Even a battery can be a sensor. I measured the current in a remote control battery while pressing various buttons. The typical remote control dumps a LOT of current through an infrared LED (up to 1A momentarily). The better to reach a TV or DVD player in a far corner of the room. This activity shows up clearly in the battery current.
There are new user activity recognition tools out there that are just a bit bigger than a battery and add an accelerometer. Green Goose (whose sensor kit will be available in January) and Twine (which recently succeeded on Kickstarter–you can back them until January 3) are two. Software and the user interface are going to determine how useful these tools are. Today’s example shows there is plenty of information just from monitoring the battery’s current in an existing gadget.
With the remote, there is always a startup pattern in the battery current that probably is a manufacturer’s code, then a 4ms space, then a bunch of shark-fin shapes that vary depending on what button was pushed. Filter capacitors in nearly all gadgets store some power locally, preventing a blackout when there’s activity elsewhere in the circuit. These capacitors distort the battery signal level a lot–making shark fins from square waves–but they don’t change the timing between signal edges. You can definitely see a distinct timing pattern in the digits “o” through “9.”Tips of the fins are separated by either 1ms or 2ms. Here’s a screenshot of the variable part of the signal from pressing the button “5”
Battery current when remote control button 5 pressed
Assuming the 2ms corresponds to “1” and the 1ms corresponds to a “o” , there are 20 data bits here. There are 4 bits that stay constant (1110), then 4 data bits (varies), four constant bits (1111), 4 bits that are the inverse of the data bits, and finally a constant (1111). Here are the data bits I found. Interestingly they don’t correspond to binary values for the button number. Anyone with a relevant datasheet might be able to check on this, it’s a Samsung Blu-ray remote circa 2008.
Button 0 1101
Button 1 0100
Button 2 1100
Button 3 0010
Button 4 1010
Button 5 0110
Button 6 0001
Button 7 1110
Button 8 1001
Button 9 0101
In short, your basic alkaline battery knows exactly what you’re doing, watching, and thinking. You may need to get out more. You can even zoom in see the 38 KHz infrared modulation that’s used to reduce sensitivity to ambient light. Read further for more on what is going on and how to do this on your own remote…
Here’s a nice visualization from Greg Charvat at MIT using a LED-equipped sensor to map out 2.4 GHz waves. The sensor detects the direction as well as magnitude, so you get two different colors. The 2.4 GHz frequency is used for Bluetooth, wireless sensors (especially ZigBee), Wi-Fi and almost every other gadget, if you’re in the US.
Its wavelength is a few centimeters. (In comparision, the wavelength of 60 Hz electromagnetic radiation is a few thousand MILES)
Now you can almost feel the positive and negative wavefronts thanks to this long exposure image, created by moving the sensing antenna around while the LED lights up.
Now the digital 60 Hz filter is up and running on the Arduino. The goal was to take in all frequencies of EMF, reject anything that was not 60 Hz, and show the amplitude of the 60 Hz component. Here’s a graph of what the Arduino is doing–producing the green filtered signal from the blue raw signal. It also does a moving average over three cycles so there is a bit of lag, about 0.05 seconds. Thanks to Chris Isert for getting the Arduino interrupts set up so we could control the sampling rate (500 Hz).
Raw and filtered 60 Hz signal sampled at 500 Hz and sent over serial link
It’s very sensitive to whether the antenna is being touched–higher values are when it is touched, then it drops off when not touched as shown about halfway through the graph. But if the user is also touching ground while touching the antenna, the values drop to nearly zero.
There should really be a lowpass RC filter on the EMF detector so that the filter does not get fooled by high frequency harmonics above half the sampling frequency. This may be why the green signal is a little bit higher than the tips of the blue peaks.
Here’s the Arduino code, and more information about what the filter is doing to the signal…
Here is a composite image where the EMF detector (hanging out on the left) was moved around a power strip that was turned on. Red dots show where the LED went on. This is the original EMF detector, no frequency filtering, but the signal is likely to be mostly 60 Hz based on what we have measured lately. Thanks to Anand Kulkarni for moving the sensor all over the place, and Dr. Karla Welch for making the composite image.
Magnetic fields are another three-dimensional field that you can represent using a threshold. Instead of using a lighted LED as a threshold, as in the RFID visualization, how about carving out a solid representation of the field?
(Thanks to Dan Bowen for the link!)