Patent Description:
Cognitive load corresponds to the amount of working memory demanded while performing a certain task. Estimation of cognitive load is crucial to many domains pertaining to optimum work allocation, assessing a work environment, medical diagnosis, and the like. Various physiological sensing like Electroencephalogram (EEG), Galvanic Skin Response (GSR), Electrocardiogram (ECG), skin temperature, Electromyogram (EMG), Photophlethesmography (PPG), etc. have been used to determine cognitive load in the art. These techniques require subjects to wear physiological sensors which may be uncomfortable / unacceptable in certain scenarios. However, with the advent of nearable eye trackers, nowadays, it is possible to remotely assess cognitive load imposed on a subject. Moreover, these devices are easy to use as they do not impose additional overhead experienced due to wearable sensors like head mounted eye trackers. Eye movements and pupil size variations render valuable information pertaining to the psychological states of the subjects. Normally pupils dilate in the absence of light in order to allow more light to pass into the eye. However, there are other instances resulting in pupil dilation like challenging cognitive task (termed as task evoked pupillary response (TEPR)), emotional states, medication, etc. These changes are involuntary actions and most of the time it happens unconsciously. Thus, the same pupil dilation might represent different conditions and the exact meaning depends largely on the situation. Estimating cognitive load based on pupil dilation is therefore a challenge. Document (Cognitive state recognition using wavelet singular entropy and ARMA entropy with AFPA optimized GP classification) discloses cognitive state, which is the inner mental state of a person while interacting with an artificial system through man-machine interface, can be affected by various factors, such as fatigue, stress, mental workload, attention deficit, and executive function, among others, which can lead to errors, accidents, or even disasters. One practical solution to this problem is to monitor and recognize the cognitive state of subjects via physiological signals. In this study, a hybrid adaptive flower pollination algorithm-Gaussian process model is proposed to recognize the cognitive state of in-flight pilots. Instead of using the traditional conjugate gradient technique to find optimal hyperparameters, an improved flower pollination algorithm is proposed. The adaptive Lévy strategy is then used to increase the robustness of this algorithm, as well as to enhance the global optimization and generalization capability of the Gaussian process model. In addition to conventional features in the time-frequency domain, a novel set of features involving wavelet singular entropy and autoregressive-moving average entropy is proposed to improve classification accuracy. Experiments are performed through flight simulations in a full flight simulator with six degrees of freedom. Comparable experimental results validate the feasibility of the proposed method for recognizing cognitive state and provide a wide range of conclusions on the feature selection and feature patterns of cognitive state (Abstract). Document (<CIT>) discloses system and method for assessing a plurality of Health Care Devices (HCDs). Features of plurality of patients are extracted using at least two Health Care Devices (HCDs). Based on the features extracted, a performance index and a usability index of the at least two HCDs are calculated. The performance index is calculated by performing an anova analysis on the features extracted. Further, the performance index and the usability index calculated are normalized for each of the at least two HCDs. Subsequently, an average of the performance index and the usability index normalized is calculated. Further, a quality index of the at least two HCDs is determined based on the performance index and the usability index normalized to assess the at least two HCDs (Abstract).

Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems.

According to the invention, there is provided a processor implemented method as defined in claim <NUM>.

According to the invention, there is provided a system is as defined in claim <NUM>.

According to the invention, there is provided a computer program product comprising a non-transitory computer readable medium having a computer readable program embodied therein, wherein the computer readable program, when executed on a computing device, causes the computing device is as defined in claim <NUM>.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the embodiments of the present disclosure.

The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles.

It should be appreciated by those skilled in the art that any block diagram herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computing device or processor, whether or not such computing device or processor is explicitly shown.

It is intended that the following detailed description be considered as exemplary only, with the invention being defined in the following claims.

