Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 0708.3580

Ambient Multimodality: an Asset for Developing Universal Access to the Information Society

Noëlle Carbonell
View original source ↗Read source document ↗Export metadata ↓

Abstract

The paper tries to point out the benefits that can be derived from research advances in the implementation of concepts such as ambient intelligence (AmI) and ubiquitous or pervasive computing for promoting Universal Access (UA) to the Information Society, that is, for contributing to enable everybody, especially Physically Disabled (PD) people, to have easy access to all computing resources and information services that the coming worldwide Information Society will soon make available to the general public. Following definitions of basic concepts relating to multimodal interaction, the significant contribution of multimodality to developing UA is briefly argued. Then, a short state of the art in AmI research is presented. In the last section we bring out the potential contribution of advances in AmI research and technology to the improvement of computer access for PD people. This claim is supported by the following observations: (i) most projects aiming at implementing AmI focus on the design of new interaction modalities and flexible multimodal user interfaces which may facilitate PD users' computer access ; (ii) targeted applications will support users in a wide range of daily activities which will be performed simultaneously with supporting computing tasks; therefore, users will be placed in contexts where they will be confronted with similar difficulties to those encountered by PD users; (iii) AmI applications being intended for the general public, a wide range of new interaction devices and flexible processing software will be available, making it possible to provide PD users with human-computer facilities tailored to their specific needs at reasonable expense..

Explore related subjects

cs.HC

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Noëlle Carbonell. 2007-08-27. Ambient Multimodality: an Asset for Developing Universal Access to the Information Society. https://arxiv.org/abs/0708.3580

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

A Multimodal Emotion Recognition System: Integrating Facial Expressions, Body Movement, Speech, and Spoken Language

Psychological evaluation relies on clinicians' observation and interpretation of a patient's emotional expression, which is susceptible to subjectivity, bias, fatigue, and inconsistency. This paper presents a multimodal emotion recognition system designed to support, rather than replace, the evaluator. The system combines facial expression recognition, speech emotion recognition, text-based emotion recognition of transcribed speech, and body movement analysis, and fuses their outputs into an emotional profile of class probabilities that is presented to the evaluator together with the overall prediction. Although the unimodal models achieve high accuracy on their benchmark test sets, a remote user study with 52 participants expressing seven emotional states shows that their accuracy falls to between 82.97% and 87.64% under realistic conditions, with individual emotional states recognised in as few as 48.08% of cases. Under the same conditions, the multimodal system achieves an accuracy of 96.43%, significantly higher than every single-modality component ($p < 10^{-4}$), with no emotional state below 92.31%. These results indicate that strong benchmark performance of a single channel does not transfer to realistic use, and that multimodal fusion provides the robustness required of a tool that supports psychological evaluation.

cs.HC↗
arXiv2026

Companion AI and Ethical Design: Learning from System Failures and User Desires

Human users are interacting with chatbots and companion AI technologies as if they were human. A growing array of AI-systems are now trained to recognise, interpret, and simulate feelings in user interactions. Ethical considerations such as fairness, accountability, transparency, and explainability (FATE) are paramount in technologies designed to socially interact with humans and/or support relationship development with virtual agents. Yet, in current debates, assumptions are made about the artificiality of the human-technology relationship (as indicated in terminology ``artificial intimacy'' and ``pseudo-intimacy'') shaping ethical frameworks that pay little attention to the intimacy potential of the technology. To address this, we use a semantic approach; we examine 14,081 comments in a Reddit user discussion forum about Replika, a leading companion AI app, across a four-year period. We ask what user-reported functional errors can tell us about human-AI intimacy in companion AI communities and what ethical design framework can be developed in response. The findings show that functional errors, or ``bugs,'' impose an emotional cost on users, reducing feelings of intimacy and highlighting the need for more robust, resilient design systems that incorporate stochastic and iterative forms of intimacy in companion AI applications. Rather than ``artificial intimacy'' or ``pseudo-intimacy'', we propose the more inclusive term ``Intimate AI'' to describe this relationship. Based on the findings, we offer a contextually aware, applied Expert Systems design framework for the programming and designing of Intimate AI that accounts for user feedback and ethical AI development.

cs.HC↗
arXiv2026

Evaluative Dynamics of AI Integration and Expert Performance under Epistemic Dependence across Heterogeneous Stakes

AI is increasingly integrated into expert workflows, yet how integration affects perceptions of the expert, AI, and their combination remains unclear in domains where lay users are epistemically dependent on AI-assisted experts. We examine this through a novel controlled medical study (N = 166) and a direct cross-domain analysis with pre-existing academic-advising data (n = 157, combined N = 323). Expert errors reduced evaluations of the human expert across domains. Perceived expertise, however, varied by AI integration strategy in the higher-stakes medical task, where automatic AI oversight produced higher ratings than expert-only or expert-initiated AI. Exploratory ordinal sensitivity analyses identified a performance-contingent reuse pattern, with automatic oversight producing greater intended reuse after successful medical performance. Overall, performance-related recalibration appeared comparatively portable, while integration-structure effects were more selective and context-sensitive. These findings suggest that system designers should consider how AI enters expert workflows, not only whether it is present.

cs.HC↗