Arijit Sinharay

EEG-based fuzzy cognitive load classification during logical analysis of program segments

The paper aims at designing a novel scheme for cognitive load classification of subjects engaged in program analysis. The logic of propositions has been employed here to construct program segments to be used for cognitive load analysis and classification. Electroencephalogram signals acquired from the subjects during program analysis are first fuzzified and the resultant fuzzy membership functions are then submitted to the input of a fuzzy rule-based classifier to determine the class of the cognitive load of the subjects. Experimental results envisage that the proposed classifier has a good classification accuracy of 86.2%. Performance analysis of the fuzzy classifier further reveals that it outperforms two most widely used classifiers: Support Vector Machine and Naive Bayes classifier.

Evaluation of Different Onscreen Keyboard Layouts Using EEG Signals

The paper aims at evaluation of different onscreen keyboard layouts based on the biological responses of the users. The signal used for the said purpose is Electroencephalogram acquired by low cost neuro-headset from Emotiv. We propose to use human cognition as the fundamental feature to discriminate between user-friendly vs. cumbersome onscreen layout designs. To validate our observations we compared our results with bench marked data based on user study and KLM-GOMS model. A classifier is first trained for high and low cognition tasks based on well-established cognitive tests (e.g. Stroop test) and then this classifier is used to report the cognition class for a particular onscreen layout. A high cognition load class indicates complexity in the layout design whereas a low cognition output indicates the layout to be user friendly. Present evaluation methods like user study or KLM-GOMS based model, serves as an indirect measure of goodness of layout designs. In contrast, our approach has a unique advantage as this is a direct measure of humans biological response subjected to stimuli (in our case onscreen keyboard layouts) hence more reliable.

Stereo Vision Based Pedestrians Detection and Distance Measurement for Automotive Application

This paper presents a compact and robust solution to use stereo vision to detect pedestrians as well as measure their instantaneous distances from the vehicle. This simplifies sensor requirements as the same video information (captured from two cameras) is used for detection of pedestrians as well as measurement of their corresponding distances. To apply stereo vision technique a novel approach has been made to locate identical points on human so that triangulation can be used for measuring distances. In addition to this, the paper also presents a novel technique used to make the pedestrian detection algorithm (based on well know Viola-Jones methodology) more robust by using tracking and assistive decision making (i.e. one camera helps other to eliminate noise). The proposed system is tested on standard x86 machine and gives good real time performance.

Smartphone Based Digital Stethoscope for Connected Health -- A Direct Acoustic Coupling Technique

Mobile smartphones have revolutionized the concept of mobile phones as different apps are built to offer various interesting applications in healthcare, gaming, etc. rather than using the phone only for voice services. The application developers take advantage of onboard sensors, web connectivity and powerful processing units of the smartphones to develop such interesting apps. In this paper, we present an interesting approach where direct acoustic coupling technique is employed to quickly and conveniently convert the smartphones into high quality digital stethoscope by using an ultra-low cost attachment. The design consideration primarily focused on affordability, simplicity and use-friendly aspects into account. The motivation of this work is to enable the heart patients to send their heart sound to doctors chamber/ hospitals from their home instead of travelling all the way to hospitals/clinics. This is particularly useful in poor or developing countries where there is scarcity of healthcare centers and patients have to travel a long distance to visit the clinic. The situation becomes more serious in case of patients who went through heart surgery and requires follow up visits or for elderly population requiring routine checkups. For both the cases, in many instances, doctors primarily listen to the heart sound to prescribe further actions. Our solution can greatly help such situations and in many cases it can completely eliminate the travel. In addition, since data can also be recorded and saved, this opens up possibility for statistical analysis to further aid the diagnosis / monitoring. Thus, our solution can serve as a viable tool in connected health use cases for heart patients for needy geography.

Cognitive load measurement - A methodology to compare low cost commercial EEG devices

Use of EEG signals in measuring cognitive load is a widely practiced area and falls under Brain-Computer-Interfacing (BCI) technology. However this technology uses medical grade EEG devices that are expensive as well as not user-friendly for regular use. Recent launch of low cost wireless EEG headsets from different companies opens up the possibility for commercialization of BCI and thus drew attention of the research community all over the world. While there are numerous studies on BCI with the use of medical grade devices there are limited numbers of papers reported on those using low cost devices. Moreover, reports on evaluating relative performance of these commercially available EEG devices based on a specific BCI experiment are minuscule. This paper attempts to fill this gap and presents a methodology to compare with various aspects between two widely used low cost wireless EEG devices namely Emotiv and Neurosky for application in cognitive load detection.

