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.

/pdf/vision-based-hand-gesture-recognition-for-human-computer-3dczparvi2.pdf

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

A nonparametric neural network model based on Rough-Fuzzy Membership function, multilayer perception, and back-propagation algorithm is described. The described model is capable to deal with rough uncertainty as well as fuzzy uncertainty associated with classification of remotely sensed multi-spectral images. The input vector consists of membership values to linguistic properties while the output vector is defined in terms of rough fuzzy class membership values. This allows efficient modeling of indiscernibility and fuzziness between patterns by appropriate weights being assigned to the back-propagated errors depending upon the Rough-Fuzzy Membership values at the corresponding outputs. The effectiveness of the model is demonstrated on classification problem of IRS-P6 LISS IV images of Allahabad area. The results are compared with statistical (Minimum Distance), conventional MLP, and FMLP models.

Nonparametric neural network model based on rough-fuzzy membership function for classification of remotely sensed images

Conventional methods for segmentation on Synthetic Aperture Radar (SAR) images aim to provide automatic analysis and interpretation of data.Usually the task gets failed at many cases due to the influence of speckle in the segmented results. The present work proposes a Hybrid framework for segmentation which will provide approachable segmentation results in SAR imagery for the post processing. This framework uses the full advantage of Non Subsampled Contourlet to achieve promising results. The final resultsare analyzed using different metrics and it shows that our proposed framework is providing promoting results in Segmentation of SAR images.

/pdf/framework-for-segmentation-of-sar-imagery-based-on-nsct-and-4x2wtt3gw5.pdf

Framework for Segmentation of SAR imagery based on NSCT and IABC Algorithm

Autism spectrum disorder (ASD) is a neurodevelopmental disorder in which the neurology of an autistic person is severely hampered. A child with autism has difficulty answering his name, avoids eye contact, and cannot express his emotions. Early diagnosis can assist children with ASD enhance their intellectual abilities while reducing autistic symptoms. In computer vision, determining developmental disorder problems from facial image data is a significant but largely unexplored challenge. This paper proposed a method to classify autistic and non-autistic facial images using model 1 (Xception) and model 2 (Augmentation + Xception). Among Model 1 and Model 2, Model 2 achieved higher accuracy of 98% and a minimum loss of 0.08. 

Autism Spectrum Disorder Classification of Facial Images Using Xception Model and Transfer Learning with Image Augmentation

As we all know, COVID-19 appears to be having a terrible impact on world health and well-being. Furthermore, at its peak, the COVID-19 cases worldwide reached a huge number i.e., in millions. The objective of the present work is to develop a model that detects COVID-19 utilizing CT-Scan Image Dataset and DL Techniques. As the number of verified cases rises, it becomes more critical to monitor and precisely categorize healthy and infected people. RT-PCR testing is the most used approach for the detection of Covid-19. However, several investigations have found that it has a low sensitivity in the early stages. Computer tomography (CT) is also used to detect image-morphological patterns of COVID-19-related chest lesions. The RT-PCR technique for diagnosing COVID has some drawbacks. For starters, test kits are insufficiently available, necessitating greater testing time and the sensitivity of testing varies. Therefore, employing CT scan pictures to screen COVID-19 is essential. The results showed that CT scan pictures might efficiently identify COVID-19, saving more lives. A Convolutional Neural Network (CNN) is a sort of Artificial Neural Network that is commonly used for image/object detection with class. An Input layer, Hidden layers, and an Output layer are common components of a neural network (NN). CNN is inspired by the brain's architecture. Artificial neurons or nodes in CNNs, like neurons in the brain, take inputs, process them, and deliver the result as output. Illness severity can be detected and calculated for future scopes and research. Another challenge encountered when dealing with severity infection detection and extending the existing work by using frameworks in order to increase accuracy. The proposed ECNN technique outperformed than CNN in terms of accuracy (95.35), execution time, and performance. This study could be extended or improved in the future by directing severity identification on the CT-Scan image dataset.

Deep Learning for Detection and Prediction of Covid-19 Virus on CT-Scan Image Dataset

Nowadays, depression is one of the most predominant mental health ailments, even causing demise of many lives, especially among the youngsters and middle-aged persons. Use of social media and posting individual expression on it is very much use to for the people. So, social media posts are very useful for detecting a person is in depression or not. In this paper, we have considered depressive and non-depressive tweets classification. We have proposed a sequential Deep learning model with three layers: Embedded, lD-Convolutional and LSTM Layer. The performance of the proposed model has been compared with three traditional Machine Learning models Naïve Bayes, KNN, Random Forest for depressive and non-depressive tweets classification. Our proposed approach of sequential Deep Learning model has achieved 98.47% accuracy, which is better than other models we have compared with.

Anupam Agrawal

Papers

Nonparametric neural network model based on rough-fuzzy membership function for classification of remotely sensed images

Framework for Segmentation of SAR imagery based on NSCT and IABC Algorithm

Autism Spectrum Disorder Classification of Facial Images Using Xception Model and Transfer Learning with Image Augmentation

Deep Learning for Detection and Prediction of Covid-19 Virus on CT-Scan Image Dataset

Depressive and Non-depressive Tweets Classification using a Sequential Deep Learning Model