This library smooths raw freehand input and predicts the input's motion to minimize display latency. It turns noisy pointer input from touch/stylus/etc. into the beautiful stroke patterns of brushes/markers/pens/etc.

Be advised that this library was designed to model handwriting, and as such, prioritizes smooth, good-looking curves over precise recreation of the input.

This library is designed to have minimal dependencies; the library itself relies only on the C++ Standard Library and Abseil, and the tests use the GoogleTest framework.

Ink Stroke Modeler can be built and the tests run from the GitHub repo root with:

bazel test ...

To use Ink Stroke Modeler in another Bazel project, put the following in the WORKSPACE file to download the code from GitHub head and set up dependencies:

load("@bazel_tools//tools/build_defs/repo:git.bzl", "git_repository")

git_repository(
    name = "ink_stroke_modeler",
    remote = "https://github.com/google/ink-stroke-modeler.git",
    branch = "main",
)
load("@ink_stroke_modeler//:workspace.bzl", "ink_stroke_modeler_workspace")
ink_stroke_modeler_workspace()

If you want to depend on a specific version, you can change the options passed to git_repository. Or if you want to use a local checkout of Ink Stroke Modeler instead, use the local_repository workspace rule instead of git_repository.

Since Ink Stroke Modeler requires C++20, it must be built with --cxxopt='-std=c++20' (or similar indicating a newer version). You can put the following in your project's .bazelrc to use this by default:

build --cxxopt='-std=c++20'

Then you can include the following in your targets' deps:

• @ink_stroke_modeler//ink_stroke_modeler:stroke_modeler: ink_stroke_modeler/stroke_modeler.h
• @ink_stroke_modeler//ink_stroke_modeler:types: ink_stroke_modeler/types.h
• @ink_stroke_modeler//ink_stroke_modeler:params: ink_stroke_modeler/types.h

Ink Stroke Modeler can be built and the tests run from the GitHub repo root with:

cmake .
cmake --build .
ctest

To use Ink Stroke Modeler in another CMake project, you can add the project as a submodule:

git submodule add https://github.com/google/ink-stroke-modeler

And then include it in your CMakeLists.txt, requiring at least C++20:

set(CMAKE_CXX_STANDARD 20)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
# If you want to use installed (or already fetched) versions of Abseil and/or
# GTest (for example, if you've installed libabsl-dev and libgtest-dev), add:
# set(INK_STROKE_MODELER_FIND_ABSL ON)
# set(INK_STROKE_MODELER_FIND_GTEST ON)
add_subdirectory(ink-stroke-modeler)

Then you can depend on the following with target_link_libraries:

• InkStrokeModeler::stroke_modeler: ink_stroke_modeler/stroke_modeler.h
• InkStrokeModeler::types: ink_stroke_modeler/types.h
• InkStrokeModeler::params: ink_stroke_modeler/types.h

CMake does not have a mechanism for enforcing target visibility, but consumers should depend only on the non-test top-level targets defined in ink_stroke_modeler/CMakeLists.txt.

The CMake build uses the abstractions defined in cmake/InkBazelEquivalents.cmake to structure targets in a way that's similar to the Bazel BUILD files to make it easier to keep those in sync. The main difference is that CMake has a flat structure for target names, e.g. @com_google_absl//absl/status:statusor in Bazel is absl::statusor in CMake. This means that targets need to be given unique names within the entire project in CMake.

Ink Stroke Modeler is intended to be built into consumers from source. But sometimes a packaged version is needed, for example to integrate with other build systems that wrap CMake. To test the packaging and export of Ink Stroke Modeler targets, you can install the dependencies with something like:

sudo apt install libabsl-dev googletest

And then run:

cmake -DINK_STROKE_MODELER_FIND_DEPENDENCIES=ON \
    -DCMAKE_INSTALL_PREFIX=$HOME/cmake .
make install

Or to build fetched dependencies from source instead of using installed ones:

cmake -DCMAKE_INSTALL_PREFIX=$HOME/cmake .
make install

Set CMAKE_INSTALL_PREFIX to point the install somewhere other than system library paths.

The Ink Stroke Modeler API is in the namespace ink::stroke_model. The primary surface of the API is the StrokeModeler class, found in stroke_modeler.h. You'll also simple data types and operators in types.h, and the parameters which determine the behavior of the model in params.h.

