Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction

2019 ◽  
Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiangkun Li ◽  
Guoqing Sun ◽  
Yifei Li

With the development of science and technology, the introduction of virtual reality technology has pushed the development of human-computer interaction technology to a new height. The combination of virtual reality and human-computer interaction technology has been applied more and more in military simulation, medical rehabilitation, game creation, and other fields. Action is the basis of human behavior. Among them, human behavior and action analysis is an important research direction. In human behavior and action, recognition research based on behavior and action has the characteristics of convenience, intuition, strong interaction, rich expression information, and so on. It has become the first choice of many researchers for human behavior analysis. However, human motion and motion pictures are complex objects with many ambiguous factors, which are difficult to express and process. Traditional motion recognition is usually based on two-dimensional color images, while two-dimensional RGB images are vulnerable to background disturbance, light, environment, and other factors that interfere with human target detection. In recent years, more and more researchers have begun to use fuzzy mathematics theory to identify human behaviors. The plantar pressure data under different motion modes were collected through experiments, and the current gait information was analyzed. The key gait events including toe-off and heel touch were identified by dynamic baseline monitoring. For the error monitoring of key gait events, the screen window is used to filter the repeated recognition events in a certain period of time, which greatly improves the recognition accuracy and provides important gait information for motion pattern recognition. The similarity matching is performed on each template, the correct rate of motion feature extraction is 90.2%, and the correct rate of motion pattern recognition is 96.3%, which verifies the feasibility and effectiveness of human motion recognition based on fuzzy theory. It is hoped to provide processing techniques and application examples for artificial intelligence recognition applications.


Author(s):  
Tomaž Vodlan ◽  
Andrej Košir

This chapter presents the methodology for transformation of behavioural cues into Social Signals (SSs) in human-computer interaction that consists of three steps: acquisition of behavioural cues, manual and algorithmic pre-selection of behaviour cues, and classifier selection. The methodology was used on the SS class {hesitation, no hesitation} in the interaction between a user and video-on-demand system. The first step included observation of the user during interaction and obtaining information about behavioural cues. This step was tested on several users. The second step was the manual and algorithmic pre-selection of all cues that occurred into a subset of most significant cues. Different combinations of selected cues were then used in verification process with the aim of finding the combination with the best recognition rate. The last step involved the selection of an appropriate classifier. For example, a logistic regression model was obtained in combination with four features.


2015 ◽  
Vol 66 ◽  
pp. 1-3 ◽  
Author(s):  
Friedhelm Schwenker ◽  
Stefan Scherer ◽  
Louis-Philippe Morency

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