Review of Motion Reconstruction Using Sparse Accelerometer Data

1. Review of Motion Reconstruction Using Sparse Accelerometer Data JOCHEN TAUTGES Universit ¨ at Bonn and ARNO ZINKE GfaRmbH, Bonn and BJO¨ RN KRU¨GER and JAN BAUMANN and ANDREAS WEBER Universit ¨ at Bonn and THOMAS HELTEN and MEINARD MU¨ LLER and HANS-PETER SEIDEL Universit ¨ at des Saarlandes and MPI Informatik and BERND EBERHARDT HdM Stuttgart The

2. Overview The authors explain that the development of methods and tools for the generation of motion capture data has become an important area of research in computer animation. Their approach is a novel idea to incorporate four 3D accelerometers (inertial sensors) that attach to the human actor to achieve this. This paper was particularly interesting to me because of my previous work with these types of sensors when I worked at NASA.

3. Overview The authors claim that their procedure scales to even large data sets of millions of frames. They compare their work to some of the current methods used to capture motion, such as reflectors that are attached to the actor and act as reference points for the computer to use in it’s algorithms. They also talk about the limitations of current mocap methods, such as the need to be indoors, have a limited volume of space to work in, etc…

4. Overview of the Animation System As you can see, the MoCap database is used in the preprocessing stage as well as the synthesis stage.

5. Overview of the Animation System This picture shows where the inertial sensors are attached to the actor and in what orientation they are placed.

6. Sparse Data One interesting thing to note is how the database is used to ‘fill-in’ the gaps in the motion as it’s being generated. Because the data is sparse, there are a lot of assumptions made about any particular movement (running, walking, jumping, etc…)

7. Lazy Neighborhood Graph Okay, I have to admit they lost me on this picture, but it looks really cool. It has something to do with the Online Lazy Neighborhood Graph, which is shown in the first picture. I believe it shows frames of motion and possible movements from frame to frame based on some algorithm. Apparently, this approach can compensate for temporal variations in motion (speed differences vs. time).

8. Lots of Math Alright, this paper had a LOT of math calculations in it and although I understood the syntax of the equations, that’s about as far as I could go with that. I just couldn’t follow what they were talking about with the equations.

9. Motion Examples In the image below, they were showing examples of the types of motion they were able to capture with their sensors.

10. Sensor Setups This graph shows the different sensor setups and the average reconstruction error of the data. This has to do with how accurate the algorithms ‘fill-in-the-blanks’.

11. Conclusion They were able to show that although data from inertial sensors contains less information than positional data derived from reflectors, motion reconstruction is possible in many cases using only a few accelerometers.