To begin, you'll need choose parameters for the model (the parameters for your use case may vary; see params.h), instantiate a StrokeModeler, and set the parameters via StrokeModeler::Reset():

StrokeModelParams params{
    .wobble_smoother_params{
        .timeout{.04}, .speed_floor = 1.31, .speed_ceiling = 1.44},
    .position_modeler_params{.spring_mass_constant = 11.f / 32400,
                             .drag_constant = 72.f},
    .sampling_params{.min_output_rate = 180,
                     .end_of_stroke_stopping_distance = .001,
                     .end_of_stroke_max_iterations = 20},
    .stylus_state_modeler_params{.max_input_samples = 20},
    .prediction_params = StrokeEndPredictorParams()};

StrokeModeler modeler;
if (absl::Status status = modeler.Reset(params); !status.ok()) {
  // Handle error.
}

You may also call Reset() on an already-initialized StrokeModeler to set new parameters; this also clears the in-progress stroke, if any. If the Update or Predict functions are called before Reset, they will return absl::FailedPreconditionError.

To use the model, pass in an Input object to StrokeModeler::Update() each time you receive an input event. This appends to a std::vector<Result> of stroke smoothing results:

Input input{
  .event_type = Input::EventType::kDown,
  .position{2, 3},  // x, y coordinates in some consistent units
  .time{.2},  // Time in some consistent units
  // The following fields are optional:
  .pressure = .2,  //  Pressure from 0 to 1
  .orientation = M_PI  // Angle in plane of screen in radians
  .tilt = 0,  // Angle elevated from plane of screen in radians
};
if (absl::Status status = modeler.Update(input, smoothed_stroke);
    !status.ok()) {
  // Handle error...
  return status;
}
// Do something with the smoothed stroke so far...
return absl::OkStatus();

Inputs are expected to come in a stream, starting with a kDown event, followed by zero or more kMove events, and ending with a kUp event. If the modeler is given out-of-order inputs or duplicate inputs, it will return absl::InvalidArgumentError. Input.time may not be before the time of the previous input.

Inputs are expected to come from a single stroke produced with an input device. Extraneous inputs, like touches with the palm of the hand in touch-screen stylus input, should be filtered before passing the correct input events to Update. If the input device allows for multiple strokes at once (for example, drawing with two fingers simultaneously), the inputs for each stroke must be passed to separate StrokeModeler instances.

The time values of the returned Result objects start at the time of the first input and end either at the time of the last Input (for in-progress strokes) or a hair after (for completed ones). The position of the first Result exactly matches the position of the first Input and the position of the last Result attempts to very closely match the match the position of the last Input, wile the Result objects in the middle have their position adjusted to smooth out and interpolate between the input values.

To construct a prediction, call StrokeModeler::Predict() while an input stream is in-progress. This takes a std::vector<Result> and replaces the contents with the new prediction:

if (absl::Status status = modeler.Predict(predicted_stroke); !status.ok) {
  // Handle error...
  return status;
}
// Do something with the new prediction...
return absl::OkStatus();

If no input stream is in-progress, it will instead return absl::FailedPreconditionError.

When the model is configured with StrokeEndPredictorParams, the last Result returned by Predict will have a time slightly after the most recent Input. (This is the same as the points that would be added to the Results returned by Update if the most recent input was kUp instead of kMove.) When the model is configured with KalmanPredictorParams, the prediction may take more time to "catch up" with the position of the last input, then take an additional interval to extend the stroke beyond that position.

The state of the most recent stroke can be represented by concatenating the vectors of Results returned by a series of successful calls to Update, starting with the most recent kDown event. If the input stream does not end with a kUp event, you can optionally append the vector of Results returned by the most recent call to Predict.

(Note: Mathematical formulas below are rendered with MathJax, which GitHub's native Markdown rendering does not support. You can view the rendered version on GitHub pages.)

The design goals for this library were:

• Minimize overall latency.
• Accurate reproduction of intent for drawing and handwriting (preservation of inflection points).
• Smooth digitizer noise (high frequency).
• Smooth human created noise (low and high frequency). For example:
  • For most people, drawing with direct mouse input is very accurate, but looks bad.
  • For most people, drawing with a real pen looks bad. We should capture some of the differences between professional and amateur pen control.

At a high level, we start with an assumption that all reported input events are estimates (from both the person and hardware). We keep track of an idealized pen constrained by a physical model of motion (mass, friction, etc.) and feed these position estimates into this idealized pen. This model lags behind reported events, as with most smoothing algorithms, but because we have the modeled representation we can sample its location at arbitrary and even future times. Using this, we predict forward from the delayed model to estimate where we will go. The actual implementation is fairly simple: a fixed timestep Euler integration to update the model, in which input events are treated as spring forces on that model.

