There is a growing demand for flexible and soft electronic devices. In particular, stretchable, skin-mountable, and wearable strain sensors are needed for several potential applications including personalized health-monitoring, human motion detection, human-machine interfaces, soft robotics, and so forth. This Feature Article presents recent advancements in the development of flexible and stretchable strain sensors. The article shows that highly stretchable strain sensors are successfully being developed by new mechanisms such as disconnection between overlapped nanomaterials, crack propagation in thin films, and tunneling effect, different from traditional strain sensing mechanisms. Strain sensing performances of recently reported strain sensors are comprehensively studied and discussed, showing that appropriate choice of composite structures as well as suitable interaction between functional nanomaterials and polymers are essential for the high performance strain sensing. Next, simulation results of piezoresistivity of stretchable strain sensors by computational models are reported. Finally, potential applications of flexible strain sensors are described. This survey reveals that flexible, skin-mountable, and wearable strain sensors have potential in diverse applications while several grand challenges have to be still overcome.

Stretchable, Skin-Mountable, and Wearable Strain Sensors and Their Potential Applications: A Review

The demand for flexible and wearable electronic devices is increasing due to their facile interaction with human body. Flexible, stretchable and wearable sensors can be easily mounted on clothing or directly attached onto the body. Especially, highly stretchable and sensitive strain sensors are needed for the human motion detection. Here, we report highly flexible, stretchable and sensitive strain sensors based on the nanocomposite of silver nanowire (AgNW) network and PDMS elastomer in the form of the sandwich structure (i.e., AgNW thin film embedded between two layers of PDMS). The AgNW network-elastomer nanocomposite based strain sensors show strong piezoresistivity with tunable gauge factors in the ranges of 2 to 14 and a high stretchability up to 70%. We demonstrate the applicability of our high performance strain sensors by fabricating a glove integrated with five strain sensors for the motion detection of fingers and control of an avatar in the virtual environment.

Highly Stretchable and Sensitive Strain Sensor Based on Silver Nanowire–Elastomer Nanocomposite

As computers become more pervasive in society, facilitating natural human---computer interaction (HCI) will have a positive impact on their use. Hence, there has been growing interest in the development of new approaches and technologies for bridging the human---computer barrier. The ultimate aim is to bring HCI to a regime where interactions with computers will be as natural as an interaction between humans, and to this end, incorporating gestures in HCI is an important research area. Gestures have long been considered as an interaction technique that can potentially deliver more natural, creative and intuitive methods for communicating with our computers. This paper provides an analysis of comparative surveys done in this area. The use of hand gestures as a natural interface serves as a motivating force for research in gesture taxonomies, its representations and recognition techniques, software platforms and frameworks which is discussed briefly in this paper. It focuses on the three main phases of hand gesture recognition i.e. detection, tracking and recognition. Different application which employs hand gestures for efficient interaction has been discussed under core and advanced application domains. This paper also provides an analysis of existing literature related to gesture recognition systems for human computer interaction by categorizing it under different key parameters. It further discusses the advances that are needed to further improvise the present hand gesture recognition systems for future perspective that can be widely used for efficient human computer interaction. The main goal of this survey is to provide researchers in the field of gesture based HCI with a summary of progress achieved to date and to help identify areas where further research is needed.

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Vision based hand gesture recognition for human computer interaction: a survey

Hand and Mind: What Gestures Reveal about Thought. David McNeill. Chicago and London: University of Chicago Press, 1992. 416 pp.

Hand and Mind: What Gestures Reveal about Thought

The available versions of the Vandenberg and Kuse (1978) Mental Rotations Test (MRT) have physically deteriorated because only copies of copies are available. We report results from a redrawn version of the MRT and for alternate versions of the test. Males perform better than females, and students drawn from the physical sciences perform better than students drawn from the social sciences and humanities, confirming other reports with the original version of the MRT. Subjects find it very hard to perform the MRT when stimuli require rotation along both the top/bottom axis and the left/right axis. The magnitude of effect sizes for sex (which account, on average, for some 20% of the variance) does not increase with increasing difficulty of the task. Minimal strategy effects were observed and females did not perform differently during the menstrual period as opposed to the days between the menstrual periods. Practice effects are dramatic, confirming other reports with the original MRT, and can also be shown to be powerful in a transfer for practice paradigm, where test and retest involve different versions of the MRT. Main effects of handedness on MRT performance were not found.

A redrawn Vandenverg and Kuse mental rotations test : different versions and factors that affect performance

In this paper, a comparative analysis has been done to estimate a robust classifier to classify motor imagery EEG data. First, segment detection and feature extraction have been done over the raw EEG data. Then the frequency domain features have been extracted using FFT. Six classifiers DNN, SVM, KNN, Naive Bayes,’ Random Forest, and Decision Tree have been considered for this study. The DNN model configured with four layers and used the binary cross-entropy loss function and sigmoid activation function for all layers. The optimizer used is “Adam” having the default-learning rate of 0.001. In this experiment, for the purpose of the estimation of the performance of various classifiers, the experiment used dataset IVa from BCI Competition III, which consisted of EEG signal data for five subjects, namely ‘aa,’ ‘al,’ ‘av,’ ‘aw,’ and ‘ay.’ The highest average accuracy of 70.32% achieved by the DNN model, whereas the model achieved an accuracy of 80.39% over the subject ‘aw.’ The objective of this experiment encompasses the different models for the classification of various motor tasks from EEG signals.

Performance Estimation and Analysis Over the Supervised Learning Approaches for Motor Imagery EEG Signals Classification

The purpose of this work is to solve the challenges faced by us in the field of hate speech recognition on various social media platforms, that is to get a better machine learning model that can detect hate speech with greater accuracy. As the reach of the internet and mobile phones has extended abruptly in the past few years, everyone has the power to share their opinions, but some use it as an opportunity to spread hate among one another. In this paper, we used the Davidson [10] dataset which is the most popular Twitter dataset for hate speech detection, and further we implemented various machine learning-based algorithms and compared them on the basis of various parameters such as accuracy, precision score, recall and F1 scores. After the study, we found out that XGBoost when used with TF-IDF transformer embedding gave us an accuracy of 94.43%, which is the maximum among these three models for the given benchmark dataset.

Comparative Analysis of Different Machine Learning Methods for Hate Speech Recognition in Twitter Text Data

Multi-Level Manifolds for Remote Sensing Imagery Classification

Finding Visual Attention in an Image: Learning through Hierarchal Inheritance Approach☆

Road network management is an important task for the economic and social growth. In the context of an Indian city, efficient road network management is required to overcome the problem of traffic congestions and road accidents. Computer traffic simulation is a better way to analysis the road network. In this study a maiden attempt for a systematic empirical traffic simulation study is performed in the perspective of Raipur city in India. The aim of this study is to set up, validate and experiment a simulation model of traffic by using Simulation of Urban Mobility (SUMO). General Terms Computer Simulation, Road Network Management, Modelling.

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Anupam Agrawal

Papers

Performance Estimation and Analysis Over the Supervised Learning Approaches for Motor Imagery EEG Signals Classification

Comparative Analysis of Different Machine Learning Methods for Hate Speech Recognition in Twitter Text Data

Multi-Level Manifolds for Remote Sensing Imagery Classification

Finding Visual Attention in an Image: Learning through Hierarchal Inheritance Approach☆

Empirical Study on Traffic Simulation Model in the Perspective of Raipur City