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

Packing problems on its current state are being utilized for wide area of industrial applications. The aim of present research is to create and implement an intelligent system that tackles the problem of 2D packing of objects inside a 2D container, such that objects do not overlap and the container area is to be maximized. The packing problem becomes easier, when regular/rectangular objects and container are used. In most of the practical situations, the usage of irregular objects comes to existence. To solve the packing problem of irregular objects inside a rectangular container, a hybrid intelligence approach is introduced in our proposed work. The combination of machine intelligence and human intelligence is referred as the hybrid intelligence or semi-automated approach in the proposed methodology. The incorporation of human intelligence in the outcome of machine intelligence is possible to obtain using the internet crowdsourcing as we wish to handle the packing problem through internet crowdsourcing involving rural people. The proposed methodology is tested on different standard data sets and it is observed that it has clear advantage over both manual as well as fully automated heuristic based methods in terms of time and space efficiency.

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A novel hybrid intelligence approach for 2D packing through internet crowdsourcing

Seizure is an unexpected, uncontrollable disturbance in the neuronal activity of the brain in which human being encounters changes in functioning, motion, emotions, and in levels of consciousness. The paper proposes a methodology for the detection of seizures using Electroencephalogram (EEG) signals through Channel Selection. Seizure detection using all the EEG channels leads less detection accuracy as well as high computation. The trade-off between accuracy and computation is done by using channel optimization techniques. The proposed methodology consists of a channel optimization technique which integrates Binary Particle Swarm Optimization (BPSO) with Extreme Learning Machine (ELM) to give the optimized set of channels. The data of the optimized set of channels is then classified using Extreme Learning Machine for gauging its performance. The result obtained from the proposed methodology is compared with the results without channel optimization technique. Also, the result is compared with the combination of channels obtained through maximized and minimized Fitness function. The proposed methodology achieved a maximum detection accuracy of 93.21% with only 6/23 channels, whereas ELM achieved a maximum detection accuracy of 90.27% without channel Selection. Thus, it effectively solves the problem associated with using all the channels and achieves better detection accuracy.

EEG Channel Selection Approach for Seizure Detection Based on Integrated BPSO and ELM

Many tasks involving production software that involve spatial reasoning can only produce "good" rather than optimum solutions. It is frequently possible for humans to improve on algorithmically generated solutions computed by CAD/CAM software. The thesis motivating this research is that large numbers of industrial optimisation tasks involving spatial reasoning (such as 2D part nesting) can be outsourced as human intelligence tasks to rural workers to provide a sustainable source of skilled employment. We hypothesis that 3D spatial reasoning ability is essential to solve spatial optimization tasks. In this paper we reported the 3D spatial manipulation ability of eighty rural workers in four rural business process outsourcing (BPO) centres in India. The assessments reveal that although the average spatial manipulation ability is less than the literature reported benchmark, there are talented workers identified in all the four rural centres, and the results identify priority activities required to enable the proposed approach.

Towards crowdsourcing spatial manufacturing tasks from rural India

In the advancement of Computer graphics tools/libraries used on Web-based services with its associated technologies and multimedia for making user friendly system. In this paper, we have proposed an approach for displaying the 3D Medical volume data visualization on the web-based system. Through which, the 3D volume dataset of many resolution sizes can be visualized onto the web using webGL with HTML5. Here we have used VTK tool for slicing the 3D Medical dataset. In the addition, this system can be accessed by many users at the same time. The 3D volume dataset is visualized with the user's perspective. If he/she wants to select any desired slice then the user can access and fetch that slice. This system has been tested by four different 3D Medical volume datasets with different resolution sizes from 6 MB to 3.15 GB.

Web-based Volume Visualization of 3D Medical Data using Slice Streaming Method

This work projects light upon the challenges of hate speech detection in memes and demonstrates the various machine learning model to automatically detect hate in the internet memes. Memes are the visual content shared on the social media in the form of combination of picture and some textual phrases to depict light humour or jokes. However, some images in the form of memes can also be used to convey misinformation and hate, so their early automatic detection is necessary to stop the hate spreading to wide range of users or population which may cause unrest and harm to human life and property. In this paper, the hateful meme dataset by Facebook AI has been used to test the various unimodal and a multimodal approach to baseline performance for these models and highlight the challenges these hate memes pose to the community.

Anupam Agrawal

Papers

A novel hybrid intelligence approach for 2D packing through internet crowdsourcing

EEG Channel Selection Approach for Seizure Detection Based on Integrated BPSO and ELM

Towards crowdsourcing spatial manufacturing tasks from rural India

Web-based Volume Visualization of 3D Medical Data using Slice Streaming Method

Detection of hate speech in Social media memes: A comparative Analysis