3 citations · 3 across the 2 of their papers we have counts for
5 papers
MuRAL: A Multi-Resident Ambient Sensor Dataset Annotated with Natural Language for Activities of Daily Living
Xi Chen, Julien Cumin, Fano Ramparany +1
Recent progress in Large Language Models (LLMs) has enabled advanced reasoning and zero-shot recognition for human activity understanding with ambient sensor data. However, widely…
Towards LLM-Powered Ambient Sensor Based Multi-Person Human Activity Recognition
Xi Chen, Julien Cumin, Fano Ramparany +1
Human Activity Recognition (HAR) is one of the central problems in fields such as healthcare, elderly care, and security at home. However, traditional HAR approaches face challenge…
Generative Resident Separation and Multi-label Classification for Multi-person Activity Recognition
Xi Chen, Julien Cumin, Fano Ramparany +1
This paper presents two models to address the problem of multi-person activity recognition using ambient sensors in a home. The first model, Seq2Res, uses a sequence generation app…
Q-SMASH: Q-Learning-based Self-Adaptation of Human-Centered Internet of Things
Hamed Rahimi, Iago Felipe Trentin, Fano Ramparany +1
As the number of Human-Centered Internet of Things (HCIoT) applications increases, the self-adaptation of its services and devices is becoming a fundamental requirement for address…
SMASH: a Semantic-enabled Multi-agent Approach for Self-adaptation of Human-centered IoT
Hamed Rahimi, Iago Felipe Trentin, Fano Ramparany +1
Nowadays, IoT devices have an enlarging scope of activities spanning from sensing, computing to acting and even more, learning, reasoning and planning. As the number of IoT applica…