Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges
This paper firstly introduces common wearable sensors, smart wearable devices and the key
application areas. Since multi-sensor is defined by the presence of more than one model or …
application areas. Since multi-sensor is defined by the presence of more than one model or …
Deep gait recognition: A survey
A Sepas-Moghaddam, A Etemad - IEEE transactions on pattern …, 2022 - ieeexplore.ieee.org
Gait recognition is an appealing biometric modality which aims to identify individuals based
on the way they walk. Deep learning has reshaped the research landscape in this area …
on the way they walk. Deep learning has reshaped the research landscape in this area …
Limu-bert: Unleashing the potential of unlabeled data for imu sensing applications
Deep learning greatly empowers Inertial Measurement Unit (IMU) sensors for various mobile
sensing applications, including human activity recognition, human-computer interaction …
sensing applications, including human activity recognition, human-computer interaction …
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 …
Tasked: transformer-based adversarial learning for human activity recognition using wearable sensors via self-knowledge distillation
Wearable sensor-based human activity recognition (HAR) has emerged as a principal
research area and is utilized in a variety of applications. Recently, deep learning-based …
research area and is utilized in a variety of applications. Recently, deep learning-based …
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 …
SWL-Adapt: An unsupervised domain adaptation model with sample weight learning for cross-user wearable human activity recognition
Abstract In practice, Wearable Human Activity Recognition (WHAR) models usually face
performance degradation on the new user due to user variance. Unsupervised domain …
performance degradation on the new user due to user variance. Unsupervised domain …
Noise-robust machinery fault diagnosis based on self-attention mechanism in wavelet domain
A Tian, Y Zhang, C Ma, H Chen, W Sheng, S Zhou - Measurement, 2023 - Elsevier
Machinery fault diagnosis in noisy scenes is important and challenging to ensure the safety
and stability of machinery. Existing machinery fault diagnosis methods mostly handle noisy …
and stability of machinery. Existing machinery fault diagnosis methods mostly handle noisy …
HMGAN: A hierarchical multi-modal generative adversarial network model for wearable human activity recognition
Wearable Human Activity Recognition (WHAR) is an important research field of ubiquitous
and mobile computing. Deep WHAR models suffer from the overfitting problem caused by …
and mobile computing. Deep WHAR models suffer from the overfitting problem caused by …
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 …