Cognitive load or mental workload is characterized by the amount of memory resources utilized to ascertain a task. As per the cognitive load theory, cognitive load can be broadly classified into three major classes, viz. , intrinsic load, extraneous load and germane load. Intrinsic load is experienced due to the complexity of the task itself. For instance, adding <NUM> digit numbers is comparatively difficult than adding single digit numbers. Extraneous load occurs owing to the presentation of the information in the task; for instance, presenting data graphically over a tabulated format. The former induces lower extraneous load over the latter. Germane load occurs during comprehending and processing of new information. Most of the existing researches use raw pupil size as an indicator of mental workload and they do not use any specific metric which can be used to differentiate between various levels of cognitive load. The present disclosure is directed towards intrinsic cognitive load and for all purposes, the expression "cognitive load" referred hereinafter in the present disclosure refers to intrinsic cognitive load.

Traditional techniques of assessing cognitive load involved analyzing performance measures like total score or time taken to analyze the cognitive load imposed during a task. However, this method involves customizing the performance measures to the task as the tasks differ often. Also, this method lacks continuous monitoring as the performance score is obtained after a certain interval or after the completion of the task. Another method employed in the art is a questionnaire based approach which gives subjective measures of the task difficulty experienced by the user and is generally done after the task completion. This method is highly biased and solely depends on the subject's perception of work load.

Physiological sensing provides relatively better estimation in the art. It renders real time monitoring of load imposed on the subject without their knowledge. Perceptual-motor changes during a task may be measured using sensors and by inference the perceptual-motor changes are attributed to cognitive performance of the subject. High resolution sensors are efficient in terms of accuracy, however, their increased cost and complexity has created a surge for affordable and easy to use devices. However, such affordable devices are basically low resolution ones and are prone to noise inherent due to sensor mechanics involved.

The present disclosure is directed towards employing low cost non-intrusive wearables that are traditionally considered inefficient in facilitating cognitive load estimation. Changes in screen illumination, ethnic differences among the subjects, operating distance from the screen, data loss due to blinks and other related measurement noise form key factors that affect efficiency of cognitive load estimation. Unobtrusive eye tracking is advantageous for scenarios like evaluation of distraction, mental effort, anxiety etc. Subjects may get disturbed while wearing the eye trackers mounted on head which may lead to distortion of results. Hence, in accordance with the present disclosure, non-intrusive wearables in the form of nearables such as low cost, infrared-based eye trackers are employed to capture raw pupil size data to quantify the cognitive load based on dilating nature of the pupil. Particularly, the present disclosure distinguishes and quantifies the intrinsic cognitive load into two classes - low and high loads using the dilating nature of the pupil. In accordance with the present disclosure, a metric is provided to estimate cognitive load based on the pupil dilation and validated based on nature of visual field area and the percentage change in pupil diameter and also with the intelligence level of the participants, thereby eliminating the inherent inefficiency traditionally associated with low cost nearables.

Referring now to the drawings, and more particularly to <FIG>, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and method.

<FIG> illustrates an exemplary block diagram of a system <NUM> for cognitive load estimation based on pupil dilation, in accordance with an embodiment of the present disclosure. In an embodiment, the system <NUM> includes one or more processors <NUM>, communication interface device(s) or input/output (I/O) interface(s) <NUM>, and one or more data storage devices or memory <NUM> operatively coupled to the one or more processors <NUM>. The one or more processors <NUM> that are hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, graphics controllers, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) are configured to fetch and execute computer-readable instructions stored in the memory. In an embodiment, the system <NUM> can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, servers, a network cloud and the like.

The I/O interface device(s) <NUM> can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface device(s) can include one or more ports for connecting a number of devices to one another or to another server.

The memory <NUM> may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, one or more modules (not shown) of the system <NUM> can be stored in the memory <NUM>.

In an embodiment, the system <NUM> comprises one or more data storage devices or memory <NUM> operatively coupled to the one or more processors <NUM> and is configured to store instructions configured for execution of steps of the method <NUM> by the one or more processors <NUM>.

<FIG> is an exemplary flow diagram illustrating a computer implemented method for cognitive load estimation based on pupil dilation, in accordance with an embodiment of the present disclosure. The steps of the method <NUM> will now be explained in detail with reference to the components of the system <NUM> of <FIG>. Although process steps, method steps, techniques or the like may be described in a sequential order, such processes, methods and techniques may be configured to work in alternate orders. In other words, any sequence or order of steps that may be described does not necessarily indicate a requirement that the steps be performed in that order. The steps of processes described herein may be performed in any order practical. Further, some steps may be performed simultaneously.

