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Human Activities

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WiPg: Contactless Action Recognition Using Ambient Wi-Fi Signals.

Sensors (Basel, Switzerland)
Motion recognition has a wide range of applications at present. Recently, motion recognition by analyzing the channel state information (CSI) in Wi-Fi packets has been favored by more and more scholars. Because CSI collected in the wireless signal en...

Human Activity Recognition via Hybrid Deep Learning Based Model.

Sensors (Basel, Switzerland)
In recent years, Human Activity Recognition (HAR) has become one of the most important research topics in the domains of health and human-machine interaction. Many Artificial intelligence-based models are developed for activity recognition; however, ...

Robust Human Activity Recognition by Integrating Image and Accelerometer Sensor Data Using Deep Fusion Network.

Sensors (Basel, Switzerland)
Studies on deep-learning-based behavioral pattern recognition have recently received considerable attention. However, if there are insufficient data and the activity to be identified is changed, a robust deep learning model cannot be created. This wo...

A Semi-Supervised Transfer Learning with Dynamic Associate Domain Adaptation for Human Activity Recognition Using WiFi Signals.

Sensors (Basel, Switzerland)
Human activity recognition without equipment plays a vital role in smart home applications, freeing humans from the shackles of wearable devices. In this paper, by using the channel state information (CSI) of the WiFi signal, semi-supervised transfer...

Complex Deep Neural Networks from Large Scale Virtual IMU Data for Effective Human Activity Recognition Using Wearables.

Sensors (Basel, Switzerland)
Supervised training of human activity recognition (HAR) systems based on body-worn inertial measurement units (IMUs) is often constrained by the typically rather small amounts of labeled sample data. Systems like IMUTube have been introduced that emp...

An Efficient Human Instance-Guided Framework for Video Action Recognition.

Sensors (Basel, Switzerland)
In recent years, human action recognition has been studied by many computer vision researchers. Recent studies have attempted to use two-stream networks using appearance and motion features, but most of these approaches focused on clip-level video ac...

Feature Fusion of a Deep-Learning Algorithm into Wearable Sensor Devices for Human Activity Recognition.

Sensors (Basel, Switzerland)
This paper presents a wearable device, fitted on the waist of a participant that recognizes six activities of daily living (walking, walking upstairs, walking downstairs, sitting, standing, and laying) through a deep-learning algorithm, human activit...

Predicting Human Motion Signals Using Modern Deep Learning Techniques and Smartphone Sensors.

Sensors (Basel, Switzerland)
The global adoption of smartphone technology affords many conveniences, and not surprisingly, healthcare applications using wearable sensors like smartphones have received much attention. Among the various potential applications and research related ...

A Novel Hybrid Deep Learning Model for Human Activity Recognition Based on Transitional Activities.

Sensors (Basel, Switzerland)
In recent years, a plethora of algorithms have been devised for efficient human activity recognition. Most of these algorithms consider basic human activities and neglect postural transitions because of their subsidiary occurrence and short duration....

MSTCN: A multiscale temporal convolutional network for user independent human activity recognition.

F1000Research
In recent years, human activity recognition (HAR) has been an active research topic due to its widespread application in various fields such as healthcare, sports, patient monitoring, etc. HAR approaches can be categorised as handcrafted feature met...