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Human activity recognition in artificial intelligence framework: a narrative review
Human activity recognition (HAR) has multifaceted applications due to its worldly usage of
acquisition devices such as smartphones, video cameras, and its ability to capture human …
acquisition devices such as smartphones, video cameras, and its ability to capture human …
Focal: Contrastive learning for multimodal time-series sensing signals in factorized orthogonal latent space
This paper proposes a novel contrastive learning framework, called FOCAL, for extracting
comprehensive features from multimodal time-series sensing signals through self …
comprehensive features from multimodal time-series sensing signals through self …
[HTML][HTML] A survey on deploying mobile deep learning applications: A systemic and technical perspective
With the rapid development of mobile devices and deep learning, mobile smart applications
using deep learning technology have sprung up. It satisfies multiple needs of users, network …
using deep learning technology have sprung up. It satisfies multiple needs of users, network …
IF-ConvTransformer: A framework for human activity recognition using IMU fusion and ConvTransformer
Y Zhang, L Wang, H Chen, A Tian, S Zhou… - Proceedings of the ACM …, 2022 - dl.acm.org
Recent advances in sensor based human activity recognition (HAR) have exploited deep
hybrid networks to improve the performance. These hybrid models combine Convolutional …
hybrid networks to improve the performance. These hybrid models combine Convolutional …
[HTML][HTML] A Review of Recent Techniques for Human Activity Recognition: Multimodality, Reinforcement Learning, and Language Models
U Oleh, R Obermaisser, AS Ahammed - Algorithms, 2024 - mdpi.com
Human Activity Recognition (HAR) is a rapidly evolving field with the potential to
revolutionise how we monitor and understand human behaviour. This survey paper provides …
revolutionise how we monitor and understand human behaviour. This survey paper provides …
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 …
On the use of a convolutional block attention module in deep learning-based human activity recognition with motion sensors
S Agac, O Durmaz Incel - Diagnostics, 2023 - mdpi.com
Sensor-based human activity recognition with wearable devices has captured the attention
of researchers in the last decade. The possibility of collecting large sets of data from various …
of researchers in the last decade. The possibility of collecting large sets of data from various …
Giobalfusion: A global attentional deep learning framework for multisensor information fusion
The paper enhances deep-neural-network-based inference in sensing applications by
introducing a lightweight attention mechanism called the global attention module for multi …
introducing a lightweight attention mechanism called the global attention module for multi …
Spatial-temporal masked autoencoder for multi-device wearable human activity recognition
The widespread adoption of wearable devices has led to a surge in the development of multi-
device wearable human activity recognition (WHAR) systems. Nevertheless, the …
device wearable human activity recognition (WHAR) systems. Nevertheless, the …
Deep learning based multimodal complex human activity recognition using wearable devices
L Chen, X Liu, L Peng, M Wu - Applied Intelligence, 2021 - Springer
Wearable device based human activity recognition, as an important field of ubiquitous and
mobile computing, is drawing more and more attention. Compared with simple human …
mobile computing, is drawing more and more attention. Compared with simple human …