跟踪多点触摸的动作与发射器和接收器的框内 [英] Tracking multi-touch movements inside the frame with transmitters and receivers

问题描述

的问题与下一帧的设备上跟踪多触摸（至少两个手指触摸）。

The problem with tracking multi-touches (at least two finger touches) on the following frame device.

白色圆圈是LED和黑色圆圈是接收器。当用户这个框架内移动手指，我们可以分析它的接收器接收到的光从LED并没有收到。在此基础上，我们需要以某种方式跟踪手指的运动。

White circles are LEDs and black circles are receivers. When user moves fingers inside this frame we can analyze which receivers received light from the LEDs and which has not received. Based on that we need to track movements of the fingers somehow.

这是我们有独立的X和Y坐标的第一个问题。是什么把它们结合起来的有效途径？ 第二个问题的关注分析坐标，当两个手指接近对方。如何在两者之间不同的？

First problem that we has separate x and y coordinates. What is the effective way to combine them? Second problem concerns analyzing coordinates when two fingers are close to each other. How to distinct between them?

我发现，K-均值聚类凸轮这里是有用的。有什么其他的算法，我应该更仔细来处理这个任务？

I found that k-means clustering cam be useful here. What are other algorithms I should look more carefully to handle this task?

推荐答案

正如你指出你的图中，用两个手指不同的手指位置可以产生相同的传感器读数，所以你可能有一些不可约的不确定性，除非你能找到一些聪明的方法来使用previous历史什么的。

As you point out in your diagram, with two fingers different finger positions can give the same sensor readings, so you may have some irreducible uncertainty, unless you find some clever way to use previous history or something.

你真的需要知道每个手指的位置？这是正确的抽象这种情况呢？也许你可以得到一个合理的用户界面，如果你限制自己一个手指为precise指点，并认识到如由未使用手指的位置的中间再presentation一些手段手势命令。你可以找到手势可以彼此容易区分在给定原始传感器读数？

Do you actually need to know the position of each finger? Is this the right abstraction for this situation? Perhaps you could get a reasonable user interface if you limited yourself to one finger for precise pointing, and recognised e.g. gesture commands by some means that did not use an intermediate representation of finger positions. Can you find gestures that can be easily distinguished from each other given the raw sensor readings?

我想在传统的计算机科学的方法，这将是收集来自不同的手势传感器读数，把它们在某种机器学习中，并希望最好的。您也可以尝试借鉴如何随时间的不同手势和传感器读数变化看他们的图形是否有任何明显的突出。如果你想尝试一下机器学习算法， HTTP：//www.cs.waikato .ac.nz /毫升/ WEKA / 可能是一个良好的开端。

I suppose the stereotypical computer science approach to this would be to collect the sensor readings from different gestures, throw them at some sort of machine learning box, and hope for the best. You might also try drawing graphs of how the sensor readings change over time for the different gestures and looking at them to see if anything obvious stands out. If you do want to try out machine learning algorithms, http://www.cs.waikato.ac.nz/ml/weka/ might be a good start.

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