Like many real-time smoothing algorithms, the stroke modeler lags behind the raw input. To compensate for this, the stroke modeler has the capability to predict the upcoming points, which are used to "catch up" to the raw input.

Typically, we expect the stroke modeler to receive raw inputs in real-time; for each input it receives, it produces one or more modeled results.

When asked for a prediction, the stroke modeler computes it based on its current state -- this means that the prediction is invalid as soon as the next input is received.

NOTE: The definitions and algorithms in this document are defined as though the input streams are given as a complete set of data, even though the engine actually receives raw inputs one by one. It's written this way for ease of understanding.

However, one of the features of these algorithms is that any result that has been constructed will remain the same even if more inputs are added, i.e. if we view the algorithm as a function $$F$$ that maps from an input stream to a set of results, then $$F({i_0, \ldots, i_j}) \subseteq F({i_0, \ldots, i_j, i_{j + 1}})$$. Applying the algorithm in real-time is then effectively the same as evaluating the function on increasingly large subsets of the input stream, i.e. $$F({i_0}), F({i_0, i_1}), F({i_0, i_1, i_2}), \ldots$$

Definition: We define the pressure $$\sigma$$ as the amount of force which is applied to the writing surface with the stylus, as reported by the platform. We expect $$\sigma \in [0, 1]$$ for devices that report pressure, with 0 and 1 representing the minimum and maximum detectable pressure, or $$\sigma = \emptyset$$ if the device does not report pressure. $$\square$$

Definition: We define the tilt $$\theta$$ as the angle between the stylus, and a vector perpendicular to the writing surface. We expect $$\theta \in [0, \frac{\pi}{2}]$$. $$\square$$

Definition: We define the orientation $$\phi$$ as the counter-clockwise angle between the projection of the stylus onto the writing surface, and the writing surface's x-axis. We expect $$\phi \in [0, 2 \pi)$$. $$\square$$

NOTE: In the code, we use a sentinel value of -1 to represent unreported pressure, tilt, or orientation.

Definition: A raw input is a struct $${p, t, \sigma, \theta, \phi}$$, where $$p$$ is a Vec2 position, $$t$$ is a Time, $$\sigma$$ is a float representing the pressure, $$\theta$$ is a float representing tilt, and $$\phi$$ is a float representing orientation. For any raw input $$i$$, we denote its position, time, pressure, tilt, and orientation as $$i^p$$, $$i^t$$, $$i^\sigma$$, $$i^\theta$$, and $$i^\phi$$ respectively. $$\square$$

Definition: An input stream is a finite sequence of raw inputs, ordered by time strictly non-decreasing, such that the first raw input is the kDown, and each subsequent raw input is a kMove, with the exception of the final one, which may alternatively be the kUp. Additionally, we say that a complete input stream is a one such that the final raw input is the kUp. $$\square$$

Definition: A tip state is a struct $${p, v, t}$$, where $$p$$ a Vec2 position, $$v$$ is a Vec2 velocity, and $$t$$ is a Time. Similarly to raw input, for a tip state $$q$$, we denote its position, velocity, and time as $$q^p$$, $$q^v$$, and $$q^t$$, respectively. $$\square$$

Definition: A stylus state is a struct $${\sigma, \theta, \phi}$$, where $$\sigma$$ is a float representing the pressure, $$\theta$$ is a float representing tilt, and $$\phi$$ is a float representing orientation. As above, for a stylus state $$s$$, we denote its pressure, tilt, and orientation as $$s^\sigma$$, $$s^\theta$$, and $$s^\phi$$ respectively. $$\square$$

Definition: We define the rounding function $$R$$ such that $$R(x) = \begin{cases} \lfloor x \rfloor, & x - \lfloor x \rfloor \leq \frac{1}{2} \ \lceil x \rceil & o.w. \end{cases}$$. $$\square$$

Definition: We define the linear interpolation function $$L$$ such that $$L(a, b, \lambda) = a + \lambda (b - a)$$. $$\square$$

Definition: We define the normalization function $$N$$ such that $$N(a, b, \lambda) = \frac{\lambda - a}{b - a}$$. $$\square$$

Definition: We define the clamp function $$C$$ such that $$C(a, b, \lambda) = \max(a, \min(b, \lambda))$$. $$\square$$

The positions of the raw input tend to have some noise, due to discretization on the digitizer or simply an unsteady hand. This noise produces wobbles in the model and prediction, especially at slow speeds.

To reduce high-frequency noise, we take a time-variant moving average of the position instead of the position itself -- this serves as a low-pass filter. This, however, introduces some lag between the raw input position and the filtered position. To compensate for that, we use a linearly-interpolated value between the filtered and raw position, based on the moving average of the velocity of the input.

