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

Seizures are the part of the epilepsy that occurs in central nervous system which leads abnormal brain activity. Electroencephalogram (EEG) signal recordings are mostly used in epileptic seizure de...

A Multi-View SVM Approach for Seizure Detection from Single Channel EEG Signals

Brain Computer Interface technology enables a pathway for analyzing EEG signals for seizure detection. EEG signal decomposition, features extraction and machine learning techniques are more familiar in seizure detection. However, selecting decomposition technique and concatenation of their features for seizure detection is still in the state-of-the-art phase. This work proposes DWT-EMD Feature level Fusion-based seizure detection approach over multi and single channel EEG signals and studied the usability of discrete wavelet transform (DWT) and empirical mode decomposition (EMD) feature fusion with respect to individual DWT and EMD features over classifiers SVM, SVM with RBF kernel, decision tree and bagging classifier for seizure detection. All classifiers achieved an improved performance over DWT-EMD feature level fusion for two benchmark seizure detection EEG datasets. Detailed quantification results have been mentioned in the Results section.

/pdf/dwt-emd-feature-level-fusion-based-approach-over-multi-and-263axhkw.pdf

DWT-EMD Feature Level Fusion Based Approach over Multi and Single Channel EEG Signals for Seizure Detection

The field of Emotion recognition (ER) is a part of human-computer interaction and this field has evolved very rapidly since the last decade. There are several works which have been done on emotion recognition using audio and video, however recently work is being done on fusion of the different modalities. The aim of this paper is to fuse the results of emotion detection obtained using audio and visual modalities to increase the amount of accuracy in emotion detection. Four emotions are addressed in this paper – angry, sad, happy and disgust. The face is extracted and the control points and muscle vectors of the face are calculated. Using this data, the features are extracted and then the SVM classifier is used to classify the emotion present in the video sample. For the audio module, different prosodic features like pitch, formant frequency, energy and MFCC are computed for feature extraction. The global feature vector is formed and then the SVM classifier for audio is used for the final result. Both the results are then fused using the F-score technique. This F-score value is used for the formation of the decision matrix which gives the fused emotion of the two modules. We checked the results of fusion on SAVEE dataset as well as on our own created dataset. We obtained correct results for emotion after fusion even in those cases where either video or audio mode failed to detect the correct emotion. The tool we have developed using the fusion approach is able to recognize emotions in real-time.

Fusion Based Emotion Recognition System

Human-computer interaction is a growing research area. There are several ways of interaction with the computer. Handwriting has continued to persist as a means of communication and recording information in the day to day life even with the introduction of new technologies. Due to the growth of technology in India, it becomes important to devise ways that allow people to communicate with computer in Indian languages. Hindi being the national language of India, we present a way to communicate with the computer in Hindi or more precisely, 'Devanagari script'. Due to absence of a global font to represent Devanagari characters, it is important that the computer recognizes the characters written by the user in order to interact with him. The algorithm implemented for character recognition first segments the image containing Devanagari text fed to the software into lines, lines to words and words to characters. The obtained characters are then brought down to a standard size. The Kohonen Neural Network based recognizer then comes into action and recognizes the text character by character and provides the output in Unicode format. The network has been designed with no hidden layer to support quick recognition. Apart from text recognition from an image, we also provided the option to recognize individual handwritten characters drawn using a mouse. Such a system provides keyboard less computer interaction. The technique is implemented using Java. The overall recognition rate for a fixed font machine printed characters is 90.26% and for hand written characters, it is 83.33%.

/pdf/devanagari-character-recognition-towards-natural-human-190f7aonfm.pdf

Devanagari character recognition towards natural human-computer interaction

Identification of people among each other has always been a tough and challenging task for the researchers. There are many techniques which are used for identify ing a person but biometric technique is the standard one which allows us for online identification of individuals on the basis of their physiological and behavioral features. The veins based systems include finger veins, face veins, palm veins, head veins, heart veins, iris, palatal veins of the rogue etc. The multi-veins based systems use the veins of different physiological traits for identifying a person. This paper illustrates an overview of veins based personal identificat ion systems. The perfo rmance of different single and multi-veins based identification systems are analyzed in this paper. The features like reliability, security, accuracy, robustness and long term stability along with the strengths and weaknesses of various veins based biometric approaches were taken into considerations while analyzing the results of existing research papers published so far. At last the future research directions in the field of veins based identification systems have also been outlined.

/pdf/veins-based-personal-identification-systems-a-review-5eccieg0b8.pdf

Anupam Agrawal

Papers

A Multi-View SVM Approach for Seizure Detection from Single Channel EEG Signals

DWT-EMD Feature Level Fusion Based Approach over Multi and Single Channel EEG Signals for Seizure Detection

Fusion Based Emotion Recognition System

Devanagari character recognition towards natural human-computer interaction

Veins Based Personal Identification Systems: A Review