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Ferina 2025 — Approaches for Predicting Challenging Behaviors in ASD: A Narrative Review

Citation: Ferina, J., Dando, E., Anderson, C., et al. (2025). Journal of Personalized Medicine, 15(10), 453.
DOI: 10.3390/jpm15100453
Type: Narrative review

Key Contribution

Comprehensive review of all approaches for predicting challenging behaviors in ASD. Covers wearables, non-wearable sensors, and manually recorded data. Includes short-term (seconds) to long-term (next-day) prediction horizons.

Covered Approaches

  • Wearable biosensors (EDA, HR, ACC) — Goodwin, Imbiriba et al.
  • Video-based emotion detection (SSBD, ARRBD datasets)
  • Sleep data → next-day behavior prediction
  • Environmental data (GI, pollen, weather, moon phase)

Key Gap Identified

Most approaches are short-lead (seconds to minutes). Longer-horizon prediction (next-day) is emerging but under-explored. Few studies on large, diverse populations.

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