Representation Matters in Longitudinal Affective Computing
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...
| Authors | Igor Matias, Maximilian Haas, Eric J. Daza et al. |
|---|---|
| Primary category | cs.HC |
| Published | 2 July 2026 |
| Last updated at source | 2 July 2026 |
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