Tag Archives: Classical mechanics

Project KTracker kicks off

Watch a demo video
We have started to develop a high quality three-dimensional motion tracking system for science education based on the Microsoft Kinect controller, which was released about 18 months ago. This development is part of the Mixed-Reality Labs project funded by the National Science Foundation.

KTracker will provide a versatile interface between the Kinect and many physics experiments commonly conducted in the classroom. It will also provide natural user interfaces for students to control the software for data collection, analysis, and task management. For example, the data collector will automatically pause while the Kinect detects that the experimenter is adjusting the apparatus to create a new experimental condition (during which the data collection should be suspected). Or the user can "wave" to the Kinect to instruct the software to invoke a procedure. In this way, the user will not need to switch hands between the apparatus and the keyboard or mouse of the computer (this "hand-switching" scene seems familiar to the experimentalists reading this post, huh?). The Kinect sensor has the capacity to recognize both gestures of the experimenter and motions of the subject, making it an ideal device for carrying out performance assessment based on motor skill analysis.

KTracker is not a post-processing tool. It is not based on video analysis. Thanks to the high performance infrared-based depth camera built in the Kinect, KTracker is capable of doing motion tracking and kinematic analysis in real time. This is very important as it helps to accelerate the data analysis process and contributes to enhancing the interactivity of laboratory experiments.

KTracker will also integrate a popular physics engine, Box2D, to support simulation fitting. For example, the user can design a computer model of the pendulum shown in the above video and adjust the parameters so that its motion will fit what the camera is showing--all in real time. Like the graph demonstrated in the above video, the entire Box2D will be placed in a translucent pane on top of the camera view, making it easy for the user to align the simulation view and the experiment view.

KTracker will soon be available for download on our websites. We will keep you posted.

Kinect-based motion tracking and analysis

Click here to watch a video.
Microsoft's Kinect controller offers the first affordable 3D camera that can be used to detect complex three-dimensional motions such as body language, gestures, and so on. It provides a compelling solution to motion tracking, which--up to this point--is often based on analyzing the conventional RGB data from one or more video cameras.

The conventional wisdom of motion tracking based on RGB data requires complicated algorithms to process a large amount of video data, making it harder to implement a real-time application. The Kinect adds a depth camera that detects the distances between the subjects and the sensor based on the difference of the infrared beams it emits and the reflection it receives. This gives us a way to dynamically construct a 3D model of what is in front of the Kinect with a rate of about 10-30 frames per second, fast enough to build interactive applications (see the video linked under the above image). For as low as $100, we now have a revolutionary tool for tracking 3D motions of almost anything.

The demo video in this post shows an example of using the Kinect sensor to track and analyze the motion of a pendulum. The left part of the above image shows the overlay of trajectory and velocity vector to the RGB image of the pendulum, whereas the right part shows the slice of the depth data that is relevant to analyzing the pendulum.

The National Science Foundation provides funding to this work.

Simulation fitting of experimental results: A damped pendulum

The National Science Foundation recently awarded us a new grant to explore the concept of mixed-reality lab (MRL), which we proposed to combine the power of simulations and sensors to provide a new level of integrated learning experience that connects invisible science concepts with natural phenomena.

One of the proposed ways to integrate sensors and simulations is a strategy called "simulation fitting." When scientists observe something in the natural world, they typically build mathematical models to explain their observations. It is through this process that scientists make sense of their findings, understand the mechanisms more deeply, and derive new ideas for further investigations.

This is also an essential cycle of scientific inquiry we would like students to learn. The MRL project will explore how this "simulation fitting" strategy can improve science learning.

An example we have looked at is a simple pendulum. A swinging pendulum will eventually stop because of damping, which comes from two different sources: air resistance and bearing friction. Air resistance depends on the velocity of the pendulum whereas bearing friction doesn't.

My colleague Ed Hazzard has done a neat experiment that shows the difference of the two damping effects. His pendulum, under the normal circumstance, loses very little energy and can swing for a long time. To slow it down quickly, he attached a piece of paper to create a "sail," thus dramatically increasing the air drag. The result from a rotatory sensor shows the decaying of the rotational angle of the pendulum. In this case, the envelope of the curve looks like an exponential function (see the first image).

Launch the simulation.
Removing the "sail" and inserting some cotton into the bearing to increase the dry friction, he was able to amplify the effect of the dry friction. This time, the result of the rotational angle shows an envelope of a straight line, instead of an exponential curve (see the second image--it had two runs).

To understand these effects, we have created simulations that fit exactly the behaviors (see the third image). These simple simulations are based on solving Newton's equation of motion, with the only difference in the damping force: In one case it is proportional to the velocity; in the other case it is constant.

Our next step is to explore how to translate what we have done into a learning activity.