[PDF][PDF] Deep unsupervised domain adaptation: A review of recent advances and perspectives
Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …
domains, partly because of its ability to learn from data and achieve impressive performance …
Transfer learning and its extensive appositeness in human activity recognition: A survey
A Ray, MH Kolekar - Expert Systems with Applications, 2024 - Elsevier
In this competitive world, the supervision and monitoring of human resources are primary
and necessary tasks to drive context-aware applications. Advancement in sensor and …
and necessary tasks to drive context-aware applications. Advancement in sensor and …
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 …
Semi-supervised and personalized federated activity recognition based on active learning and label propagation
One of the major open problems in sensor-based Human Activity Recognition (HAR) is the
scarcity of labeled data. Among the many solutions to address this challenge, semi …
scarcity of labeled data. Among the many solutions to address this challenge, semi …
[PDF][PDF] Optimal search mapping among sensors in heterogeneous smart homes
Y Yu, Z Hao, G Li, Y Liu, R Yang, H Liu - Math. Biosci. Eng, 2023 - aimspress.com
There are huge differences in the layouts and numbers of sensors in different smart home
environments. Daily activities performed by residents trigger a variety of sensor event …
environments. Daily activities performed by residents trigger a variety of sensor event …
Dexar: Deep explainable sensor-based activity recognition in smart-home environments
The sensor-based recognition of Activities of Daily Living (ADLs) in smart-home
environments is an active research area, with relevant applications in healthcare and …
environments is an active research area, with relevant applications in healthcare and …
Unsupervised domain adaptation with and without access to source data for estimating occupancy and recognizing activities in smart buildings
Energy-efficient buildings have gained increasing interest in the last decades as they
provide optimal energy management. With the emergence of smart homes, many smart tools …
provide optimal energy management. With the emergence of smart homes, many smart tools …
Large language models are zero-shot recognizers for activities of daily living
The sensor-based recognition of Activities of Daily Living (ADLs) in smart home
environments enables several applications in the areas of energy management, safety, well …
environments enables several applications in the areas of energy management, safety, well …
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 …
Personalized semi-supervised federated learning for human activity recognition
The most effective data-driven methods for human activities recognition (HAR) are based on
supervised learning applied to the continuous stream of sensors data. However, these …
supervised learning applied to the continuous stream of sensors data. However, these …