Human action recognition from various data modalities: A review
Human Action Recognition (HAR) aims to understand human behavior and assign a label to
each action. It has a wide range of applications, and therefore has been attracting increasing …
each action. It has a wide range of applications, and therefore has been attracting increasing …
A review of multimodal human activity recognition with special emphasis on classification, applications, challenges and future directions
Human activity recognition (HAR) is one of the most important and challenging problems in
the computer vision. It has critical application in wide variety of tasks including gaming …
the computer vision. It has critical application in wide variety of tasks including gaming …
Continuous human activity classification from FMCW radar with Bi-LSTM networks
A Shrestha, H Li, J Le Kernec… - IEEE Sensors …, 2020 - ieeexplore.ieee.org
Recognition of human movements with radar for ambient activity monitoring is a developed
area of research that yet presents outstanding challenges to address. In real environments …
area of research that yet presents outstanding challenges to address. In real environments …
Semantics-aware adaptive knowledge distillation for sensor-to-vision action recognition
Existing vision-based action recognition is susceptible to occlusion and appearance
variations, while wearable sensors can alleviate these challenges by capturing human …
variations, while wearable sensors can alleviate these challenges by capturing human …
Vision and inertial sensing fusion for human action recognition: A review
S Majumder, N Kehtarnavaz - IEEE Sensors Journal, 2020 - ieeexplore.ieee.org
Human action recognition is used in many applications such as video surveillance, human-
computer interaction, assistive living, and gaming. Many papers have appeared in the …
computer interaction, assistive living, and gaming. Many papers have appeared in the …
3D Human Action Recognition: Through the eyes of researchers
Abstract Human Action Recognition (HAR) has remained one of the most challenging tasks
in computer vision. With the surge in data-driven methodologies, the depth modality has …
in computer vision. With the surge in data-driven methodologies, the depth modality has …
Multimodn—multimodal, multi-task, interpretable modular networks
Predicting multiple real-world tasks in a single model often requires a particularly diverse
feature space. Multimodal (MM) models aim to extract the synergistic predictive potential of …
feature space. Multimodal (MM) models aim to extract the synergistic predictive potential of …
Driver fatigue detection systems using multi-sensors, smartphone, and cloud-based computing platforms: a comparative analysis
Q Abbas, A Alsheddy - Sensors, 2020 - mdpi.com
Internet of things (IoT) cloud-based applications deliver advanced solutions for smart cities
to decrease traffic accidents caused by driver fatigue while driving on the road …
to decrease traffic accidents caused by driver fatigue while driving on the road …
Radar-based human activity recognition using hybrid neural network model with multidomain fusion
W Ding, X Guo, G Wang - IEEE Transactions on Aerospace and …, 2021 - ieeexplore.ieee.org
This article concerns the issue of how to combine the multidomainradar information,
including range–Doppler, time–Doppler, and time–range, for human activity recognition …
including range–Doppler, time–Doppler, and time–range, for human activity recognition …
Multi-sensor fusion based industrial action recognition method under the environment of intelligent manufacturing
Z Wang, J Yan - Journal of Manufacturing Systems, 2024 - Elsevier
In the context of intelligent manufacturing and Industry 4.0, the manufacturing industry is
rapidly transitioning toward mass personalization production. Despite this trend, the …
rapidly transitioning toward mass personalization production. Despite this trend, the …