Exploiting IMU Sensors for IOT Enabled Health Monitoring

Inertial Measurement Units (IMUs) embedded in commercial mobile devices are a good choice for continuous monitoring in healthcare domain due to their attractive form factor and low power consumption. We present improved and accurate sensing algorithms using a single IMU to sense basic events like step count, stride length, fall, immobility etc. Our algorithms have been shown to perform better than the state of the art algorithms, and are implemented in such a way that IMU is not bound to any specific position or orientation with respect to the user. We propose a 3-layer based framework for a complete end-to-end system architecture for IoT enabled health monitoring, useful for application in areas like individual fitness monitoring and elderly care.

A Kalman Filter Based Approach to De-noise the Stereo Vision Based Pedestrian Position Estimation

This paper presents a methodology of using Kalman filter to minimize the error in stereo vision based distance measurement data (3D position of pedestrians). In stereo vision, little point mis-correspondence leads to a very bad estimate of depth during triangulation. There are robust correspondence algorithms but all of them suffer from algorithm complexity affecting the time performance. If simple correspondence algorithms are used that gave good real time performance, then the results suffer from erroneous depth measurement. In this paper, we have applied a predictive-corrective model using Kalman filter on the erroneous depth measurement. Being applied in time domain as compared to stereo image domain, the proposed approach has much less algorithm complexity and hence gives good real-time performance. The results also show that the proposed algorithm is able to significantly reduce the measurement noise without affecting the pedestrian tracking ability.

The Ultrasonic Directional Tidal Breathing Pattern Sensor: Equitable Design Realization Based on Phase Information

Pulmonary ailments are conventionally diagnosed by spirometry. The complex forceful breathing maneuver as well as the extreme cost of spirometry renders it unsuitable in many situations. This work is aimed to facilitate an emerging direction of tidal breathing-based pulmonary evaluation by designing a novel, equitable, precise and portable device for acquisition and analysis of directional tidal breathing patterns, in real time. The proposed system primarily uses an in-house designed blow pipe, 40-kHz air-coupled ultrasound transreceivers, and a radio frequency (RF) phase-gain integrated circuit (IC). Moreover, in order to achieve high sensitivity in a cost-effective design philosophy, we have exploited the phase measurement technique, instead of selecting the contemporary time-of-flight (TOF) measurement; since application of the TOF principle in tidal breathing assessments requires sub-micro to nanosecond time resolution. This approach, which depends on accurate phase measurement, contributed to enhanced sensitivity using a simple electronics design. The developed system has been calibrated using a standard 3-L calibration syringe. The parameters of this system are validated against a standard spirometer, with maximum percentage error below 16%. Further, the extracted respiratory parameters related to tidal breathing have been found to be comparable with relevant prior works. The error in detecting respiration rate only is 3.9% compared to manual evaluation. These encouraging insights reveal the definite potential of our tidal breathing pattern (TBP) prototype for measuring tidal breathing parameters in order to extend the reach of affordable healthcare in rural regions and developing areas.

A Photoplethysmograph Based Practical Heart Rate Estimation Algorithm for Wearable Platforms

We propose a practical Heart Rate Estimation algorithm utilizing wrist-based photoplethysmography (PPG) signals for continuous health monitoring of crane workers who spend long hours in an isolated cabin in the harsh factory environment. Our novelty lies in devising a low footprint algorithm that can reliably estimate Heart Rate in presence of motion artefact as well as offers the feasibility of deploying on a wearable platform. More particularly, our solution addresses two fundamental issues: a) correcting weak wrist PPG signal from frequent motion artefacts and b) identifying signal processing techniques that can be practically implemented on an embedded platform with limited resources in terms of memory and CPU. Experimental results demonstrate the validity of such algorithm and exhibit a great potential to be employed in the real field.

Analysis of Cognitive Load -- Importance of EEG Channel Selection for Low Resolution Commercial EEG Devices

Measurement of cognitive load using brain signalsis an important area of research in human behavior and psychology. Recently, there have been attempts to use low cost, commercially available Electroencephalogram (EEG) devices for the analysis of the cognitive load. Due to the reduced number of leads, these low resolution devices pose major challenges in signal processing as well as in feature extraction. In this paper, we investigate the significant leads or channels that are useful for the analysis of the cognitive load. We use a standard matching test and n-back memory test imparting low and high cognitive loads respectively. The investigation is based on the analysis of variance (ANOVA) of Alpha and Theta frequency band signals for various combinations of leads. Comparisons have been done between the previously reported leads and those obtained using a few feature selection algorithms. Results indicate that for a given stimulus, though the significant leads are very much dependent on the subjects, the leads corresponding to the left frontal lobe and right parieto-occipital lobe are in general most significant across majority of subjects for analysis of the cognitive load.