turbotouchpredictor v0.0.2

TurboTouch predictor typescript version

Provides a typescript implementation for the TurboTouch predictor.

Install

npm install turbotouchpredictor

Minimal example

import { TurboTouchPredictor } from 'TurboTouchPredictor'

let ttpPredictor = new TurboTouchPredictor();

// Amount of prediction in ms. Allowed values: 0, 16, 32, 48, 64
ttpPredictor.setAmountOfCompensation(32);

let predictedPoint = ttpPredictor.predict({x: 0, y: 0, t: 0, state: "Interacting"});

Doc

constructor

• new TurboTouchPredictor()

predict

▸ predict(e): any

Predicts a point from the current lagging one

Parameters

Returns

any

• predicted point p, p.x: x corrdinate, p.y: t corrdinate, p.t: timestamp in nanoseconds

reset

▸ reset(): void

setAmountOfCompensation

▸ setAmountOfCompensation(comp): void

Sets the parameters of the predictor for the given amount of compensation

Parameters

Related publication

@inproceedings{10.1145/3242587.3242646,
    author = {Nancel, Mathieu and Aranovskiy, Stanislav and Ushirobira, Rosane and Efimov, Denis and Poulmane, Sebastien and Roussel, Nicolas and Casiez, G\'{e}ry},
    title = {Next-Point Prediction for Direct Touch Using Finite-Time Derivative Estimation},
    year = {2018},
    isbn = {9781450359481},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3242587.3242646},
    doi = {10.1145/3242587.3242646},
    abstract = {End-to-end latency in interactive systems is detrimental to performance and usability, and comes from a combination of hardware and software delays. While these delays are steadily addressed by hardware and software improvements, it is at a decelerating pace. In parallel, short-term input prediction has shown promising results in recent years, in both research and industry, as an addition to these efforts. We describe a new prediction algorithm for direct touch devices based on (i) a state-of-the-art finite-time derivative estimator, (ii) a smoothing mechanism based on input speed, and (iii) a post-filtering of the prediction in two steps. Using both a pre-existing dataset of touch input as benchmark, and subjective data from a new user study, we show that this new predictor outperforms the predictors currently available in the literature and industry, based on metrics that model user-defined negative side-effects caused by input prediction. In particular, we show that our predictor can predict up to 2 or 3 times further than existing techniques with minimal negative side-effects.},
    booktitle = {Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology},
    pages = {793–807},
    numpages = {15},
    keywords = {touch input, latency, lag, prediction technique},
    location = {Berlin, Germany},
    series = {UIST '18}
}