In accordance with an embodiment of the present disclosure, the one or more processors <NUM> are configured to capture, at step <NUM>, a cognitive task evoked pupillary response in the form of raw pupil size data associated with a subject, wherein the capturing is based on remote eye tracking. In an embodiment, EyeTribe™ eye tracker may be used. Good calibration may be ensured to analyze the impact of eye motion under cognitive load since deviation from target gaze location is critical to the analyses. The raw pupil size data obtained by remote eye tracking may contain some missing values due to eye blinks or some abrupt values of pupil size due to hardware glitches. Remote eye tracker like EyeTribe™ returns a zero value during the blink interval. Accordingly, in accordance with an embodiment of the present disclosure, the one or more processors <NUM> are configured to pre-process, at step <NUM>, the captured raw pupil size data to obtain filtered data. In an embodiment the missing portions in the captured raw pupil size data may be interpolated based on previous and consequent data points to obtain adjusted raw pupil data, wherein the adjusted raw pupil data accounts for the missing portions to ensure availability of continuous data. Pupil responses during cognitive activities mainly occur below <NUM> above which the signal is mainly due to measurement noise. Accordingly, the pre-processing at step <NUM> may include low pass filtering of the adjusted raw pupil data by a low pass filter having a cutoff frequency of <NUM> to eliminate noise and obtain filtered data.

In accordance with the present disclosure, the one or more processors <NUM> are configured to perform frequency domain analysis, at step <NUM>, on the filtered data to obtain cognitive load L(t). The step of performing frequency domain analysis on the filtered data comprises segmenting the filtered data to obtain baseline and trial portions based on time stamps extracted from metadata associated with the raw pupil size data. The baseline portion is illustrated in an exemplary embodiment depicted in <FIG> and the associated description. The trial portions follow the marked base line portions. The estimated cognitive load is a function of a sequence of load pulses with varying magnitudes. The total cognitive load imparted for a given trial portion is thereby a combination of all the load pulses.

In accordance with the present disclosure, short-time Fourier transform (s-transform) is performed to obtain frequency fωi and power pωi for each frequency bin associated with the filtered data. In accordance with an embodiment, the one or more processors are configured to compute mean frequency f(ω) of the filtered data, at step 206a, based on (i) number of frequency bins n, (ii) frequency band ωi of each of the frequency bins i, and (iii) frequency fωi and energy density associated with each of the frequency bins. The mean frequency f(ω) of the filtered data, in accordance with the present disclosure may be represented as given below.

In accordance with an embodiment, the one or more processors are configured to compute cognitive load L(t), at step 206b, based on the mean frequency f(ω) and power p(ω) corresponding to the mean frequency f(ω). Accordingly, the cognitive load L(t) may be represented as given below.

A mental addition task was used and developed using Matlab to impart low and high cognitive loads on subjects. In the low load task, single digit numbers from <NUM>-<NUM> were used, whereas for the high load task, numbers in the range of <NUM>-<NUM> were selected. Numbers such as <NUM>, <NUM> and <NUM> were excluded since they are relatively easier to add with other numbers. In all, <NUM> numbers for the low load and <NUM> numbers for the high load were selected.

