Generating a breathing alert

1. A method comprising:

training, by a processing apparatus, a breathing model on training video streams of a plurality of subjects;

receiving, by the processing apparatus, a first video stream of a subject;

estimating, by the processing apparatus, a breathing signal from the first video stream using the breathing model;

determining, by the processing apparatus, one of a large-scale motion and/or a breathing event of the subject based on the breathing signal using the breathing model;

generating, by the processing apparatus, an alert if both no breathing event is identified and no large-scale motion of the subject is identified within an event time interval;

presenting, by the processing apparatus, the first video stream, a breathing report, and/or an audio stream, wherein the breathing report comprises a numerical representation, a heat map, a graph, and/or a streaming sinusoidal graph of a breath rate, a maximum inter-breath interval, a minimum inter-breath interval, inter-breath interval statistics, and inter-breath interval histogram, motion frequency, motion magnitude, motion duration, sleep length, sleep quality, sleep intervals, a sleep position, a sleep status, and/or apnea event data;

detecting, by a processing apparatus, a sleep position and/or a sleep status of the subject from the first video stream, wherein the sleep position comprises thrashing, a stomach orientation, a back orientation, and/or a side orientation and the sleep status comprises asleep and awake; and

communicating, by the processing apparatus, a notification in response to a specified sleep position, a specified sleep status, a change of the sleep position, and/or a change of sleep status.

2. The method of claim 1 , the method further comprising:

training, by a processing apparatus, a crying model on a plurality of subjects crying;

receiving, by the processing apparatus, an audio stream; and

identifying, by the processing apparatus, the subject crying from the audio stream using the crying model.

3. The method of claim 1 , the method further comprising:

training, by a processing apparatus, a smiling model on a plurality of subjects smiling; and

identifying, by the processing apparatus, the subject smiling from the first video stream using the smiling model.

4. The method of claim 1 , the method further comprising:

measuring, by a processing apparatus, ambient light; and

in response to the ambient light being less than a light threshold, activating, by the processing apparatus, an infrared light, wherein the first video stream captures the subject in infrared.

5. The method of claim 1 , the method further comprising:

detecting, by the processing apparatus, a face from the first video stream;

scaling, by the processing apparatus, a face region comprising the face to a specified pixel size;

calculating, by the processing apparatus, a skin histogram on a color space of face region;

selecting, by the processing apparatus, vectors of pixels based on the skin histogram;

iteratively generating, by the processing apparatus, a skin mask from the vectors; and

iteratively tracking, by the processing apparatus, the face.

6. An apparatus comprising:

a processing apparatus;

a memory storing code executable by the processing apparatus to perform:

training a breathing model on training video streams of a plurality of subjects;

receiving a first video stream of a subject;

estimating a breathing signal from the first video stream using the breathing model;

determining one of a large-scale motion and/or a breathing event of the subject based on the breathing signal using the breathing model;

generating an alert if both no breathing event is identified and no large-scale motion of the subject is identified within an event time interval;

presenting the first video stream, a breathing report, and/or an audio stream, wherein the breathing report comprises a numerical representation, a heat map, a graph, and/or a streaming sinusoidal graph of a breath rate, a maximum inter-breath interval, a minimum inter-breath interval, inter-breath interval statistics, and inter-breath interval histogram, motion frequency, motion magnitude, motion duration, sleep length, sleep quality, sleep intervals, a sleep position, a sleep status, and/or apnea event data;

detecting a sleep position and/or a sleep status of the subject from the first video stream, wherein the sleep position comprises thrashing, a stomach orientation, a back orientation, and/or a side orientation and the sleep status comprises asleep and awake; and

communicating a notification in response to a specified sleep position, a specified sleep status, a change of the sleep position, and/or a change of sleep status.

7. The apparatus of claim 6 , the processing apparatus further:

training a crying model on a plurality of subjects crying;

receiving an audio stream; and

identifying the subject crying from the audio stream using the crying model.

8. The apparatus of claim 6 , the processing apparatus further:

training a smiling model on a plurality of subjects smiling; and

identifying the subject smiling from the first video stream using the smiling model.

9. The apparatus of claim 6 , the processing apparatus further:

measuring ambient light; and

in response to the ambient light being less than a light threshold, activating an infrared light, wherein the first video stream captures the subject in infrared.

10. The apparatus of claim 6 , the processing apparatus further:

detecting a face from the first video stream;

scaling a face region comprising the face to a specified pixel size;

calculating a skin histogram on a color space of face region;

selecting vectors of pixels based on the skin histogram;

iteratively generating a skin mask from the vectors; and

iteratively tracking the face.

11. A system comprising:

a camera;

a processing apparatus;

a memory storing code executable by the processing apparatus to perform:

training a breathing model on training video streams of a plurality of subjects;

receiving a first video stream from the camera of a subject;

estimating a breathing signal from the first video stream using the breathing model;

determining one of a large-scale motion and/or a breathing event of the subject based on the breathing signal using the breathing model;

generating an alert if both no breathing event is identified and no large-scale motion of the subject is identified within an event time interval;

presenting the first video stream, a breathing report, and/or an audio stream, wherein the breathing report comprises a numerical representation, a heat map, a graph, and/or a streaming sinusoidal graph of a breath rate, a maximum inter-breath interval, a minimum inter-breath interval, inter-breath interval statistics, an inter-breath interval histogram, motion frequency, motion magnitude, motion duration, sleep length, sleep quality, sleep intervals, a sleep position, a sleep status, and/or apnea event data;

detecting a sleep position and/or a sleep status of the subject from the first video stream, wherein the sleep position comprises thrashing, a stomach orientation, a back orientation, and/or a side orientation and the sleep status comprises asleep and awake; and

communicating a notification in response to a specified sleep position, a specified sleep status, a change of the sleep position, and/or a change of sleep status.

12. The system of claim 11 , the processing apparatus further:

training a crying model on a plurality of subjects crying;

receiving an audio stream; and

identifying the subject crying from the audio stream using the crying model.

13. The system of claim 11 , the processing apparatus further:

training a smiling model on a plurality of subjects smiling; and

identifying the subject smiling from the first video stream using the smiling model.

14. The system of claim 11 , the processing apparatus further:

measuring ambient light; and

in response to the ambient light being less than a light threshold, activating an infrared light, wherein the first video stream captures the subject in infrared.

15. The system of claim 11 , the processing apparatus further:

detecting a face from the first video stream;

scaling a face region comprising the face to a specified pixel size;

calculating a skin histogram on a color space of face region;

selecting vectors of pixels based on the skin histogram;

iteratively generating a skin mask from the vectors; and

iteratively tracking the face.