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Human Activities

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Fight Recognition in video using Hough Forests and 2D Convolutional Neural Network.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
While action recognition has become an important line of research in computer vision, the recognition of particular events such as aggressive behaviors, or fights, has been relatively less studied. These tasks may be extremely useful in several video...

Unobtrusive Activity Recognition of Elderly People Living Alone Using Anonymous Binary Sensors and DCNN.

IEEE journal of biomedical and health informatics
Elderly population (over the age of 60) is predicted to be 1.2 billion by 2025. Most of the elderly people would like to stay alone in their own house due to the high eldercare cost and privacy invasion. Unobtrusive activity recognition is the most p...

Deep Learning for Fall Detection: Three-Dimensional CNN Combined With LSTM on Video Kinematic Data.

IEEE journal of biomedical and health informatics
Fall detection is an important public healthcare problem. Timely detection could enable instant delivery of medical service to the injured. A popular nonintrusive solution for fall detection is based on videos obtained through ambient camera, and the...

Deep Recurrent Neural Networks for Human Activity Recognition.

Sensors (Basel, Switzerland)
Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples ...

A Novel Energy-Efficient Approach for Human Activity Recognition.

Sensors (Basel, Switzerland)
In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy con...

Matrix and Tensor Completion on a Human Activity Recognition Framework.

IEEE journal of biomedical and health informatics
Sensor-based activity recognition is encountered in innumerable applications of the arena of pervasive healthcare and plays a crucial role in biomedical research. Nonetheless, the frequent situation of unobserved measurements impairs the ability of m...

Deeply Learned View-Invariant Features for Cross-View Action Recognition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Classifying human actions from varied views is challenging due to huge data variations in different views. The key to this problem is to learn discriminative view-invariant features robust to view variations. In this paper, we address this problem by...

Learning a Deep Model for Human Action Recognition from Novel Viewpoints.

IEEE transactions on pattern analysis and machine intelligence
Recognizing human actions from unknown and unseen (novel) views is a challenging problem. We propose a Robust Non-Linear Knowledge Transfer Model (R-NKTM) for human action recognition from novel views. The proposed R-NKTM is a deep fully-connected ne...

A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices.

IEEE journal of biomedical and health informatics
The increasing popularity of wearable devices in recent years means that a diverse range of physiological and functional data can now be captured continuously for applications in sports, wellbeing, and healthcare. This wealth of information requires ...

Scaling Laws in City Growth: Setting Limitations with Self-Organizing Maps.

PloS one
Do scaling relations always provide the means to anticipate the relationships between the size of cities, costs of maintenance, and the socio-economic benefits resulting from their growth? Scaling laws are considered a universal principle that descri...