A bedside camera detects REM sleep and agrees with a clinical EEG
Details
- External ID
- 48797671
- Source
- HN
- Company
- —
- Product
- A bedside camera detects REM sleep and agrees with a clinical EEG
- Website domain
- lucidcode.com
- Launched
- July 5, 2026
- Cohort
- —
- Upvotes
- 10
- Upvotes percentile
- 0.5418160095579451
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:26 p.m.
- Updated at
- Sept. 7, 2026, 9:26 p.m.
Description
I build a device called the INSPEC that detects REM sleep from a bedside infrared camera instead of electrodes. It watches the face under IR light and measures frame-to-frame pixel variance in the eye region. During quiet sleep the face is stable. During REM the eye movements under the lids produce measurable variance, which the device aggregates over time before it flags a REM period. Toss detection, whole-frame motion rejection, and face tracking filter out body movement and out-of-frame periods.After I gave a talk on contactless REM detection, a sleep lab loaned me a clinical-grade EEG device. I recorded a full night wearing the EEG with the camera running at the bedside.I scored the EEG with three independent sleep stage classifiers: ez6 and ez6moe from ezscore, and DreamentoScorer from Dreamento. The REM periods fell at the same times the camera had flagged REM from the video. The long REM periods late in the night, around hours three, five, and seven, matched in every classifier.
Enrichment
- Theme
- sleep, wake, and clock utilities
- Vertical
- Healthcare
- Function
- Hardware & robotics
- Audience
- B2B
- AI stance
- AI feature
- Project type
- Commercial product
- Normalized one-liner
- camera-based rem sleep detection
- Manually corrected
- False
Could you build this?
No This is a custom hardware device with an infrared camera, computer vision algorithms measuring micromotions across sleep epochs, and clinical EEG validation.
What it would actually take: Building this requires custom IR camera hardware, firmware with low-latency image capture, and computer vision pipelines (optical flow or pixel-variance analysis) fine-tuned to detect rapid eye movements under closed eyelids. It also requires clinical sleep study validation against polysomnography/EEG datasets, requiring deep biomedical engineering and hardware prototyping expertise.
Discussion
No comments on this launch.
Competitors
Other products that read as similar to this one — 83 launches clear the similarity bar, closest 8 shown.
Attention rank: #39 of 84 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 230 days after the earliest competitor.
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Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a hardware & robotics tool for Horizontal yet.