Algorithm #1: Given an input stream $${i_0, i_1, \ldots, i_n}$$, a duration $$\Delta$$ over which to take the moving average, and minimum and maximum velocities $$v_{min}$$ and $$v_{max}$$ we construct the noise-filtered input stream $${i_0', i_1', \ldots, i_n'}$$ as follows:

NOTE: The values of $$\Delta$$, $$v_{min}$$, and $$v_{max}$$ are taken from WobbleSmootherParams:

• $$\Delta$$ = timeout
• $$v_{min}$$ = speed_floor
• $$v_{max}$$ = speed_ceiling

For each raw input $$i_j$$, we can then find the set of raw inputs that occurred within the previous duration $$\Delta$$:

$$I_j^{<\Delta} = \left{i_k \middle| i_j^t - i_k^t \in [0, \Delta]\right}$$

We then calculate the moving averages of position $$\overline p_j$$ and velocity $$\overline v_j$$:

$$\begin{align*} \overline p_j & = \begin{cases} i_j^p, & j = 0 \vee j = n \\ \frac{1}{|I_j^{<\Delta}|} \sum\limits_k i_k^p \left[i_k \in I_j^{<\Delta} \right], & \mathrm{o.w.} \end{cases} \\ \overline v_j & = \begin{cases} 0, & j = 0 \\ \frac{1}{|I_j^{<\Delta}|} \sum\limits_k \frac{|i_k^p - i_{k - 1}^p|} {i_k^t - i_{k - 1}^t} \left[i_k \in I_j^{<\Delta} \right], & \mathrm{o.w.} \end{cases} \end{align*}$$

NOTE: In the above equations, the square braces denote the Iverson bracket.

We then normalize $$\overline v_j$$ between $$v_{min}$$ and $$v_{max}$$, and find the filtered position $$p'_j$$ by linearly interpolating between $$i_j^p$$ and $$\overline p_j$$:

$$\begin{align*} \lambda_j & = \begin{cases} 0, & \overline v_j < v_0 \\ 1, & \overline v_j > v_1 \\ N(v_{min}, v_{max}, v_j), & \mathrm{o.w.} \end{cases} \\ p'_j & = L\left(\overline p_j, i_j^p, \lambda_j\right) \end{align*}$$

Finally, we construct raw input $$i'_j = \left{p'_j, i_j^t, i_j^\sigma\right}. \square$$

Input packets may come in at a variety of rates, depending on the platform and input device. Before performing position modeling, we upsample the input to some minimum rate.

Algorithm #2: Given an input stream $$I = {i_0, i_1, \ldots, i_n}$$, and a maximum time between inputs $$\Delta_{max}$$, we construct the upsampled input stream $$U \supseteq I$$ as follows:

NOTE: The value of $$\Delta_{target}$$ is 1 / SamplingParams::min_output_rate.

We construct a sequence $${a_1, a_2, \ldots, a_n}$$ such that $$a_j = \left \lceil \frac{i_j^t - i_{j - 1}^t}{\Delta_{target}} \right \rceil$$, representing the required number of interpolations between each pair of raw inputs.

We then construct a sequence of sequences $${u_1, u_2, \ldots, u_n}$$ such that $$u_j = {L(i_{j - 1}, i_j, \frac{k}{a_j} , | , k = 1, 2, \ldots, a_j}$$. Finally, we construct the upsampled input stream by concatenation: $$U = {i_0, u_1, u_2, \ldots, u_n}$$. $$\square$$

The position of the pen is modeled as a weight connected by a spring to an anchor. The anchor moves along the resampled raw inputs, pulling the weight along with it across a surface, with some amount of friction. We then use Euler integration to solve for the position of the pen.

NOTE: For simplicity, we say that the spring has a relaxed length of zero, and that the anchor is unafected by outside forces such as friction or the drag from the pen.

{height="200"}

Lemma: We can model the position of the pen with the following ordinary differential equation:

$$\frac{d^2s}{dt^2} = \frac{k_s}{m_{pen}}(\Phi(t) - s(t)) - k_d \frac{ds}{dt}$$

where $$t$$ is the time, $$s(t)$$ is the position of the pen, $$\Phi(t)$$ is the position of the anchor, $$m_{pen}$$ is a the mass of the pen, $$k_s$$ is the spring constant, and $$k_d$$ is the drag constant.

