The Blog of Ian Mercer.

Probabilistic Home Automation

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My recent addtion of multiple hypothesis tracking enables my home automation system to not only say how many people it thinks are home and which rooms are occupied but also to assign a probability to each of those numbers. That changes the way it will work as I replace boolean values with probability comparisons to see if it is, say 95% certain before it acts on a value. It also enableds exciting new scenarios that I'll explore here.

More intelligent lighting control

In the past my home automation relied mostly on time-outs to decide when a room was no longer occupied. After a period of no activity on any of the sensors in the room (motion, strain-gauge, TV volume control, light switches, or telephones) it would start dimming the lights in that room and would turn off any audio playing. There were a couple of more complicated scenarios where it attempted to use logic to reason that someone was still in a room if that room could be closed off. And while my house does have a lot of sensors this approach really wasn't that much smarter than a cheap PIR motion light switch.

The light state machine makes it seem smarter because it will leave a light on or off when a user wants to override this relatively dumb behavior and the gradual, imperceptible dim feature makes it a lot more forgiving (or rather 'forgiven' when it makes a mistake).

Now, however with a probabilistic model of the home based on 'target tracking' it can turn lights off much sooner as it can be almost certain that the one person in that room just moved to a neighboring room. This will allow it to save even more energy over my baseline from years ago before any home automation was installed.

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I've been working on home automation for over 15 years and I'm close to achieving my goal which is a house that understands where everyone is at all times, can predict where you are going next and can control lighting, heating and other systems without you having to do or say anything. That's a true "smart home".

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Cover Image for Light sensors for Home Automation

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Microwave Doppler Sensors (RCWL-0516)

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Ian Mercer
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Optical-beam sensors

Optical-beam sensors are reliable and can cover a long-distance such as across a garage or aisle-way. When they include multiple-beams they have good false-trigger rejection.

Ian Mercer
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Cover Image for PIR Sensors for Home Automation

PIR Sensors for Home Automation

PIR sensors are cheap and easy to use but they suffer from slow response times and low repeat rates.

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Strain-gauges

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Ian Mercer
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Event blocks

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Ian Mercer
Ian Mercer
Cover Image for Logistic function - convert values to probabilities

Logistic function - convert values to probabilities

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Ian Mercer
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ATAN curve for probabilities

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Ian Mercer
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Multiple hypothesis tracking

A statistical approach to understanding which rooms are occupied in a smart house

Ian Mercer
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Cover Image for A state machine for lighting control

A state machine for lighting control

An if-this-then-that style rules machine is insufficient for lighting control. This state machine accomplishes 90% of the correct behavior for a light that is controlled automatically and manually in a home automation system.

Ian Mercer
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Home Automation States

Understanding the many different 'states' a house can have is critical to creating great home automation

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Ian Mercer
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iBeacons for Home Automation

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iBeacon meetup in Seattle - January 2015

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Home Automation Systems as a Graph

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Some new features for my home automation using an Android phone

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Connecting our dog into the home automation

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