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Lissette Lemus

dblp:54/302 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Human-AI interaction · 44% Wearable and physiological sensing · 44% Interaction techniques and input · 13%
Computer graphics and multimedia
1 paper
Multimedia analysis and retrieval · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-AI interaction
affective computing
0.912025
Teaching AI to Feel: A Collaborative, Full-Body Exploration of Emotive Communication · ACM Multimedia 2025
Wearable and physiological sensing
emotion recognition
0.912025
Teaching AI to Feel: A Collaborative, Full-Body Exploration of Emotive Communication · ACM Multimedia 2025
Multimedia analysis and retrieval › affective computing › affective multimedia analysis
multimodal emotion analysis
0.312025
Teaching AI to Feel: A Collaborative, Full-Body Exploration of Emotive Communication · ACM Multimedia 2025
Interaction techniques and input › post-WIMP interaction › embodied interaction
full-body interaction
0.312025
Teaching AI to Feel: A Collaborative, Full-Body Exploration of Emotive Communication · ACM Multimedia 2025

Methods — techniques the papers use, named apart from their topics

recommender system · 1.7movenet · 1.7
YearPublicationVenuePosition
2025 Teaching AI to Feel: A Collaborative, Full-Body Exploration of Emotive Communication
abstract
Commonaiverse is an interactive installation exploring human emotions through full-body motion tracking and real-time AI feedback. Participants engage in three phases: Teaching, Exploration and the Cosmos Phase, collaboratively expressing and interpreting emotions with the system. The installation integrates MoveNet for precise motion tracking and a multi-recommender AI system to analyze emotional states dynamically, responding with adaptive audiovisual outputs. By shifting from top-down emotion classification to participant-driven, culturally diverse definitions, we highlight new pathways for inclusive, ethical affective computing. We discuss how this collaborative, out-of-the-box approach pushes multimedia research beyond single-user facial analysis toward a more embodied, co-created paradigm of emotional AI. Furthermore, we reflect on how this reimagined framework fosters user agency, reduces bias, and opens avenues for advanced interactive applications.
Esen K. Tütüncü, Lissette Lemus, Kris Pilcher, Holger Sprengel, Jordi Sabater-Mir
ACM Multimedia2