Proof: From the positions of the pen and the anchor, we can use Hooke's Law to find the force exerted by the spring:

$$F = k_s (\Phi(t) - s(t))$$

Combining the above with Newton's Second Law of Motion, we can then find the acceleration of the pen $$a(t)$$:

$$F = m_{pen} \cdot a(t) \\ a(t) = \frac{k_s}{m_{pen}}(\Phi(t) - s(t))$$

We also apply a drag to the pen, applying an acceleration of $$-k_dv(t)$$, where $$v(t)$$ is the velocity of the pen. This gives us:

$$a(t) = \frac{k_s}{m_{pen}}(\Phi(t) - s(t)) - k_dv(t)$$

NOTE: This isn't exactly how you would calculate friction of an object being dragged across a surface. However, if you assume that $$m_{pen} = 1$$, then it matches the form of the equation for a sphere moving through a viscous fluid. See Kinetic Friction and Stokes' Law.

From the equations of motion, we know that $$v(t) = \frac{ds}{dt}$$ and $$a(t) = \frac{dv}{dt} = \frac{d^2s}{dt^2}$$. By substitution, we arrive at the ordinary differential equation stated above. $$\square$$

Algorithm #3: Given an incomplete input stream $${i_0, i_1, \ldots, i_n}$$, initial position $$s_0$$, and initial velocity $$v_0$$, we construct tip states $${q_0, q_1, \ldots}$$ as follows:

NOTE: When modeling a stroke, we choose $$s_0 = i_0^p$$ and $$v_0 = 0$$, such that pen is at stationary at the first raw input point. The StrokeEndPredictor also makes use of this algorithm with different initial values, however.

We define the function $$\Phi$$ such that $$\forall j, \Phi(i_j^t) = i_j^p$$. From above, we know that we can model the position $$s(t)$$ with an ordinary differential equation:

$$\frac{d^2s}{dt^2} = \frac{k_s}{m_{pen}}(\Phi(t) - s(t)) - k_d \frac{ds}{dt}$$

Because the spring constant and mass are so closely coupled in our model, we combine them into a single "shape mass" constant $$M = \frac{m_{pen}}{k_s}$$, giving us:

$$\frac{d^2s}{dt^2} = \frac{\Phi(t) - s(t)}{M} - k_d \frac{ds}{dt}$$

NOTE: The values of $$M$$ and $$k_d$$ are PositionModelerParams::spring_mass_constant and PositionModelerParams::drag_constant, respectively.

We define $$s_j = s(i_j^t)$$, $$v_j = \frac{ds}{dt}(i_j^t)$$, and $$a_j = \frac{d^2s}{dt^2}(i_j^t)$$, and use the given $$s_0$$ and $$v_0$$. Recalling that $$\Phi(i_j^t) = i_j^p$$, we can iteratively construct the each following position:

$$\begin{align*} \Delta_j & = i_j^t - i_{j - 1}^t \\ a_j & = \frac{i_j^p - s_{j - 1}}{M} - k_d v_{j - 1} \\ v_j & = v_{j - 1} + \Delta_j a_j \\ s_j & = s_{j - 1} + \Delta_j v_j \end{align*}$$

Finally, we construct tip state $$q_j = {s_j, i_j^t, v_j}.\square$$

The position modeling algorithm, like many real-time smoothing algorithms, tends to trail behind the raw inputs by some distance. At the end of the stroke, we iterate on the position modeling algorithm a few additional times, using the final raw input position as the anchor, to allow the stroke to "catch up."

Algorithm #4: Given the final anchor position $$\omega$$, the final tip state $$q_{final}$$, the maximum number $$K_{max}$$ of end-of-stroke tip states to create, the initial time between tip states $$\Delta_{initial}$$, and a stopping distance $$d_{stop}$$, we construct the next tip state $$q_{next}$$ as follows:

NOTE: The values of $$\Delta_{initial}$$, $$K_{max}$$, and $$d_{stop}$$ are taken from SamplingParams:

• $$\Delta_{initial}$$ = min_output_rate
• $$K_{max}$$ = end_of_stroke_max_iterations
• $$d_{stop}$$ = end_of_stroke_stopping_distance

Let $$\Delta_{next} = \Delta_{initial}$$. Using the update equations from algorithm #3, we construct a candidate tip state $$q_c$$:

$$\begin{align*} a & = \frac{\omega - q_{final}^p}{M} - k_d q_{final}^v \\ v & = q_{final}^v + \Delta_{next} a \\ s & = q_{final}^p + \Delta_{next} v \\ q_c & = {s, q_{final}^t + \Delta_{next}, v} \end{align*}$$

If the distance traveled $$|q_c - q_{final}|$$ is less than $$d_{stop}$$, we discard the candidate, stop iterating, and declare the stroke complete. Further iterations are unlikely to yield any significant progress.

