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

Gestures are used in day to day life like nodding and waving without us being aware of them. It has become an important part in the communication among the humans. In the recent years new methods of Human Computer Interaction (HCI) are being developed. Some of them are based on interaction with machines through hand, head, facial expressions, voice, touch and many are still the current topic of research. However relying on just one of them reduces the accuracy of the whole HCI and is also limiting the options available to users. The objective of this paper is thus to use two of the important modes of interaction - hand and head to control any application running on computer using Computer Vision algorithms. From input video stream, hand is segmented and the corresponding gesture is being recognized based on the shape and pattern of movement of hand. For head gesture recognition, head is first detected and then optical flow method is used to get the movement of head which is then recognized by finite state automata. Using the user interface of the software, an operator can control any interactive application (say VLC player, Image browser etc) using hand and head gestures which in turn are automatically mapped to the mouse and keyboard events through Windows API. The proposed multimodal approach is particularly useful to communicate with computers and other electronic appliances from a distance where mouse and keyboard are not convenient to work with.

Vision-based multimodal human-computer interaction using hand and head gestures

Real Time Gesture Recognition System for Interaction in Dynamic Environment

Capsule neural networks on spatio-temporal EEG frames for cross-subject emotion recognition

ideo surveillance has gained importance in law enforcement, security and military applications The system consists of processing steps such as object detection, movement tracking, and activity monitoring The paper contribution is to present the human activity analysis system that both detect a human with carrying or abandoning an object and segments the object from the human so that it can be tracked Segmentation of objects is done from the background using advance Gaussian mixture model The tracking algorithm considers the human as whole from frame to frame, it does not track the human parts such as limbs Object features such as center of mass, size, and bounding box are used in this paper to estimate a matching between objects in consecutive frames As the object is segmented and tracked, Bayesian inference framework is used for event analysis This system uses a single camera view and unusual activity is detected using the detected objects and object tracking result The operator is notified if an unusual activity is detected

Unusual activity detection for video surveillance

In this paper, we present an approach to interactive out-of-core volume data exploration that has been developed to augment the existing capabilities of the LhpBuilder software, a core component of the European project LHDL ( http://www.biomedtown.org/biomed_town/lhdl ). The requirements relate to importing, accessing, visualizing and extracting a part of a very large volume dataset by interactive visual exploration. Such datasets contain billions of voxels and, therefore, several gigabytes are required just to store them, which quickly surpass the virtual address limit of current 32-bit PC platforms. We have implemented a hierarchical, bricked, partition-based, out-of-core strategy to balance the usage of main and external memories. A new indexing scheme is introduced, which permits the use of a multiresolution bricked volume layout with minimum overhead and also supports fast data compression. Using the hierarchy constructed in a pre-processing step, we generate a coarse approximation that provides a preview using direct volume visualization for large-scale datasets. A user can interactively explore the dataset by specifying a region of interest (ROI), which further generates a much more accurate data representation inside the ROI. If even more precise accuracy is needed inside the ROI, nested ROIs are used. The software has been constructed using the Multimod Application Framework, a VTK-based system; however, the approach can be adopted for the other systems in a straightforward way. Experimental results show that the user can interactively explore large volume datasets such as the Visible Human Male/Female (with file sizes of 3.15/12.03 GB, respectively) on a commodity graphics platform, with ease.

Anupam Agrawal

Papers

Vision-based multimodal human-computer interaction using hand and head gestures

Real Time Gesture Recognition System for Interaction in Dynamic Environment

Capsule neural networks on spatio-temporal EEG frames for cross-subject emotion recognition

Unusual activity detection for video surveillance

Enabling the interactive display of large medical volume datasets by multiresolution bricking