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Human Activities

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Low-Cost and Device-Free Human Activity Recognition Based on Hierarchical Learning Model.

Sensors (Basel, Switzerland)
Human activity recognition (HAR) has been a vital human-computer interaction service in smart homes. It is still a challenging task due to the diversity and similarity of human actions. In this paper, a novel hierarchical deep learning-based methodol...

Sensor-Based Human Activity Recognition with Spatio-Temporal Deep Learning.

Sensors (Basel, Switzerland)
Human activity recognition (HAR) remains a challenging yet crucial problem to address in computer vision. HAR is primarily intended to be used with other technologies, such as the Internet of Things, to assist in healthcare and eldercare. With the de...

An autonomous debating system.

Nature
Artificial intelligence (AI) is defined as the ability of machines to perform tasks that are usually associated with intelligent beings. Argument and debate are fundamental capabilities of human intelligence, essential for a wide range of human activ...

Attend and Guide (AG-Net): A Keypoints-Driven Attention-Based Deep Network for Image Recognition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
This article presents a novel keypoints-based attention mechanism for visual recognition in still images. Deep Convolutional Neural Networks (CNNs) for recognizing images with distinctive classes have shown great success, but their performance in dis...

Out-of-Distribution Detection of Human Activity Recognition with Smartwatch Inertial Sensors.

Sensors (Basel, Switzerland)
Out-of-distribution (OOD) in the context of Human Activity Recognition (HAR) refers to data from activity classes that are not represented in the training data of a Machine Learning (ML) algorithm. OOD data are a challenge to classify accurately for ...

AR3D: Attention Residual 3D Network for Human Action Recognition.

Sensors (Basel, Switzerland)
At present, in the field of video-based human action recognition, deep neural networks are mainly divided into two branches: the 2D convolutional neural network (CNN) and 3D CNN. However, 2D CNN's temporal and spatial feature extraction processes are...

LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes.

Sensors (Basel, Switzerland)
Human Activity Recognition (HAR) employing inertial motion data has gained considerable momentum in recent years, both in research and industrial applications. From the abstract perspective, this has been driven by an acceleration in the building of ...

Multitask Non-Autoregressive Model for Human Motion Prediction.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Human motion prediction, which aims at predicting future human skeletons given the past ones, is a typical sequence-to-sequence problem. Therefore, extensive efforts have been devoted to exploring different RNN-based encoder-decoder architectures. Ho...

On the Impact of Biceps Muscle Fatigue in Human Activity Recognition.

Sensors (Basel, Switzerland)
Nowadays, Human Activity Recognition (HAR) systems, which use wearables and smart systems, are a part of our daily life. Despite the abundance of literature in the area, little is known about the impact of muscle fatigue on these systems' performance...

Hypergraph Neural Network for Skeleton-Based Action Recognition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Recently, skeleton-based human action recognition has attracted a lot of research attention in the field of computer vision. Graph convolutional networks (GCNs), which model the human body skeletons as spatial-temporal graphs, have shown excellent re...