We then construct the segment $$S$$ connecting $$q_{final}^p$$ and $$q_c^p$$, and find the point $$S_\omega$$ on $$S$$ nearest to $$\omega$$. If $$S_\omega \neq q_c^p$$, it means that the candidate has overshot the anchor. We discard the candidate, and find a new candidate using a smaller timestep, setting $$\Delta_{next}$$ to one half its previous value. This is repeated until either we find a candidate that does not overshoot the anchor, or the minimum distance traveled check fails.

Once we have found a suitable candidate, we let $$q_{next} = q_c$$.

If the remaining distance $$|\omega - q_{next}^p|$$ is greater than $$d_{stop}$$ and the total number of iterations performed, including discarded candidates, is not greater than $$K_{max}$$, we use this same process to construct an additional tip state for the end-of-stroke, substituting $$q_{next}$$ and $$\Delta_{next}$$ for $$q_{final}$$ and $$\Delta_{initial}$$, respectively. $$\square$$

As mentioned above, the position modeling algorithm tends to trail behind the raw inputs. In addition to correcting for this at the end-of-stroke, we construct a set of predicted tip states every frame to present the illusion of lower latency.

The are two predictors: the StrokeEndPredictor (found in internal/prediction/stroke_end_predictor.h) uses a slight variation of the end-of-stroke algorithm, and the KalmanPredictor (found in internal/prediction/kalman_predictor.h) uses a Kalman filter to estimate the current state of the pen tip and construct a cubic prediction.

Regardless of the strategy used, the predictor is fed each position and timestamp of raw input as it is received by the model. The model's most recent tip state is given to the predictor when the predicted tip states are constructed.

The StrokeEndPredictor constructs a prediction using algorithm #4 to model what the stroke would be if the last raw input had been a kUp, letting $$\omega$$ be the most recent tip state, and $$q_{final}$$ be the last raw input.

The KalmanPredictor uses a pair of Kalman filters, one each for the x- and y-components, to estimate the current state of the pen from the given inputs.

The Kalman filter implementation operates on the assumption that all states have one unit of time between them; because of this, we need to rescale the estimation to account for the actual input and output rates. Additionally, our experiments suggest that linear predictions look and feel better than non-linear ones; as such, we also provide weight factors that may be used to reduce the effect of the acceleration and jerk coefficients on the estimate and prediction.

Once we have the estimated state, we construct the prediction in two parts, each of which is a sequence of tip states described by a cubic polynomial curve. The first smoothly connects the last tip state to the estimate state, and the second projects where the expected position of the pen is in the future.

Algorithm #5: Given a process noise factor $$\epsilon_p$$ and measurement noise factor $$\epsilon_m$$, we construct the Kalman filter as follows:

The difference in time between states, $$\Delta_t$$, is always assumed to be equal to 1.

The state transition model is derived from the equations of motion: given $$s$$ = position, $$v$$ = velocity, $$a$$ = acceleration, and $$j$$ = jerk, we can find the new values $$s'$$, $$v'$$, $$a'$$, and $$j'$$:

$$\begin{align*} s' & = s + v \Delta_t + \frac{a \Delta_t^2}{2} + \frac{j \Delta_t^3}{6} \\ v' & = v + a \Delta_t + \frac{j \Delta_t^2}{2} \\ a' & = a + j \Delta_t \\ j' & = j \\ \end{align*}$$

The process noise covariance is modeled as noisy force; from the equations of motion, we can say that the impact of that noise on each state is equal to:

$$\epsilon_p \begin{bmatrix}\frac{\Delta_t^3}{6} \ \frac{\Delta_t^2}{2} \\ \Delta_t \ 1 \end{bmatrix} \begin{bmatrix}\frac{\Delta_t^3}{6} & \frac{\Delta_t^2}{2} & \Delta_t & 1 \end{bmatrix}$$

The measurement model is simply $$\begin{bmatrix}1 & 0 & 0 & 0\end{bmatrix}$$, representing the fact that we only observe the position of the input.

