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Found 7 papers, 4 papers with code
Using Language Model to Bootstrap Human Activity Recognition Ambient Sensors Based in Smart Homes
We thus demonstrate that taking into account the context of the sensors and their semantics increases the classification performances and enables transfer learning.
A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning
In this paper, we present recent algorithms, works, challenges and taxonomy of the field of human activity recognition in a smart home through ambient sensors.
Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart Homes
Our analysis also shows that FCN is a suitable algorithm for smart home activity recognition and hightlights the advantages of automatic feature extraction.
A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks
A second type of approaches fix the deep model size and introduce a mechanism whose objective is to ensure a good compromise between stability and plasticity of the model.
Initial Classifier Weights Replay for Memoryless Class Incremental Learning
It leverages initial classifier weights which provide a strong representation of past classes because they are trained with all class data.
How An Automated Gesture Imitation Game Can Improve Social Interactions With Teenagers With ASD
With the outlook of improving communication and social abilities of people with ASD, we propose to extend the paradigm of robot-based imitation games to ASD teenagers.
Building an Automated Gesture Imitation Game for Teenagers with ASD
Autism spectrum disorder is a neurodevelopmental condition that includes issues with communication and social interactions.
