# Pseudo-Label Augmentation for Affect Sensing in Small Collaborative Groups

arXiv: https://arxiv.org/abs/2609.16077 · HTML: https://mlsanity.com/paper/2609.16077/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 46 references that are in the map.
- First posted on arXiv 13 Sep 2026.

## Builds on (most cited first, 13 of 13)

- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Time-Series Representation Learning via Temporal and Contextual Contrasting](https://mlsanity.com/paper/2106.14112/) (2021)
- [Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks](https://mlsanity.com/paper/1706.00527/) (2017)
- [An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks](https://mlsanity.com/paper/2007.15951/) (2020)
- [Label Distribution Learning](https://mlsanity.com/paper/1408.6027/) (2014)
- [K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations](https://mlsanity.com/paper/2005.04120/) (2020)
- [SALSA: A Novel Dataset for Multimodal Group Behavior Analysis](https://mlsanity.com/paper/1506.06882/) (2015)
- [A Survey on Physiological Signal Based Emotion Recognition](https://mlsanity.com/paper/2205.10466/) (2022)
- [Advancing Face-to-Face Emotion Communication: A Multimodal Dataset (AFFEC)](https://mlsanity.com/paper/2504.18969/) (2025)
- [MuMTAffect: A Multimodal Multitask Affective Framework for Personality and Emotion Recognition from Physiological Signals](https://mlsanity.com/paper/2509.04254/) (2025)
- [Dynamics of Collective Group Affect: Group-level Annotations and the Multimodal Modeling of Convergence and Divergence](https://mlsanity.com/paper/2409.08578/) (2024)
- [Modelling the Interplay of Eye-Tracking Temporal Dynamics and Personality for Emotion Detection in Face-to-Face Settings](https://mlsanity.com/paper/2510.24720/) (2025)
- [AffectAI-Capture: A Reproducible Multimodal Protocol for Small-Group Meeting Research](https://mlsanity.com/paper/2605.19794/) (2026)

## Abstract (arXiv)

Physiological affect sensing in naturalistic group interaction is often limited by sparse labels rather than sensor data: wearable devices produce many time windows, while self-reports are collected only a few times per session. Using GroupAffect-4, a four-person collaborative dataset with wearable physiology, eye tracking, Big Five personality, and post-task VAD labels, we study pseudo-label augmentation for affect sensing under sparse supervision. We compare no augmentation, Gaussian Process pseudo-labelling, personality-aware trust weighting, and joint personality-plus-confidence weighting within a shared target-construction pipeline. Results show that pseudo-label augmentation improves over the labelled-only baseline in the known-team setting. However, the narrow range of Big Five cosine similarities (0.91-0.99) makes fine-grained personality weighting ineffective; personality similarity functions mainly as a same-team filter rather than a calibrated trust signal. With smoothing, augmented SVM variants are effectively tied on Valence and Arousal, while the joint personality-plus-confidence variant gives the highest Dominance score. Cross-subject LOSO transfer remains encouraging, especially for Arousal, whereas strict session-isolated LOGO removes the augmentation benefit. Given only 10 groups, LOGO should be interpreted as a conservative lower bound on unseen-group transfer. Overall, the results suggest that pseudo-label augmentation can make better use of sparsely labelled collaborative affect data, while personality information is most useful as a within-team selection mechanism.