I've been using strain-gauge sensors for some time now in my home automation system. They are invisible and highly reliable.
Some time ago I switched from the Sure Action boards to my own HX711 based processor boards and recently I added capacitive coupling between the bridge and the HX711 which removes any DC offset or temperature drift.
It works well. Next step is to create a PCB.
This new technique to compress the time series data collected by my home automation system seems to be working really well.
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".
My year long Bluetooth project that won the $20,000 HCI and Microsoft competition during lockdown has continued to grow and now reliably tracks how many people are in the house and outside and can locate any device down to room level.
Digital Twin are an online representation of a real world object, a copy of its properties in the digital world and a way to send updated and commands to it. In effect I've been making them for years but now they have a trendy name.
An overview of the many sensors I've experimented with for home automation including my favorite under-floor strain gauge, through all the usual PIR, beam and contact sensors to some more esoteric devices like an 8x8 thermal camera.
One way to reduce the volume of sensor data is to remove redundant points. In a system with timestamped data recorded on an irregular interval we can achieve this by removing co-linear points.
ESP32 provides a great platform for sensors around the house but by the time you've added a USB power brick, cable and enclosure it's quite messy. I wanted a device that I could just plug in with no exposed wires and no mounting needed so I designed one in OpenSCAD.
Filtering raw data is essential for a reliable home automation system. Here are some of the many ways you can filter sensor data.
Bluetooth sensing for home automation is a great proxy for people counting as it can detect and locate each cellphone in the house. iBeacons attached to tools, cars and pets can provide a 'find my anything' feature too.
Gas sensors come in many different flavors including CO2, VOC and particulate sensors.
Humidity sensors are great for controlling extractor fans in bathrooms and other damp spaces.
Having at least one light sensor is critical for any home automation system that controls lightng. Lights need to be turned on when it's dark not at specific times of day, especially here in Seattle when it can be dark and cloudy at any time of day.
Microwave doppler sensors can be found in some alarm sensors but there are also available very cheaply as a separate component. They offer exceptional range but suffer from false triggers requiring a probailistic approach to people sensing.
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.
PIR sensors are cheap and easy to use but they suffer from slow response times and low repeat rates.
Strain-gauges are my top-rated sensor for home automation because they are invisible, reliable and can be tuned to detect people and ignore pets.
Temperature sensors I've experimented with for home automation.
Home automation systems need to respond to events in the real world. Sometimes it's an analog value, sometimes it's binary, rarely is it clean and not susceptible to problems. Let's discuss some of the ways to convert these inputs into actions.
Another super useful function for handling sensor data and converting to probabilities is the logistic function 1/(1+e^-x). Using this you can easily map values onto a 0.0-1.0 probability range.
In a home automation system we often want to convert a measurement into a probability. The ATAN curve is one of my favorite curves for this as it's easy to map overything onto a 0.0-1.0 range.
One of my inventions recently won a $20k global competition for applications that could help in a pandemic. It uses Bluetooth to count people.
A probabilistic approach to home automation models the probability that each room is occupied and how many people are in that room.
A statistical approach to understanding which rooms are occupied in a smart house
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.
Experiments with an 8x8 IR camera for privacy-preserving people detection using cameras.
CCTV cameras are an option for detecting people but within the home there are privacy concerns that need to be addressed.
Pressure sensors can detect HVAC system operation and could potentially detect clogged filters.
