Deep unsupervised domain adaptation with time series sensor data: A survey
Sensors are devices that output signals for sensing physical phenomena and are widely
used in all aspects of our social production activities. The continuous recording of physical …
used in all aspects of our social production activities. The continuous recording of physical …
Survey of transfer learning approaches in the machine learning of digital health sensing data
L Chato, E Regentova - Journal of Personalized Medicine, 2023 - mdpi.com
Machine learning and digital health sensing data have led to numerous research
achievements aimed at improving digital health technology. However, using machine …
achievements aimed at improving digital health technology. However, using machine …
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 …
ContrasGAN: Unsupervised domain adaptation in Human Activity Recognition via adversarial and contrastive learning
Abstract Human Activity Recognition (HAR) makes it possible to drive applications directly
from embedded and wearable sensors. Machine learning, and especially deep learning …
from embedded and wearable sensors. Machine learning, and especially deep learning …
Transfer learning in human activity recognition: A survey
SG Dhekane, T Ploetz - arXiv preprint arXiv:2401.10185, 2024 - arxiv.org
Sensor-based human activity recognition (HAR) has been an active research area, owing to
its applications in smart environments, assisted living, fitness, healthcare, etc. Recently …
its applications in smart environments, assisted living, fitness, healthcare, etc. Recently …
MASS: a multisource domain adaptation network for Cross-Subject touch gesture recognition
Touch gesture recognition (TGR) plays a pivotal role in many applications, such as socially
assistive robots and embodied telecommunication. However, one obstacle to practicality of …
assistive robots and embodied telecommunication. However, one obstacle to practicality of …
Comparing cross-subject performance on human activities recognition using learning models
Z Yang, M Qu, Y Pan, R Huan - IEEE Access, 2022 - ieeexplore.ieee.org
Human activities recognition (HAR) plays a vital role in fields like ambient assisted living and
health monitoring, in which cross-subject recognition is one of the main challenges coming …
health monitoring, in which cross-subject recognition is one of the main challenges coming …
Investigation of heterogeneity sources for occupational task recognition via transfer learning
Human activity recognition has been extensively used for the classification of occupational
tasks. Existing activity recognition approaches perform well when training and testing data …
tasks. Existing activity recognition approaches perform well when training and testing data …
A Survey of IMU Based Cross-Modal Transfer Learning in Human Activity Recognition
Despite living in a multi-sensory world, most AI models are limited to textual and visual
understanding of human motion and behavior. In fact, full situational awareness of human …
understanding of human motion and behavior. In fact, full situational awareness of human …
Fusion and Cross-Modal Transfer for Zero-Shot Human Action Recognition
Despite living in a multi-sensory world, most AI models are limited to textual and visual
interpretations of human motion and behavior. Inertial measurement units (IMUs) provide a …
interpretations of human motion and behavior. Inertial measurement units (IMUs) provide a …