Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities
The vast proliferation of sensor devices and Internet of Things enables the applications of
sensor-based activity recognition. However, there exist substantial challenges that could …
sensor-based activity recognition. However, there exist substantial challenges that could …
Human activity recognition: Review, taxonomy and open challenges
Nowadays, Human Activity Recognition (HAR) is being widely used in a variety of domains,
and vision and sensor-based data enable cutting-edge technologies to detect, recognize …
and vision and sensor-based data enable cutting-edge technologies to detect, recognize …
Learning adaptive spatial-temporal context-aware correlation filters for UAV tracking
Tracking in the unmanned aerial vehicle (UAV) scenarios is one of the main components of
target-tracking tasks. Different from the target-tracking task in the general scenarios, the …
target-tracking tasks. Different from the target-tracking task in the general scenarios, the …
Hydrophilic, breathable, and washable graphene decorated textile assisted by silk sericin for integrated multimodal smart wearables
Achieving integrated systems with comfortability and durability comparable to traditional
textiles is one of the ultimate pursuits of smart wearables. This work reports a hydrophilic …
textiles is one of the ultimate pursuits of smart wearables. This work reports a hydrophilic …
Inception inspired CNN-GRU hybrid network for human activity recognition
Abstract Human Activity Recognition (HAR) involves the recognition of human activities
using sensor data. Most of the techniques for HAR involve hand-crafted features and hence …
using sensor data. Most of the techniques for HAR involve hand-crafted features and hence …
Energy-efficient and interpretable multisensor human activity recognition via deep fused lasso net
Utilizing data acquired by multiple wearable sensors can usually guarantee more accurate
recognition for deep learning based human activity recognition. However, an increased …
recognition for deep learning based human activity recognition. However, an increased …
A comprehensive review on vision-based violence detection in surveillance videos
Recent advancements in intelligent surveillance systems for video analysis have been a
topic of great interest in the research community due to the vast number of applications to …
topic of great interest in the research community due to the vast number of applications to …
Resnet-se: Channel attention-based deep residual network for complex activity recognition using wrist-worn wearable sensors
Smart mobile devices are being widely used to identify and track human behaviors in simple
and complex daily activities. The evolution of wearable sensing technologies pertaining to …
and complex daily activities. The evolution of wearable sensing technologies pertaining to …
[HTML][HTML] Multimodal detection of epilepsy with deep neural networks
Epilepsy constitutes a chronic noncommunicable disease of the brain affecting
approximately 50 million people around the world. Most of the existing research initiatives …
approximately 50 million people around the world. Most of the existing research initiatives …
ST-DeepHAR: Deep learning model for human activity recognition in IoHT applications
M Abdel-Basset, H Hawash… - IEEE Internet of …, 2020 - ieeexplore.ieee.org
Human activity recognition (HAR) has been regarded as an indispensable part of many
smart home systems and smart healthcare applications. Specifically, HAR is of great …
smart home systems and smart healthcare applications. Specifically, HAR is of great …