Fifteen participants (<NUM> females, <NUM> males, mean age: <NUM>+8years) participated in the study. Necessary consents and clearance were obtained from the participants. It was ensured that the participants have similar cultural and educational backgrounds. All the participants had normal or corrected to normal vision with spectacles and all were right handed. At the beginning, the task and the procedure were explained to them. In order to ensure that the participants followed the tasks properly, a demo version of the task was performed before starting the actual data capture. EyeTribe™ eye tracker was used for the study. An initial calibration was done using the Software Development Kit (SDK) based calibration procedure to obtain a calibration score of <NUM>, which is indicative of good calibration. The sampling frequency of the eye tracker was <NUM>. A chin rest was used in order to ensure minimal head movements and kept at a distance of <NUM> away from the participant. The experiment was carried out in a closed quite room with constant lighting conditions. The participants wore a headphone to listen to the stimulus. A <NUM> second baseline section preceded the task during which the participants were asked to focus on a fixation cross in white background that was displayed in the midst of the computer screen. The fixation cross was used so that the participants are forced to keep their eyes open while calculating mentally. A series of numbers is played one after another with an interval of <NUM> seconds and the participants were instructed to add them mentally. <FIG> illustrates stimulus and cognitive tasks assigned to a subject in an exemplary setup in accordance with an embodiment of the present disclosure. The tone of the numbers is generated through a computer program so that same sound characteristics are maintained across all trial portions. Timestamps at appropriate locations were logged for further analysis. For <NUM> out of <NUM> participants, the low load task was administered first followed by the high load task. For rest of the participants, reverse order was followed in order to avoid any bias related effects owing to the sequence of the task. The eye tracker returned a zero value during blink intervals that lasted for almost <NUM>-<NUM>. These blink portions were interpolated and low pass filtering was performed to eliminate noise and obtain filtered data. The cognitive load imposed on a participant were computed using equation (<NUM>) above. <FIG> illustrates variations of estimated cognitive load values, in accordance with an embodiment of the present disclosure, for a participant. As the trial proceeds, the total load imparted on the participant also increases and is evident from <FIG>.

Similar trend is followed for the rest of the participants. The cognitive load is estimated per session and averaged across both the low and the high load tasks and is illustrated in <FIG>. It is noted that the difference between the cognitive loads corresponding to the low and high load tasks were significant for all the participants, thereby illustrating consistency of the proposed metric for cognitive load estimation.

<FIG> illustrates the nature of the distribution of the cognitive load points computed, in accordance with an embodiment of the present disclosure, for all the participants across trials, through a violin plot. The data points are statistically different with ap =<NUM>.

The performance of the method of the present disclosure was further compared with existing methods namely Percentage change in pupil diameter (PCPD), perimeter-area ratio (PAR) and form factor (FF) to distinguish the two levels of cognitive load.

An obvious means of cognitive load assessment in pupillometry is by measuring the percentage change in pupil diameter (PCPD) with respect to that during the baseline portion. Generally, the PCPD increases with increase in cognitive load. Generally PCPD increases with increase in cognitive load. Accordingly, this metric was used for comparison with the method of the present disclosure.

Cognitive load may also be estimated based on the nature of visual field of the eyes during the stimulus. During the data capture phase, the eye tracker captures noisy (x,y) coordinates of the gaze on the screen. S represents a set of all captured coordinates over a period of time and the set S is represented through equation (<NUM>) <MAT> where n is the number of samples in the signal. The missing data due to blinks in the set S are adjusted with interpolation based on previous and subsequent data samples. The perimeter-area ratio (PAR), was computed to estimate the cognitive load, given by equation (<NUM>), <MAT> where the constant <NUM> returns a value of <NUM> in case of circles, and decreases as the shape gets compact. A zero value signifies a line having zero area.

The FF method is given by equation (<NUM>), <MAT>.

The cognitive load values were computed for each trial portion for the low and high load tasks for each participant and the average values are reported in <FIG>, <FIG> and <FIG> wherein <FIG> illustrates the percentage change in pupil diameter (PCPD), as known in the art, averaged over all the trial portions; <FIG> illustrates the perimeter-area ratio (PAR), as known in the art, averaged over all the trial portions; and <FIG> illustrates the form factor (FF), as known in the art, averaged over all the trial portions respectively. It is evident that there is no consistent pattern for distinguishing the two levels of loads imparted on the participants. For instance, PCPD failed to distinguish the low and high levels of cognitive load for few participants namely, <NUM>, <NUM>, <NUM>, <NUM> and <NUM>, where reverse trends are observed. For PAR, though there is a considerable amount of separation between the two tasks, the reverse pattern is observed for participants <NUM>, <NUM>, <NUM>, <NUM>, <NUM> and <NUM>. Similar results are also obtained with FF across the participants.

In order to further evaluate separation for the low and high load tasks, the discriminating index (DI) was computed per trial i, as given in equation (<NUM>), <MAT> where D is the estimated cognitive load metric in accordance with the present disclosure or other metrics like PCPD, PAR or FF. The time duration for both the loads is same and hence, d is computed per trial portion in each of the cases. Next, the total discriminating index TDI is computed as given in equation (<NUM>), <MAT>.

