Abstract-Technology has enabled anyone with an Internet connection to easily create and share their ideas, opinions and content with millions of other people around the world. Much of the content being posted and consumed online is multimodal. With billions of phones, tablets and PCs shipping today with built-in cameras and a host of new video-equipped wearables like Google Glass on the horizon, the amount of video on the Internet will only continue to increase. It has become increasingly difficult for researchers to keep up with this deluge of multimodal content, let alone organize or make sense of it. Mining useful knowledge from video is a critical need that will grow exponentially, in pace with the global growth of content. This is particularly important in sentiment analysis, as both service and product reviews are gradually shifting from unimodal to multimodal. We present a novel method to extract features from visual and textual modalities using deep convolutional neural networks. By feeding such features to a multiple kernel learning classifier, we significantly outperform the state of the art of multimodal emotion recognition and sentiment analysis on different datasets.
Noninvasive on-skin electrodes record the electrical potential changes from human skin, which reflect body condition and are applied for healthcare, sports management, and modern lifestyle. However, current on-skin electrodes have poor conformal properties under sweaty condition in real-life because of decreased electrode-skin adhesion with sweat film at the interface. Here, we fabricated biocomposite electrodes based on silk fibroin (SF) through interfacial polymerization, which is applicable on sweaty skin. Interfacial polymerized conductive polypyrrole (PPy) and SF are structurally interlocked and endow the whole electrode with uniform stretchability. Existence of water results in similar Young's modulus of SF to the skin and enhanced interfacial adhesion. It keeps the electrodes conformal to skin under sweaty condition and allows reliable collection of ambulatory electrophysiological signals during sports and sweating. Wearable devices with these electrodes were used to acquire continuous and stable real-time electrocardiography (ECG) signals during running for 2 h. The collected signals can provide information for sports management and are also analyzed by artificial intelligence to show their potential for intelligent human emotion monitoring. Our strategy provides opportunities to record long-term continuous electrophysiological signals in real-life conditions for various smart monitoring systems.
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