Cannabis detection model
Core idea
Cannabis is one of the best wearable detection candidates after alcohol. Tolerance is the main enemy of accuracy.
Primary feature stack
- Delta resting HR from personal baseline — strongest anchor signal
- Minimum HR / plateau pattern during intoxication window (~10–30 min post-inhale)
- REM suppression same night — strongest overnight corroborator
- Sleep efficiency drop that night
- Motion context — separates from exercise (cannabis HR event is plateau-like, not spike-decay)
- Tolerance-adjusted priors — frequency-adjusted priors for heavy users
Published evidence
- MobiFit (Fitbit + smartphone fused): 99% AUC, 0.85 F1 for moderate-to-intense intoxication (naturalistic)
- Apple Watch-specific model: does not exist yet
- Expected watch-only performance: AUC ~0.75–0.85 in occasional users
Harder problems
- Heavy daily users: acute HR spike muted by tolerance → false negatives
- Distinguishing from alcohol: cannabis has sharper immediate HR event + REM suppression; alcohol has stronger next-day HRV suppression + elevated RHR
- Distinguishing from exercise: cannabis plateau HR vs exercise spike-decay curve + different motion/thermal context
Confounders
Stress/anxiety, sleep deprivation, stimulants — all can elevate HR similarly.
Related
Cannabis, HRV signatures, REM suppression, Sleep architecture, Cannabis withdrawal