This article explores the 1€ Filter, a simple, but powerful algorithm for filtering noisy real-time signals. The article focuses on the practical implementation of the algorithm, and it covers the mathematical basis, a pseudocode implementation, and simple, pure Python implementation of the algorithm. In order to understand why and how the filter works, we recommend reading the original article (1).

**Contents**

**Keywords**: Noise filtering, 1€ Filter, 1e filter, 1 euro filter, One Euro Filter

## The 1€ Filter

The 1€ Filter is a low pass filter for filtering noisy signals in real time. It’s also a simple filter with only two configurable parameters. The signal at time \(T_i\) is denoted as a value \(X_i\) and the filtered signal as value \(\hat{X}_i\). The filter is implemented using *exponential smoothing* \[
\hat{X}_1 = X_1 \\
\hat{X}_i = α X_i + (1-α) \hat{X}_{i-1}, i≥2
\tag{exponential-smoothing}
\label{exponential-smoothing}
\] where the smoothing factor \(α∈[0, 1]\), instead of being a constant, is adaptive, i.e. dynamically computed using information about the rate of change (speed) of the signal. This aims to balance the jitter versus lag trade-off since people are more sensitive to jitter at low speeds and more sensitive to lag at high speeds. The smoothing factor is defined as \[
α = \frac{1}{1 + \dfrac{τ}{T_e}},
\tag{smoothing-factor}
\label{smoothing-factor}
\] where \(T_e\) is the sampling period computed from the time difference between the samples \[
T_e=T_i-T_{i-1}
\tag{sampling-period}
\] and \(τ\) is time constant computed using the cutoff frequency \[
τ = \frac{1}{2πf_C}.
\tag{time-constant}
\]

The cutoff frequency \(f_C\) which is designed to increase linearly as the rate of change, aka speed, increases \[
f_C=f_{C_{min}} + β|\hat{\dot{X}}_i|
\tag{cutoff-frequency}
\] where \(f_{C_{min}}>0\) is the *minimum cutoff frequency*, \(β>0\) is the *speed coefficient* and \(\hat{\dot{X}}_i\) is the filtered rate of change. The rate of change \(\hat{X}_i\) is defined as the discrete derivative of the signal \[
\dot{X}_1 = 0 \\
\dot{X}_i = \frac{X_i-\hat{X}_{i-1}}{T_e}, i≥2,
\tag{derivative}
\] which is then filtered using \(\eqref{exponential-smoothing}\) with a constant cutoff frequency \(f_{C_d},\) by default \(f_{C_d}=1\).

## The Algorithm

The One Euro Filter algorithm as pseudocode. The precise implementation of the One-Euro-filter will depend on the programming language and paradigm in question. We have written this algorithm using a functional style.

\(\operatorname{Smoothing-Factor}(f_C, T_e)\)

- \(r=2π⋅f_c⋅T_e\)
**return**\(\dfrac{r}{r+1}\)

\(\operatorname{Exponential-Smoothing}(α, X_i, \hat{X}_{i-1})\)

**return**\(α X_i + (1-α) \hat{X}_{i-1}\)

\(\operatorname{One-Euro-Filter}(T_i,X_i,T_{i-1},\hat{X}_{i-1},\hat{\dot{X}}_{i-1},f_C,β,f_{C_d})\) for \(i≥2\)

- \(T_e=T_i-T_{i-1}\)
- \(α_d=\operatorname{Smoothing-Factor}(T_e, f_{C_d})\)
- \(\dot{X}_i = \dfrac{X_i-\hat{X}_{i-1}}{T_e}\)
- \(\hat{\dot{X}}_i=\operatorname{Exponential-Smoothing}(α_d, \dot{X}_i, \hat{\dot{X}}_{i-1})\)
- \(f_C=f_{C_{min}} + β|\hat{\dot{X}}_i|\)
- \(α=\operatorname{Smoothing-Factor}(f_C, T_e)\)
- \(\hat{X}_i=\operatorname{Exponential-Smoothing}(α, X_i, \hat{X}_{i-1})\)
**return**\(T_i,\hat{X}_i,\hat{\dot{X}}_i\)

## Tuning the Filter

There are two configurable parameters in the model, the *minimum cutoff frequency* \(f_{C_{min}}\) and the *speed coefficient* \(β\). Decreasing the minimum cutoff frequency will decrease slow speed jitter. Increasing the speed coefficient will decrease speed lag.

## Python Implementation

The following object-oriented Python implementation is also available in OneEuroFilter GitHub repository. The object-oriented approach stores the previous values inside the object instead of giving them explicitly as a return value as functional implementation would. It should be relatively simple to implement this algorithm in other languages.

```
import math
def smoothing_factor(t_e, cutoff):
r = 2 * math.pi * cutoff * t_e
return r / (r + 1)
def exponential_smoothing(a, x, x_prev):
return a * x + (1 - a) * x_prev
class OneEuroFilter:
def __init__(self, t0, x0, dx0=0.0, min_cutoff=1.0, beta=0.0,
d_cutoff=1.0):
"""Initialize the one euro filter."""
# The parameters.
self.min_cutoff = float(min_cutoff)
self.beta = float(beta)
self.d_cutoff = float(d_cutoff)
# Previous values.
self.x_prev = float(x0)
self.dx_prev = float(dx0)
self.t_prev = float(t0)
def __call__(self, t, x):
"""Compute the filtered signal."""
t_e = t - self.t_prev
# The filtered derivative of the signal.
a_d = smoothing_factor(t_e, self.d_cutoff)
dx = (x - self.x_prev) / t_e
dx_hat = exponential_smoothing(a_d, dx, self.dx_prev)
# The filtered signal.
cutoff = self.min_cutoff + self.beta * abs(dx_hat)
a = smoothing_factor(t_e, cutoff)
x_hat = exponential_smoothing(a, x, self.x_prev)
# Memorize the previous values.
self.x_prev = x_hat
self.dx_prev = dx_hat
self.t_prev = t
return x_hat
```

Here we provide an example of how to use the One Euro Filter for filtering a signal in real-time.

```
# Let signal be a generator that yields a tuple (t, x)
# where t is time and x the values of the signal.
signal = ...
# Instantiate the OneEuroFilter with initial values and
# your value for parameters min_cutoff and beta.
t0, x0 = next(signal)
one_euro_filter = OneEuroFilter(
t0=t0,
x0=x0,
min_cutoff=...,
beta=...
)
# Filter the signal in real time.
for (t, x) in signal:
x_hat = one_euro_filter(t, x)
...
```

## References

1. CASIEZ, Géry, ROUSSEL, Nicolas and VOGEL, Daniel. 1€ filter: A simple speed-based low-pass filter for noisy input in interactive systems. In : *Proceedings of the sigchi conference on human factors in computing systems*. ACM, 2012. p. 2527–2530.