Sequential Human Behavior Recognition for Cooking-Support Robots

2005 ◽  
Vol 17 (6) ◽  
pp. 717-724 ◽  
Author(s):  
Tsukasa Fukuda ◽  
◽  
Yasushi Nakauchi ◽  
Katsunori Noguchi ◽  
Takashi Matsubara ◽  
...  
Keyword(s):  
Information Technology ◽  
Activity Recognition ◽  
Human Behavior ◽  
Experimental Results ◽  
Behavior Recognition ◽  
Household Appliances ◽  
Recent Advances ◽  
The Subject ◽  
Human Behavior Recognition

Recent advances in information technology are making electric household appliances computerized and networked. If our environments could intuit our activities, e.g., by sensors, novel services taking anticipated actions into account would become possible. We propose activity recognition that infers a subject’s next action based on previously observed behaviors. We developed a cooking-support robot that suggests by voice and gesture what the subject may want to do next. Experimental results confirmed feasibility of the inference and the quality of support.

Human Intention Detection and Activity Support System for Ubiquitous Sensor Room

2004 ◽  
Vol 16 (5) ◽  
pp. 545-551 ◽  
Author(s):  
Yasushi Nakauchi ◽  
◽  
Katsunori Noguchi ◽  
Pongsak Somwong ◽  
Takashi Matsubara ◽  
...  
Keyword(s):  
Human Behavior ◽  
Support System ◽  
Learning System ◽  
Experimental Results ◽  
Behavior Recognition ◽  
Behavior Detection ◽  
Human Intention ◽  
Rf Tag ◽  
Meaningful Behavior

In this paper, we propose the human behavior detection and activity support environment Vivid Room. Behavior in Vivid Room is detected by numerous sensors built into the room, i.e., magnet sensors for doors and drawers, microswitches for chairs, and ID tags for personnel, and information is collected by a sensor server via an RF tag system and LAN. To recognize meaningful behavior, e.g., studying, eating, and resting, we use ID4-based learning system. We also developed activity support using sound and voice taking into account human behavior in the room. Experimental results confirmed the accuracy of behavior recognition and the quality of support.

3D Human behavior recognition based on binocular vision and face–hand feature

Optik ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 4712-4717 ◽  
Author(s):  
Qing Ye ◽  
Junfeng Dong ◽  
Yongmei Zhang
Keyword(s):  
Human Behavior ◽  
Binocular Vision ◽  
Behavior Recognition ◽  
Human Behavior Recognition

Based on spatio-temporal graph convolution networks with residual connection for intelligence behavior recognition

2021 ◽  
pp. 002072092199660
Author(s):  
Yinong Zhang ◽  
Shanshan Guan ◽  
Cheng Xu ◽  
Hongzhe Liu
Keyword(s):  
Human Behavior ◽  
Behavior Recognition ◽  
Convolutional Network ◽  
Skeleton Model ◽  
Basic Technology ◽  
Temporal Model ◽  
Temporal Graph ◽  
Spatio Temporal ◽  
Human Behavior Recognition ◽  
Behavior Characteristics

In the era of intelligent education, human behavior recognition based on computer vision is an important branch of pattern recognition. Human behavior recognition is a basic technology in the fields of intelligent monitoring and human-computer interaction in education. The dynamic changes of human skeleton provide important information for the recognition of educational behavior. Traditional methods usually use manual information to label or traverse rules only, resulting in limited representation capabilities and poor generalization performance of the model. In this paper, a kind of dynamic skeleton model with residual is adopted—a spatio-temporal graph convolutional network based on residual connections, which not only overcomes the limitations of previous methods, but also can learn the spatio-temporal model from the skeleton data. In the big bone NTU-RGB + D dataset, the network model not only improved the representation ability of human behavior characteristics, but also improved the generalization ability, and achieved better recognition effect than the existing model. In addition, this paper also compares the results of behavior recognition on subsets of different joint points, and finds that spatial structure division have better effects.

SVM-HMM Based Human Behavior Recognition

2015 ◽  
pp. 93-103 ◽  
Author(s):  
Shanshan Han ◽  
Minfei Zhang ◽  
Penglin Li ◽  
Jinjie Yao
Keyword(s):  
Human Behavior ◽  
Behavior Recognition ◽  
Human Behavior Recognition
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