features are selected from domain specific features and signal property based generic features of the physiological signals. The disclosed method is used for classification of CL in scenarios such as to check how the cognitive load of a candidate varies during interviews, to assess the participants workload during online meetings and so on.

Abstract: Direct usage of endosomatic EDA has multiple challenges for practical cognitive load assessment. Embodiments of the method and system disclosed provide a solution to the technical challenges in the art by directly using the bio-potential signals to implement endosomatic approach for assessment of cognitive load. The method utilizes a multichannel wearable endosomatic device capable of acquiring and combining multiple bio-potentials, which are biomarkers of cognitive load experienced by a subject performing a cognitive task. Further, extracts information for classification of the cognitive load, from the acquired bio-signals using a set of statistical and a set of spectral features. Furthermore, utilizes a feature selection approach to identify a set of optimum features to train a Machine Learning (ML) based task classifier to classify the cognitive load experienced by a subject into high load task and low load task.

Abstract: Recognizing mental states from physiological signal is a concern not only for medical diagnostics, but also for cognitive science, behavioral studies as well as brain machine interfaces. Embodiments of the present disclosure utilize respiration signals to decipher mental states wherein non-linear baseline drifts in signal is implemented to extract the respiratory features in most effective way. Presence of class imbalance, is effectively rectified using Synthetic Minority Oversampling Technique (SMOTE) to resolve class imbalance problem, which not only increased the classification accuracy, but also reduced classifier bias towards the majority class, which in turn exceedingly enhanced the classifier sensitivity.

Abstract: This disclosure relates generally to assessment of cognitive workload using breathing pattern of a person, where cognitive workload is the amount of mental effort required while doing a task. The method and system provides assessment of cognitive workload based on breathing pattern extracted from photoplethysmograph (PPG) signal, which is collected from the person using a wearable device. The PPG signal collected using the wearable device are processed in multiple stages that include breathing signal extraction to extract breathing pattern. The extracted breathing pattern is used for assessment of cognitive workload using a generated personalized training model, wherein the personalized training model is generated and dynamically updated for each person based on selection of a sub-set of breathing pattern features using feature selection and classification techniques that include maximal information coefficient (MIC) techniques.

Abstract: Conventionally, a neuronal controller located inside the central nervous system governing the maintenance of the upright posture of the human body is designed from a control system perspective using proportional-integral-derivative (PID) controllers, wherein human postural sway is modeled either along a sagittal plan or along a frontal plane separately resulting in limited insights on intricacies of a governing neuronal controller. Also, existing neuronal controllers using a reinforcement learning (RL) paradigm are based on complex actor-critic on-policy algorithms. Analyzing human postural sway is critical to detect markers for progression of balance impairments. The present disclosure facilitates modelling the neuronal controller using a simplified RL algorithm, capable of producing postural sway characteristics in both sagittal and frontal plane together.

Abstract: Metacognitive confidence is defined as the confidence generated from the observation and critical analysis of one's own the decision making process. There are various studies indicative of the importance of measurement of confidence level of the person while doing a task. The existing confidence level measurement methods provide various limitations such invasive and complex experimental setup, noise and artifacts in the signal. A system and method for determining confidence level of a person using electroencephalogram has been provided. The system is configured to build a metric to determine the amount of metacognitive confidence, in presence of different cognitive load condition, directly from brain activity using electroencephalogram signals. The brain activity acquired from the frontal and temporal part of the brain at different frequency bands and combined with suitable weights to form the confidence metric.

Abstract: This disclosure relates to analyzing effect of a secondary cognitive load task on a primary executive task. Human random sequence generation is a marker to study cognitive functions and inability to generate random sequences (RS) can reveal underlying impairments. Traditionally, ‘call out’ or ‘write down’ procedures are used to obtain human generated numbers, wherein short term memory and number of previously generated entities visible to a subject plays a major role. Also precise trial-wise or response-wise analysis may not be possible. In the present disclosure, the human generated random numbers are digitized into RS and a cognitive load (CL) inducing task is imposed on the executive task. The CL demanding task disrupts randomization performance. Deviation from randomness, load index based on gaze data and deviation from pupillometry data of healthy subjects are provided as indicators of an interference effect imposed by the CL and thereby indicative of underlying impairments.

Abstract: Collision avoidance and postural stability adjustment may provide an effective dual task paradigm to interpret the effect of proprioceptive adaptation on balance control. However, conventionally tasks are physical tasks performed under supervision in specific set up environments. Implementations of the present disclosure provide methods and systems for interpreting neural interplay involving proprioceptive adaptation in a lower limb during a dual task paradigm. The disclosed method provides a better interpreting of the neuronal mechanisms underlying adaptation and learning of skilled motor movement and to determine the relationship of lower limb proprioceptive sense and postural stability by simulating integration of a Single Limb Stance (SLS) functionality test for postural stability and a single limb collision avoidance task, in an adaptive Virtual Reality (VR) environment provided to a subject.

Abstract: System and method for digitized digit symbol substitution test (DSST) are disclosed. In an example, a display area of a digitized DSST device is partitioned into multiple bins. Further, a series of number symbol pairs is displayed as a lookup table on top of the display, termed as a lookup area. Furthermore, a question and answer (QA) pair corresponding to the series of number symbol pairs to an examinee in multiple trials. In addition, feature values for the QA pair are computed in each of the multiple bins in the trials, wherein the feature values comprise a response time and an accuracy of response by the examinee. Moreover, probabilities of the feature values are determined in each of the multiple bins. Also, an entropy value based on the probabilities of the feature values is computed in each of the multiple bins providing information on distribution.