Table I below shows the TDI values for the estimated cognitive load metric in accordance with the present disclosure and the state of the art methods (averaged across the participants).

It is evident from the results in the table that the method of the present disclosure outperforms the existing metrics.

As part of further validation, tests were conducted to investigate the relation between the method of the present disclosure and Intelligence Quotient (IQ) of the participants. The IQ level of an individual is known to be inversely proportional to the overall cognitive load. Higher the IQ, better is the management of mental resources and hence, less is the total cognitive load. In order to check if the metric of the present disclosure is reliable enough, the total cognitive load was compared with IQ levels of the participants. As part of the experimental procedure, an online IQ test by Brainmetrix™ was taken by the participants. <FIG> illustrates the variations in estimated cognitive load (i.e. cognitive load for high load task - cognitive load for low load task) in accordance with an embodiment of the present disclosure versus IQ levels. For ease of analysis, the participants were sorted in increasing values of IQ. It was seen that for participants having higher IQ levels, the difference in the load imposed is less, indicating that the cognitive load for high load is relatively less. Thus, the method of the present disclosure conforms to the variations in cognitive load with IQ. This also shows the ability of the method of the present disclosure to handle subjective differences of the users under test.

Cognitive load is imposed during all the day-to-day activities and tasks that require usage of working memory and the related resources. In accordance with the present disclosure, systems and methods of the present disclosure provide a metric for the estimation of cognitive load based on the dilating nature of pupil size. A mental addition task was used with two variants - easy and difficult to induce low and high loads, respectively, on the participants. The metric is based on the mean frequency and power of dilation of the pupil size for both the low and the high load tasks. It was seen that there is substantial amount of separation achieved in both the cases and the nature of separation is consistent for all the participants. The method of the present disclosure was also compared with the state-of-the art methods that are based on the percentage change in pupil dilation (PCPD) and the visual field metrics like perimeter-area ratio (PAR) and form factor (FF). It is noted that the method of the present disclosure outperforms the rest of the approaches in distinguishing the two levels of cognitive loads. Also, there was an inverse relation observed in case of the difference in the high and low loads vs the IQ levels of the participants. This shows that the candidates with higher IQ levels feel less load during the high load tasks in conjunction to the low load task.

The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g. hardware means like e.g. an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. Alternatively, the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs.

The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various modules described herein may be implemented in other modules or combinations of other modules.

Such alternatives fall within the scope of the disclosed embodiments Also, the words "comprising," "having," "containing," and "including," and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items.

Claim 1:
A processor implemented method (<NUM>) comprising:
capturing a cognitive task evoked pupillary response in the form of raw pupil size data associated with a subject and metadata associated with the raw pupil size data, wherein the capturing is based on remote eye tracking (<NUM>);
pre-processing the captured raw pupil size data to obtain filtered data (<NUM>), characterized in that:
performing frequency domain analysis on the filtered data (<NUM>) to obtain cognitive load L(t) by:
segmenting the filtered data to obtain baseline and trail portions based on time stamps extracted from the metadata associated with the raw pupil size data,
computing mean frequency f(ω) of the filtered data based on (i) number of frequency bins n, (ii) frequency band ωi of each of the frequency bins i, and (iii) frequency fωi and energy density Iωi associated with each of the frequency bins as given below <MAT>
computing a cognitive load L(t) based on the mean frequency f(ω) and power p(ω) corresponding to the mean frequency f(ω) (206b) as given below <MAT>
wherein the cognitive load L(t) is computed for trial blink portion for low and a high cognitive load tasks, wherein said cognitive load tasks are mental addition tasks using ten single digit- numbers from <NUM>-<NUM> for the low cognitive load task and using ten numbers from <NUM>-<NUM>, excluding numbers <NUM>, <NUM>, <NUM>, for the high cognitive load task;
computing a discriminating index di per trial portion i, as given below <MAT>
where D is the cognitive load L(t) computed for high and low cognitive tasks, and
computing a total discriminating index, TDI evaluate separation of the low and high cognitive tasks as given below <MAT>