SensAI+Expanse Adaptation on Human Behaviour Towards Emotional Valence Prediction
Nuno A. C. Henriques, Helder Coelho, Leonel Garcia-Marques

TL;DR
This paper presents a distributed mobile-cloud system, SensAI+Expanse, that adaptively predicts human emotional valence efficiently and robustly, using various adaptive mechanisms and state-of-the-art machine learning algorithms.
Contribution
It introduces a novel adaptive mobile-cloud framework for emotional valence prediction that is resource-efficient, resilient, and capable of auto-adjusting its learning parameters.
Findings
Extreme Gradient Boosting achieved the best prediction performance.
The system demonstrated robustness to environmental and behavioral disruptions.
Energy-efficient prediction with explainability was confirmed.
Abstract
An agent, artificial or human, must be continuously adjusting its behaviour in order to thrive in a more or less demanding environment. An artificial agent with the ability to predict human emotional valence in a geospatial and temporal context requires proper adaptation to its mobile device environment with resource consumption strict restrictions (e.g., power from battery). The developed distributed system includes a mobile device embodied agent (SensAI) plus Cloud-expanded (Expanse) cognition and memory resources. The system is designed with several adaptive mechanisms in a best effort for the agent to cope with its interacting humans and to be resilient on collecting data for machine learning towards prediction. These mechanisms encompass homeostatic-like adjustments such as auto recovering from an unexpected failure in the mobile device, forgetting repeated data to save local…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEmotion and Mood Recognition · Digital Mental Health Interventions · Innovative Human-Technology Interaction
