WAVE: Secure Wireless Pairing Exploiting Human Body Movements

Author(s):  
Wei Wang ◽  
Zhan Wang ◽  
Wen Tao Zhu ◽  
Lei Wang
Keyword(s):  
Author(s):  
Elen Vogman

The Soviet Union of the 1920s produces and supports multiple connections between the policy of work in factories and the research in medical, neurological, and collective physiology. The theatrical and cinematic work of S. M. Eisenstein forms a specific prism where these interconnections appear in a spectrum of concrete attempts to engage the factory as an aesthetic and political model. The factory as a concrete topos which Eisenstein exploits in Gas Masks and Strike questions the interrelations between the human body and machine in a new iconology of a striking factory. For the duration of the Strike, the factory is represented beyond any functionality: the workers’ body movements and gestures are all the more expressive the less they have to do with their everyday work. This modulated status of production appears in Capital, Eisenstein’s unfulfilled project to realize Marx’s political economy with methods of inner monologue invented by Joyce. This last project transfigures the factory strike into the structure of cinematographic thinking where the neuro-sensorial stimuli constantly strike the logic of the everyday consciousness in the non-personal, polyphonic, and intimate monologue.


2017 ◽  
Vol 14 (1) ◽  
pp. 79-83
Author(s):  
Man Wang

“Link stress analysis method” is a method to determine to cause the original dynamic muscle of the body movements. In the basketball sports and training, it gets more and more attention. The writer tries to base the teaching practice, to make some supplement and discussion of the theoretical definition, the methods of the analysis of the original muscle and the problems that should be noticed.


2017 ◽  
Author(s):  
Beau Sievers ◽  
Caitlyn Lee ◽  
William Haslett ◽  
Thalia Wheatley

People express emotion using their voice, face and movement, as well as through abstract forms as in art, architecture and music. The structure of these expressions often seems intuitively linked to its meaning: e.g., romantic poetry is written in flowery curlicues, while the logos of death metal bands use spiky script. Here we show that these associations are universally understood because they are signaled using a multi-sensory code for emotional arousal. Specifically, variation in the central tendency of the frequency spectrum of a stimulus—its spectral centroid—is used by signal senders to express emotional arousal, and by signal receivers to make emotional arousal judgments. We show that this code is used across sounds, shapes, speech, and human body movements, providing a strong multi-sensory signal that can be used to efficiently estimate an agent’s level of emotional arousal.


Author(s):  
Lazăr Tipa

Introduction. The human body movements, in our day by day life, conscious or/and subconscious, accompany us permanently and they are presented in various ways. The movement accompanies our life, life is accompanied the movement, healthy mind in a healthy body. ”Menssana in corporesano”.


2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Mingming Zhang ◽  
Lu Yu ◽  
Keye Zhang ◽  
Bixuan Du ◽  
Bin Zhan ◽  
...  

Abstract Human body movements can convey a variety of emotions and even create advantages in some special life situations. However, how emotion is encoded in body movements has remained unclear. One reason is that there is a lack of public human body kinematic dataset regarding the expressing of various emotions. Therefore, we aimed to produce a comprehensive dataset to assist in recognizing cues from all parts of the body that indicate six basic emotions (happiness, sadness, anger, fear, disgust, surprise) and neutral expression. The present dataset was created using a portable wireless motion capture system. Twenty-two semi-professional actors (half male) completed performances according to the standardized guidance and preferred daily events. A total of 1402 recordings at 125 Hz were collected, consisting of the position and rotation data of 72 anatomical nodes. To our knowledge, this is now the largest emotional kinematic dataset of the human body. We hope this dataset will contribute to multiple fields of research and practice, including social neuroscience, psychiatry, computer vision, and biometric and information forensics.


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