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Representation Matters in Longitudinal Affective Computing — arXiv:2608.07518

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.07518
License (as archived): CC0-1.0
Archived: 2026-10-19T08:59:28.174Z
SHA-256 of the archived text: 06b16a6a5864b23350a5509c597be07cc9c334c8fcd1d61e142d314965426885
Stable URL of this page: https://metraipolis.com/documento/dc_91a76166e2db

How to cite

Representation Matters in Longitudinal Affective Computing — arXiv:2608.07518 — arXiv (en), https://arxiv.org/abs/2608.07518 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_91a76166e2db (Metraipolis archive dc_91a76166e2db, sha256 06b16a6a5864b23350a5509c597be07cc9c334c8fcd1d61e142d314965426885)

BibTeX

@misc{dc91a76166e2db,
  title = {Representation Matters in Longitudinal Affective Computing — arXiv:2608.07518},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.07518},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_91a76166e2db; sha256 06b16a6a5864b23350a5509c597be07cc9c334c8fcd1d61e142d314965426885},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.07518. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Longitudinal, in-the-wild, wearable sensing yields day-level physiology, sleep, activity, and environmental streams, whereas affect and cognition are labeled only episodically (per waves). We recast this cadence mismatch as a temporal representation problem and compare three wave-level mappings from dense histories to sparse labels: levels (within-wave summaries), absolute drift (change across waves), and proportional drift. Using almost a year of data from 82 adults in the Providemus alz study, we model 21 affect and cognition outcomes. Day-scale signals are reduced to compact wave-level descriptors (central tendency, dispersion, and distributional shape) and learned with four regressors under two orthogonal evaluation axes: leave-one-subject-out and leave-one-wave-out. Performance is reported as scaled MAE using both mean and median across folds. Differences emerge: affective states are best predicted by wave-to-wave absolute drift, whereas cognitive performance aligns with within-wave levels, reflecting emotion dynamic theories. Across windowing features, shape descriptors (e.g., minima, kurtosis) carry more signal than simple means/medians. We contribute a representation triad for sparse-label modelling, a wave-level feature schema applicable on-device, and a dual-axis reporting practice that separates cross-participant generalization from temporal robustness. These results convert temporal representation from an implicit preprocessing step into an explicit, testable design choice for real-world affective-computing applications in brain health.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-11 in cs.HC).
Categorie arXiv: Human-Computer Interaction (cs.HC) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.07518 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T08:54:55.989Z dalla pagina https://arxiv.org/catchup/cs.HC/2026-08-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).