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Human Activities

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A Comparative Analysis of Hybrid Deep Learning Models for Human Activity Recognition.

Sensors (Basel, Switzerland)
Recent advances in artificial intelligence and machine learning (ML) led to effective methods and tools for analyzing the human behavior. Human Activity Recognition (HAR) is one of the fields that has seen an explosive research interest among the ML ...

Scheduling Sensor Duty Cycling Based on Event Detection Using Bi-Directional Long Short-Term Memory and Reinforcement Learning.

Sensors (Basel, Switzerland)
A smart home provides a facilitated environment for the detection of human activity with appropriate Deep Learning algorithms to manipulate data collected from numerous sensors attached to various smart things in a smart home environment. Human activ...

Improved Activity Recognition Combining Inertial Motion Sensors and Electroencephalogram Signals.

International journal of neural systems
Human activity recognition and neural activity analysis are the basis for human computational neureoethology research dealing with the simultaneous analysis of behavioral ethogram descriptions and neural activity measurements. Wireless electroencepha...

A Hierarchical Learning Approach for Human Action Recognition.

Sensors (Basel, Switzerland)
In the domain of human action recognition, existing works mainly focus on using RGB, depth, skeleton and infrared data for analysis. While these methods have the benefit of being non-invasive, they can only be used within limited setups, are prone to...

Prediction of Human Activities Based on a New Structure of Skeleton Features and Deep Learning Model.

Sensors (Basel, Switzerland)
The recognition of human activities is usually considered to be a simple procedure. Problems occur in complex scenes involving high speeds. Activity prediction using Artificial Intelligence (AI) by numerical analysis has attracted the attention of se...

Deep Learning-Based Real-Time Multiple-Person Action Recognition System.

Sensors (Basel, Switzerland)
Action recognition has gained great attention in automatic video analysis, greatly reducing the cost of human resources for smart surveillance. Most methods, however, focus on the detection of only one action event for a single person in a well-segme...

Energy-Guided Temporal Segmentation Network for Multimodal Human Action Recognition.

Sensors (Basel, Switzerland)
To achieve the satisfactory performance of human action recognition, a central task is to address the sub-action sharing problem, especially in similar action classes. Nevertheless, most existing convolutional neural network (CNN)-based action recogn...

A Stacked Human Activity Recognition Model Based on Parallel Recurrent Network and Time Series Evidence Theory.

Sensors (Basel, Switzerland)
As the foundation of Posture Analysis, recognizing human activity accurately in real time assists in using machines to intellectualize living condition and monitor health status. In this paper, we focus on recognition based on raw time series data, w...

A Method for Sensor-Based Activity Recognition in Missing Data Scenario.

Sensors (Basel, Switzerland)
Sensor-based human activity recognition has various applications in the arena of healthcare, elderly smart-home, sports, etc. There are numerous works in this field-to recognize various human activities from sensor data. However, those works are base...

Comparing Person-Specific and Independent Models on Subject-Dependent and Independent Human Activity Recognition Performance.

Sensors (Basel, Switzerland)
The distinction between subject-dependent and subject-independent performance is ubiquitous in the human activity recognition (HAR) literature. We assess whether HAR models really do achieve better subject-dependent performance than subject-independe...