{"id":1131,"date":"2026-07-02T08:01:00","date_gmt":"2026-07-02T07:01:00","guid":{"rendered":"https:\/\/igormatias.com\/?p=1131"},"modified":"2026-09-09T08:01:13","modified_gmt":"2026-09-09T07:01:13","slug":"representation-matters-in-longitudinal-affective-computing","status":"publish","type":"post","link":"https:\/\/igormatias.com\/pt\/2026\/07\/02\/representation-matters-in-longitudinal-affective-computing\/","title":{"rendered":"Representation Matters in Longitudinal Affective Computing"},"content":{"rendered":"<p><span class=\"LabelSearchRepeater\">Abstract: 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.<\/span><\/p>\n<p>Igor Matias<sup>1,2<\/sup>, Maximilian Haas<sup>2,3<\/sup>, Eric J. Daza<sup>4,5<\/sup>, Matthias Kliegel<sup>2<\/sup>, Katarzyna Wac<sup>1<\/sup><\/p>\n<p><sup>1<\/sup>Quality of Life Technologies Lab, UNIGE, Switzerland, <sup>2<\/sup>Cognitive Aging Lab, UNIGE, Switzerland, <sup>3<\/sup>Faculty of Psychology, UniDistance Suisse, Switzerland, <sup>4<\/sup>Stats-of-1, USA, <sup>5<\/sup>Boehringer Ingelheim Pharmaceuticals Inc., USA<\/p>\n<p><span id=\"ContentPlaceHolder1_LinkPaperPage_LinkPaperContent_AuxInProceeding\" class=\"PublicationsDetailNormal\">In arXiv, 2026.<\/span><\/p>\n<p>Article: <a href=\"https:\/\/doi.org\/10.48550\/arXiv.2608.07518\">here<\/a><\/p>\n\n\n<p><\/p>","protected":false},"excerpt":{"rendered":"<p>Abstract: 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.<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_members_access_role":[],"_members_access_error":"","_links_to":"","_links_to_target":""},"categories":[19],"tags":[],"class_list":["post-1131","post","type-post","status-publish","format-standard","hentry","category-preprint"],"gutentor_comment":0,"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Representation Matters in Longitudinal Affective Computing<\/title>\n<meta name=\"description\" content=\"Abstract: Longitudinal, in-the-wild, wearable sensing yields day-level physiology, sleep, activity, and environmental streams...\" \/>\n<meta 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