1€ Filter Homepage

Algorithm

1€ Links and Downloads

• Our CHI 2012 1€ paper (PDF)
• Data corresponding to figure 1
• Questions? Contact Géry Casiez

References

Casiez, G., Roussel, N. and Vogel, D. (2012). 1€ Filter: A Simple Speed-based Low-pass Filter for Noisy Input in Interactive Systems. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI '12). Austin, Texas (May 5-12, 2012). New York: ACM Press, pp. 2527-2530.

Tuning the filter

To minimize jitter and lag when tracking human motion, the two parameters (fcmin and beta) can be set using a simple two-step procedure. First beta is set to 0 and fcmin (mincutoff) to a reasonable middle-ground value such as 1 Hz. Then the body part is held steady or moved at a very low speed while fcmin is adjusted to remove jitter and preserve an acceptable lag during these slow movements (decreasing fcmin reduces jitter but increases lag, fcmin must be > 0). Next, the body part is moved quickly in different directions while beta is increased with a focus on minimizing lag. First find the right order of magnitude to tune beta, which depends on the kind of data you manipulate and their units: do not hesitate to start with values like 0.001 or 0.0001. You can first multiply and divide beta by factor 10 until you notice an effect on latency when moving quickly. Note that parameters fcmin and beta have clear conceptual relationships: if high speed lag is a problem, increase beta; if slow speed jitter is a problem, decrease fcmin.

Tuning can also be done using the Pitch Pipe technique [Code on Github].

1€ Implementations verified and available on Github

• Python version (BSD Licence)
• C version (MIT licence) by Jonathan Aceituno
• C++ version (BSD Licence) by Nicolas Roussel
• C++ version using templates (MIT licence) by Jonathan Aceituno
• Java version (BSD Licence) by Stéphane Conversy from Université de Toulouse
• JavaScript version using class (BSD Licence) by Géry Casiez
• TypeScript version (BSD Licence) by Alix Goguey and Géry Casiez
• Arduino version (BSD Licence) by Géry Casiez
• Processing library by Raphaël de Courville

Other implementations not verified yet

• GDScript for Godot game engine (MIT licence) by Patryk Kalinowski
• C# version by Mitsuru Furuta from Sensorit > more code
• F# version by Mitsuru Furuta from Sensorit > more code
• Objective C version by Stefan Ivanov from RWTH Aachen University
• Non object-oriented version by Stefan Ivanov from RWTH Aachen University
• Available in the Vicon Tracker software
• Implemented in Google Chrome chrome://credits/
• Used in Chrome on all Android devices
• Available in Unreal Engine
• Available as a dataflow processing device in the ICon input management toolkit by Stéphane Huot from Université Paris-Sud 11
• Implemented in VRPN by Jan Ciger and Ryan Pavlik. Provides an example of quaternion filtering using linear and spherical interpolations. Used to filter Razer Hydra data.
• OCaml version by Thomas Pietrzak
• Abstraction PureData version (can be re-compiled on any OS) by Florent Berthaut
• Pure Data version by Jonathan Aceituno
• Matlab version by Henry Mak
• C# utility for Unity3D by Dario Mazzanti
• Plugin for Unreal by Dario Mazzanti
• Used in libavg
• Used in MiddleVR
• Implementation in pico8 (lua) by Stephan Steinbach
• Send me an e-mail to add your own implementation here

Try the on-line interactive demo

Thanks to Jonathan Aceituno!

Comparison with other filters (video)