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Human activity recognition based on LPA
Multimedia Tools and Applications, 2020Human activity recognition and fall detection have been popular research topics because of its wide area of application. Traditional activity recognition methods have complex feature extraction steps. We propose a new feature extraction method based on linear prediction analysis(LPA) to reduce computational complexity involved with engineering features.
Ruixiang Li, Hui Li, Weibin Shi
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Human Activity Recognition with Capsule Networks
2021Human activity recognition is a challenging problem, where deep learning methods are showing to be very efficient. In this paper we propose the use of capsule networks. This type of networks have proved to generalize better to novel viewpoints than convolutional neural networks.
Laura Llopis-Ibor +3 more
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Human Activity Recognition: A review
2022 10th International Symposium on Digital Forensics and Security (ISDFS), 2022João Gonçalo Pereira +1 more
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1997
A fundamental goal of work in recognition is to discover easily-computed visual features which are efficient indices of members of the class which is to be recognized. The hypothesis behind work in motion-based recognition is that features describing motion in the input can be efficient indices for large classes of objects and activities of interest to
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A fundamental goal of work in recognition is to discover easily-computed visual features which are efficient indices of members of the class which is to be recognized. The hypothesis behind work in motion-based recognition is that features describing motion in the input can be efficient indices for large classes of objects and activities of interest to
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Special section on human activity recognition
Pervasive and Mobile Computing, 2012[No abstract available]
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A Survey on Deep Learning for Human Activity Recognition
ACM Computing Surveys, 2022Fuqiang Gu +2 more
exaly
Human Activity Recognition with Wearable Sensors
2008This thesis investigates the use of wearable sensors to recognize human activity. The activity of the user is one example of context information -- others include the user's location or the state of his environment -- which can help computer applications to adapt to the user depending on the situation.
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