Category: INSPEC


2022-04-29 – Dreaming Detected

The volume was too low for the audio to be heard, but rapid eye movements were detected twice.

Download entry as Lucid Scribe Data (LSD) (691 MB).

Our dog woke me up from a dream at 03:30 while barking at something in the trees. I was only able to get back to sleep 2 hours later after a shower. But totally worthwhile as I had a marathon dream session with over a dozen notifications. Most of them went unnoticed as the volume was very low, but in the minute above the effect can clearly be seen. I was able to remain still and re-enter the dream with full lucidity and control.

I was able to sleep in and catch another session a bit later when the sun was already up.

Download entry as Lucid Scribe Data (LSD) (663 MB).

2022-03-10 – Consistencekey

The algorithm detected the eye movements from the same dream three times in a row in as many minutes at 6 AM. The same time as yesterday. The first two audio tracks did not have an effect, but I recognized the third one right away.

I was trying to dodge COD zombies as I had run out of ammo, and was aware that it was a dream or VR the whole time, but had very little control and my movements were very laggy. I attempted to re-enter after the audio track woke me up, but was too stressed from trying to escape the horde and gave up after 15 minutes.

Download entry as Lucid Scribe Data (LSD) (291 MB).

2022-03-09 – Zoom

Inspired by the optic and software zoom on Sebastiii’s security camera, I doubled the resolution on the inspec camera and saw immediate improvement when monitoring via Lucid Scribe. It might need some further optimizations for the standalone version that runs on the firmware to keep up, but if it drops a few frames here and there it should still catch enough eye movements.

 

 

The algorithm detected rapid eye movements three times in a row at around 6 am, with no false positives during the night.

Download entry as Lucid Scribe Data (LSD) (386 MB).

2022-02-24 – INSPEC Video Feed

Lucid Scribe can now tap into the video feed from the INSPEC camera if it is plugged in via USB, which will allow for parallel tests against the algorithm on the device’s firmware. Recorded 3 positives and 0 false positives with audio on mute for testing.

Download entry as Lucid Scribe Data (LSD) (108 MB).

2019-02-20 – Face Recon

0400

The INSPEC can now look for faces and only looks for eye movements within the area of a face, nuking a whole host of false positives from sleep artifacts like breathing. Sleep talking still gets past it – too shy to post a clip of that, but I figure if you are sleep talking there is a good chance you are dreaming and the inhibition of motor neurons from REM atonia just isn’t working around the mouth muscles.

The video above shows some eye movements at 4 am with the algorithm looking for a face multiple times a second. In order for the facial algorithm to work, I have to bend the “neck” of the device so it can see my face in portrait mode. The flexible tripod works perfectly for that.
I sometimes feel guilty about how hard the INSPEC has to work all night, but I am making it in hope of spreading lucidity, so at least it is for a worthy cause.

2019-01-25 – Human Perspective

I added some more configuration options to the settings and the algorithm still worked! It picked up the eye movements when I was lying on my back with my head tilted slightly.

The device doesn’t have a clock when it is unplugged, so the date can now be configured if you want kind-of accurate timestamps on the files. It hovers on an insect-like tripod so the camera angle can now be controlled much better.

This recording better illustrates what the bitmaps look like that are saved to the SD card when eye-movement patterns are detected. Larger movements, especially in the mornings when there is some light from the rest of the spectrum still produce the deep-dream like images.

2019-01-16 – Deep Dream

I want to believe I almost have the perfect version of the halovision device. Down to the size of a matchbox; no screen – only a night-vision camera, infrared LEDs and a processor powerful enough to run the latest machine vision algorithms at a decent rate. It records short GIFs along with BMP stills to a SD card and triggers bright LEDs when eye-movement patterns are detected after a long enough still phase. I don’t think I can make the GIFs as high-res as these “research” grade ones, I’m afraid, but good enough to see what it was detecting to help get into position. Woo hoo!