The measurement noise covariance is set to $$\epsilon_m$$. $$\square$$

NOTE: The values of $$\epsilon_p$$ and $$\epsilon_m$$ are taken from KalmanPredictorParams:

• $$\epsilon_p$$ = process_noise
• $$\epsilon_m$$ = measurement_noise

Algorithm #6: Given the input stream $${i_0, i_1, \ldots, i_n}$$, the Kalman filter's estimation $$E_K = {s_K, v_K, a_K, j_K}$$, the number of samples to use $$N_{max}$$, and weights $$w_a$$ and $$w_j$$ for the acceleration and jerk, we construct the state estimate $$E = {s, v, a, j}$$ as follows:

We first determine the average amount of time $$\Delta_a$$ between the $$N_{max}$$ most recent raw inputs:

$$\begin{align*}N_{actual} & = \max(n, N_{max}) \\ \Delta_a & = \frac{i_n^t - i_{n - N_{actual}}^t}{N_{actual}} \end{align*}$$

We then construct the state estimate:

$$E = \left { s_K, \frac{v_K}{\Delta_a}, w_a \frac{a_K}{\Delta_a^2}, w_j \frac{j_K}{\Delta_a^3} \right } \square$$

NOTE: The values of $$N_{max}$$, $$w_a$$, and $$w_j$$ are taken from KalmanPredictorParams:

• $$N_{max}$$ = max_time_samples
• $$w_a$$ = acceleration_weight
• $$w_j$$ = jerk_weight

Algorithm #7: Given the last tip state produced $$q$$, the target time between inputs $$\Delta_t$$, and the estimated pen position $$s_K$$ and velocity $$v_K$$, we construct the connecting tip states $${p_1, p_2, \ldots}$$ as follows:

NOTE: The value of $$\Delta_t$$ is 1 / SamplingParams::min_output_rate.

We first determine how many points to sample along the curve, based on the velocities at the endpoints, and the distance between them:

$$n = \left \lceil \frac{|s_K - q^p|}{\Delta_t \max(|q^v|, |v_K|)} \right \rceil$$

From this, we say that the time when the connecting curve reaches the estimated pen position is $$t_{end} = n \cdot \Delta_t$$. We then construct a cubic polynomial function $$F_C$$ such that:

$$\begin{align*} F_C(0) & = q^p \\ \frac{d F_C}{d t}(0) & = q^v \\ F_C(t_{end}) & = s_K \\ \frac{d F_C}{d t}(t_{end}) & = v_K \end{align*}$$

NOTE: The full derivation of $$F_C$$ can be found in the comments of KalmanPredictor::ConstructCubicConnector, in internal/prediction/kalman_predictor.cc.

We construct the times at which to sample the curve $$t_i = i \cdot n \cdot \Delta_t, i \in {1, 2, \ldots, n}$$, and construct the tip states $$p_i = {F_C(t_i), \frac{d F_C}{d t}(t_i), q^t + t_i}$$. $$\square$$

Algorithm #8: Given the number of raw inputs received so far $$n$$, the last raw input received $$i_R$$, the target prediction duration $$t_p$$, the estimated state $${s_K, v_K, a_K, j_K}$$, the desired number of samples $$N_s$$, the maximum distance $$\varepsilon_{max}$$ between the estimated position and the last observed state, the minimum and maximum speeds $$V_{min}$$ and $$V_{max}$$, maximum deviation from the linear prediction $$\mu_{max}$$, and baseline linearity confidence $$B_\mu$$, we estimate our confidence $$\gamma$$ in the prediction using the following heuristic:

There are four coefficients that factor into the confidence:

• A sample ratio $$\gamma_s$$, which captures how many raw inputs have been observed so far. The rationale for this coefficient is that, as we accumulate more observations, the error in any individual observation affects the estimate less.
• An error coefficient $$\gamma_\varepsilon$$, based on the distance between the estimated state and the last observed state. The rationale for this coefficient is that it should be indicative of the variance in the distribution used for the estimate.
• A speed coefficient $$\gamma_V$$, based on the approximate speed that the prediction travels at. The rationale for this coefficient is that when the prediction travels slowly, changes in direction become more jarring, appearing wobbly.
• A linearity coefficient $$\gamma_\mu$$, based on how the much the cubic prediction differs from a linear one. The rationale for this coefficient is that testers have expressed that linear predictions look and feel better.

