Diptesh Das

Mobile healthcare infrastructure for home and small clinic

In this paper, the authors describe the software infrastructure built to provide healthcare solution for home and small clinic scenario using mobile healthcare devices. The paper discusses how the data from the devices have been stored into a generic Internet of Things (IoT) backend and then retrieved using web services to provide an interactive portal which provides patient data in a unified manner. The novelty of the approach lies in the use of a mobile device as a gateway to reach the backend IoT platform, which supports application deployment and then applying it to the health-care vertical. The mobile device has hence been used as a pervasive healthcare gateway that collects data from the medical devices using either Bluetooth or Wi-Fi and uploads the data to the back-end server in desired format. Hence the mobile application acts as the edge platform. Several connected healthcare devices like ECG, pulse oxygen meter, body fat analyzer and blood pressure monitor have been used in conjunction with the mobile gateway.

Feature selection by Differential Evolution algorithm - A case study in personnel identification

Feature selection is an important area of research as it has a tremendous effect on the accuracy and performance of classification algorithms. In this paper we propose an objective function for feature selection, which combines the intra class feature variation and inter class feature distance using a Lagrangian multiplier. The inter class distance is measured using the sum of absolute difference of the ratio of mean and standard deviation for respective classes. The objective function is minimized using Differential Evolutionary (DE) Algorithm where the population vector is encoded using Binary Encoded Decimal to avoid the float number optimization problem. An automatic clustering of the possible values of the Lagrangian multiplier provides a detailed insight of the selected features during the proposed DE based optimization process. The classification accuracy of Support Vector Machine (SVM) is used to measure the performance of the selected features. The proposed algorithm outperforms the existing DE based approaches when tested on IRIS, Wine, Wisconsin Breast Cancer, Sonar and Ionosphere datasets. The same algorithm when applied on gait based people identification, using skeleton datapoints obtained from Microsoft Kinect sensor, exceeds the previously reported accuracies.

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.

Unsupervised approach for measurement of cognitive load using EEG signals

Individuals exhibit different levels of cognitive load for a given mental task. Measurement of cognitive load can enable real-time personalized content generation for distant learning, usability testing of applications on mobile devices and other areas related to human interactions. Electroencephalogram (EEG) signals can be used to analyze the brain-signals and measure the cognitive load. We have used a low cost and commercially available neuro-headset as the EEG device. A universal model, generated by supervised learning algorithms, for different levels of cognitive load cannot work for all individuals due to the issue of normalization. In this paper, we propose an unsupervised approach for measuring the level of cognitive load on an individual for a given stimulus. Results indicate that the unsupervised approach is comparable and sometimes better than supervised (e.g. support vector machine) method. Further, in the unsupervised domain, the Component based Fuzzy c-Means (CFCM) outperforms the traditional Fuzzy c-Means (FCM) in terms of the measurement accuracy of the cognitive load.

An interactive system using digital broadcasting and Quick Response code

This paper presents a novel method of creating synchronized interactive TV applications using Quick Response (QR) code, where QR code is used to tag the broadcasted TV content. The QR code is decoded at the receiver side to communicate with an Internet server for interactivity. The proposed technique can be used for both classical analog TV and digital TV. The method described here can also be applied on presently growing mobile TV and Over-the-Top (OTT) TV. It assumes Internet connectivity on the client. We present interactive TV based distance education as an example of a synchronized application using this system.

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.

Stabilization of cluster centers over fuzziness control parameter in component-wise Fuzzy c-Means clustering

This paper proposes an extension of the traditional Fuzzy c-Means algorithm by allowing each component of the datapoints to independently contribute in the decision-making process of determining the cluster membership of the point. The above extension results in an improved accuracy in clustering. The second interesting issue undertaken here is to determine the optimum fuzziness control parameter for stabilization of the cluster centers. Lastly, the proposed extension helps in identifying the important dimensions in characterization of the datapoints. Experimental runs indicate an improvement in accuracy of clustering by the proposed algorithm in comparison to the traditional Fuzzy c-Means, with respect to the measure Fmeasure parameter by 26, 15 and 6 percentage on Colon cancer, Wine and Wisconsin Diagnostic Breast Cancer (WDBC) datasets respectively.