Dual Attention-Based recurrent neural network and Two-Tier optimization algorithm for human activity recognition in individuals with disabilities. [PDF]
Sci RepAlkahtani HK, Mohammed GP, Marzouk R.
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Efficient human activity recognition on edge devices using DeepConv LSTM architectures. [PDF]
Sci RepZhou H +4 more
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Advanced smart human activity recognition system for disabled people using artificial intelligence with snake optimizer techniques. [PDF]
Sci RepAlohali MA +3 more
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Leveraging Artificial Occluded Samples for Data Augmentation in Human Activity Recognition. [PDF]
Sensors (Basel)Mathe E +3 more
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Machine Learning for Human Activity Recognition: State-of-the-Art Techniques and Emerging Trends. [PDF]
J ImagingHossen MA, Abas PE.
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Recognition of Human daily activities
2015 IEEE International Conference on Communication Workshop (ICCW), 2015Capturing the type of physical activity a person is performing thorough his daily life, can inspire the development of new and innovative applications. Examples include monitoring patients' health and physical activity performance, reasoning upon the observed activity to recommend better training strategy, new therapeutic programs, etc. In this work we
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Human activity recognition: A review
2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014), 2014Human Activity Recognition is one of the active research areas in computer vision for various contexts like security surveillance, healthcare and human computer interaction. In this paper, a total of thirty-two recent research papers on sensing technologies used in HAR are reviewed.
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Active Sensing in Human Activity Recognition
2017This work studies the problem of reducing the energy consumption of wearable sensors in a Human Activity Recognition (HAR) system. A HAR system is implemented using Hidden Markov Models, where decisions over the acquisition of new data are made based on the entropy of the posterior distribution of the activities. This problem is intractable in general,
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Continuous human activity recognition
ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004., 2005Effectively recognizing human activities requires at least 32 joint related degrees of freedom to be estimated so as to reliably track the human body in 3D. The particle filter is robust to distracting clutter by maintaining multiple hypotheses for each of these joint angles.
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Rate-Invariant Recognition of Humans and Their Activities
IEEE Transactions on Image Processing, 2009Pattern recognition in video is a challenging task because of the multitude of spatio-temporal variations that occur in different videos capturing the exact same event. While traditional pattern-theoretic approaches account for the spatial changes that occur due to lighting and pose, very little has been done to address the effect of temporal rate ...
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