We first construct the final positions $$s_l$$ and $$s_c$$, of the linear and cubic predictions, respectively:

$$\begin{align*} s_l & = s_K + t_p v_K \\ s_c & = s_K + t_p v_K + \frac{1}{2} t_p^2 a_K + \frac{1}{6} t_p^3 j_K \\ \end{align*}$$

We then construct the coefficients as follows:

$$\begin{align*} \gamma_s & = \frac{n}{N_s} \\ \gamma_\varepsilon & = C(1 - N(0, \varepsilon_{max}, |s_K - i_R^p|)) \\ \gamma_V & = C(N(V_{min}, V_{max}, \frac{|s_c - s_K|}{t_p})) \\ \gamma_\mu & = L(B_\mu, 1, C(0, 1, N(0, \mu_{max}, |s_c - s_l|))) \\ \end{align*}$$

Finally, we take the product of the coefficients to find the confidence $$\gamma = \gamma_s \gamma_\varepsilon \gamma_V \gamma_\mu$$. $$\square$$

NOTE: The value of $$t_p$$ = KalmanPredictorParams::prediction_interval, and the values of $$N_s$$, $$\varepsilon_{max}$$, $$V_{min}$$, $$V_{max}$$, $$\mu_max$$, and $$B_\mu$$ are taken from KalmanPredictorParams::ConfidenceParams:

• $$N_s$$ = desired_number_of_samples
• $$\varepsilon_{max}$$ = max_estimation_distance
• $$V_{min}$$ = min_travel_speed
• $$V_{max}$$ = max_travel_speed
• $$\mu_{max}$$ = max_linear_deviation
• $$B_\mu$$ = baseline_linearity_confidence

Algorithm #9: Given the target time between inputs $$\Delta_t$$, the time at which prediction starts $$t_0$$, the target prediction duration $$t_p$$, the confidence $$\gamma$$, and the estimated pen position $$s_K$$, velocity $$v_K$$, acceleration $$a_K$$, and jerk $$j_K$$, we construct the prediction tip states $${p_1, p_2, \ldots}$$ as follows:

We first determine the number of tip states $$n$$ to construct:

$$n = \left \lceil \gamma \frac{t_p}{\Delta_t} \right \rceil$$

We then iteratively construct a cubic prediction of the state at intervals of $$\Delta_t$$:

$$\begin{align*} s_{i+1} & = s_i + \Delta_t v_i + \frac{1}{2} \Delta_t^2 a_i + \frac{1}{6} \Delta_t^3 j_i \\ v_{i+1} & = v_i + \Delta_t a_i + \frac{1}{2} \Delta_t^2 j_i \\ a_{i+1} & = a_i + \Delta_t j_i \\ j_{i+1} & = j_i \\ \end{align*}$$

We let $$s_0 = s_K$$, $$v_0 = v_K$$, $$a_0 = a_K$$, and $$j_0 = j_K$$, and then finally construct the tip states $$p_i = {s_i, v_i, t_0 + i \Delta_t}$$ for $$i \in {1, 2, \ldots, n}$$. $$\square$$

Once the tip states have been modeled, we need to construct stylus states for each of them. For input streams that contain stylus information, we find the closest pair of sequential raw inputs within a set window, and interpolate between them to get pressure, tilt, and orientation. For input streams that don't contain any stylus information, we simply leave the pressure, tilt, and orientation unspecified.

Algorithm #10: Given an input stream $${i_0, i_1, \ldots, i_n}$$ with stylus information, a tip state $$q$$, and the number of raw inputs $$n_{search}$$ to use as the search window, we construct the stylus state $$s$$ as follows:

NOTE: The value of $$n_{search}$$ is StylusStateModelerParams::max_input_samples.

We first construct line segments $${s_1, s_2, \ldots, s_n}$$ from the positions of the input stream, such that $$s_j$$ starts at $$i_{j - 1}$$ and ends at $$i_j$$.

We next find the index $$k &gt; n - n_{search}$$ of the closest segment, such that for all $$j &gt; n - n_{search}, \mathrm{distance}(s_k, q^p) \leq \mathrm{distance}(s_j, q^p)$$.

We define $$r$$ be the ratio of $$s_k$$'s length at which the closest point to $$q^p$$ lies, and use this to interpolate between the stylus values of $$i_{k - 1}$$ and $$i_k$$, allowing for the orientation to "wrap" at $$0 = 2 \pi$$:

$$\begin{align*} \rho & = L(i_{k - 1}^\rho, i_k^\rho, r) \\ \theta & = L(i_{k - 1}^\theta, i_k^\theta, r) \\ \phi & = \begin{cases} L(i_{k - 1}^\phi + 2 \pi, i_k^\phi, r), & i_k^\phi - i_{k - 1}^\phi > \pi \\ L(i_{k - 1}^\phi, i_k^\phi + 2 \pi, r), & i_k^\phi - i_{k - 1}^\phi < -\pi \\ L(i_{k - 1}^\phi, i_k^\phi, r), & \mathrm{o.w.} \\ \end{cases} \\ \end{align*}$$

Finally, we construct the stylus state $$s = {\rho, \theta, \mod(\phi, 2 \pi)}$$. $$\square$$