Abstract: A system and method for risk prediction and corrective action for a contact type activity is provided. The method includes generating a personalized full body musculoskeletal model to depict the knee and ankle joint behavior of a subject during the contact type activity. Various contact type activities are simulated using the personalized full body musculoskeletal model. Injury biomarkers and their parameters based on the contact type activities are identified, parameters are indicative of risk of injury to participating muscle groups said activity. Based on the injury biomarkers, optimal muscle co-activation parameters are analyzed by a neuro-muscular controller, to adapt the participating muscle groups for providing the correction action against the predicted risk of injury. Said optimal muscle co-activation parameters are indicative of muscle synergy during the contact type activity.

Abstract: System and method for aiding communication for subjects suffering from paralysis of muscles controlled by peripheral nervous system are disclosed. A method for aiding communication for said subjects includes capturing, from a plurality of sensors, sensor data generated based on an interaction of a subject with an interactive UI. The plurality of sensors includes one or more body-parts movement tracking sensors for tracking motion of body-parts of the subject and one or more physiological signal sensors for monitoring physiological signals generated from the subject during the interaction. A plurality of model parameters indicative of characteristics of the subject related to the interaction are determined based on the sensor data. The navigation at the interactive UI is controlled on the plurality of model parameters.

Abstract: Systems and methods for quantification of postural balance of users in an augmented reality (AR) environment. Traditional systems and methods provide for quantifying the postural balance using the AR environment but none of them quantify or restrict the functional tasks performed by the users to a predefined level. Embodiments of the present disclosure provide for the quantification of the postural balance with a variable step height in the AR environment by acquiring first set of information comprising of data on skeletal joints, filtering the first set of information for obtaining a filtered set of data, computing the set of postural data based upon the filtered set of data and quantifying the postural balance based upon the set of postural data by computing threshold values for obtaining postural stability index scores and determining based upon the postural stability index scores, the postural balance of the users in the AR environment.

Abstract: Unimaginable are the difficulties faced by patients affected by sensory-motor disabilities while executing day to day activities. The flamboyant progress that has made in other areas of medical science does not translate itself in diagnosing them. Early detection and personalized therapy of these disorders is still out of reach. Present disclosure provides systems and methods for optimizing a joint cost function and detecting neuro muscular profiles (e.g., state of user under observation) thereof by implementing a model that quantifies these disorders in terms of a cost functional, which captures the trade-off between the torques applied and the velocities experienced at the joints. Estimation of this cost functional, otherwise known as Inverse optimal control was then carried out using an optimization procedure.

Abstract: A system and method for detection and analysis of learner's/performers cognitive flow have been provided. The system is configured to measure the cognitive state of the performer, while they are performing tasks of various complexity levels, using physiological responses like brain activation, heart rate variability and galvanic skin response. The system uses a Bayesian network based framework to probabilistically evaluate the cognitive state of the learner from the difficulty levels of the tasks, IQ level of the learner and observations made using the physiological sensing. The system also measures the actual cognitive state using a questionnaire. The predicted cognitive state and the actual cognitive state is compared and based on the outcome of comparison a relevant step is taken.

Abstract: Traditional cognitive load estimation techniques rely on raw pupil size alone which is often prone to confound with changes in illumination, errors associated with sensor devices and irregular oscillations of pupil under constant light conditions. Estimation of cognitive load finds application in many domains including optimum work allocation, assessing a work environment and medical diagnosis. The present disclosure employs frequency domain analysis of pupil size variations to estimate load imposed by a cognitive task. A cognitive load metric based on power and frequency relations at mean frequency of the variation in pupil size addresses cognitive load estimation based on pupil dilation, wherein the pupil dilation is captured by employing low cost non-intrusive nearables.

Abstract: This disclosure relates generally to health monitoring and assessment systems, and more particularly to perform postural stability assessment of a user and quantify the assessed postural stability. In an embodiment, the system, by monitoring specific actions (which are part of certain tests done for the postural stability assessment) being performed by a user, collects inputs which are then processed to determine SLS duration, the body joint vibration, and the body sway area of the user, while performing the tests. By processing the SLS duration, the body joint vibration, and the body sway area together, a postural stability index score for the user is determined, and based on this score, postural stability assessment for the user is performed.

Abstract: This disclosure relates generally to head movement noise removal from electrooculography (EOG) signals, and more particularly to systems and methods for wavelet based head movement artifact removal from electrooculography (EOG) signals. Embodiments of the present disclosure provide for head movement noise removal from the EOG signals by acquiring EOG signals of a user, filtering the acquired EOG signals to obtain a first set of filtered EOG signals, smoothening the first set of filtered EOG signals to obtain smoothened EOG signals, removing one or more redundant patterns and one or more direct current (DC) drifts from the smoothened EOG signals to obtain a second set of filtered EOG signals, and applying, a discrete wavelet transform on the second set of filtered EOG signals to filter a plurality of head movement noise from the second set of filtered EOG signals of the user.

Abstract: A method and system is provided for pre-processing of an electroencephalography (EEG) signal for cognitive load measurement. The present application provides a method and system for pre-processing of electroencephalography signal for cognitive load measurement of a user, comprises of capturing the electroencephalography signal from the head of the user, detecting the plurality of system artifacts in the captured electroencephalography signal, detecting and removing noisy window from the captured electroencephalography signal, detecting an eye blink region and filtering out said detected eye blink region from the captured electroencephalography signal, utilizing the filtered electroencephalography signal for measuring the cognitive load of the user and subsequently computing different levels of mental workloads on the user using variation of spatial distribution of frontal scalp EEG electrodes for measured cognitive load.