Three*7

WRITTEN COMPONENT ENTRY Practical Mocap in Everyday Surroundings [intial notes].

-       commercial mocap systems produce excellent in-studio reconstructions, but offer no comparable solution for acquisition in everyday environments. We present a system for acquiring motion almost anywhere.

-       Experimental results show that even motions that are traditionally difficult to acquire are recorded with ease within their natural settings.

-       Motion data has revolutionised computer animation in the last decade.

-       An entire industry has emerged in support of these activities, and numerous recordings of human performances are available in large motion repositories (e.g. mocap.cs.cmu.edu and www.moves.com)

-       The majority of current acquisition systems inhibit broader use of motion analysis by requiring data collection within restrictive lab-like environments.

-       Recording the activities, routines and motions of a human for an entire day is still challenging.

-       We explore (in this paper) the design of a wearable self-contained system that is capable of recording and reconstructing everyday activities such as walking, biking and exercising.

-       ‘our’ system is not the first acoustic-inertial tracker, but it is the first such system capable of reconstructing configurations for the entire body.

-       The best reconstructions are not perfect, but their quality (with small size and improved versatility) suggest that our system may lead to new applications in augmented reality, human-computer interaction and other fields.

-       Several mocap technologies have been proposed in the last 2 decades.

-       Optical mocap systems track rerto-reflective markers or light emitting diodes placed on the body. Exact 3D marker locations are computed from the images recorded by the surrounding cameras using triangulation methods. Optical mocap is favoured in the computer-animation community and the film industry because of their exceptional accuracy and extremely fast update rates. Disadvantages of this approach are extreme cost and lack of portability.

-       Image-based systems use computer vision techniques to obtain motion parameters directly from video footage without the use of special markers. These approaches are less accurate) than optical systems but they are more affordable and portable. They also suffer from line-of-sight problems.

-       Mechanical systems require performers to wear exoskeletons. These systems measure joints angles directly (rather than estimating the positions of points on the body) and can record motions almost anywhere. Exoskeletons are uncomfortable to wear for extended periods and impede motion, although the problems are alleviated in some modern systems.

-       Magnetic systems detect the positions and orientation using a magnetic field. These systems offer good accuracy and medium update rates with no line-of-sight issues. They are expensive, have high power consumption and are sensitive to the presence of metallic objects in the environment.

-       Inertial mocap systems measure rotation of the joint angles using gyroscopes or accelerometers placed on each body limn. Like the mechanical systems, they are portable, but cannot measure positions and distances directly for applications that must sample the geometry of the environment. The measurements drift by significant amounts over extended time periods. Also, the motion of the root cannot be reliably recovered from inertial sensors alone, though (in some cases) the issue may be alleviated by detecting foot plans.

-       Acoustic systems use the time-of-flight of an audio signal to compute the marker locations. Most current systems are not portable and handle only a small number of markers with the ‘Bat’ system. An ultrasonic pulse emitter is worn by a user, while multiple receivers are placed at fixed locations in the environment. A system by Hazas and Ward extends ultrasonic capabilities by using broadband signals, Vallidas alleviates occlusion problems with a spread-spectrum approach; Olson and colleagues are able to track receives without known emitter locations.

-       Hybrid systems combine multiple sensor types to alleviate their individual short-comings. They aim to improve performance, rather than decrease cost and increase portability.

-       Our system is capable of acquiring motions ranging from biking and driving, to skiing, table tennis and weight lifting.

-       The results are processed at a rate of 10fps and visualised without any post-processing using an automatically skinned mesh.

-       We evaluated the accuracy of our system by comparing it with Vicon’s optical motion capture system, known for its exceptional precision.

-       Optical mocap is able to recover the root transformation without drift.

-       Our sensing capabilities have lead us to explore multiple facets of our pose recovery system.

-       Due to inherent physical limitations of our hardware components, we were unable to acquire high impact motions.

-       Other types of motions that we are unable to acquire with the current prototype include interaction between multiple subjects, such as dancing.

-       Our distance measurements depend on the speed of sound, which is affected by temperature and, to a lesser extent, humidity. To obtain a more precise speed of sound, one could use a digital thermometer or a calibration device prior to each capture session.

-       Most significant limitation of our system is the lack of direct measurements of the root transformation.

-       We have presented a wearable mocap system prototype that is entirely self-contained and capable of operating for extended periods of time in a large variety of environments.

-       We should enrich motion repositories with varied data sets to further understand human motion. Restrictive recording requirements limit the scope of current motion data sets, which precents the broader application of motion processing. An inexpensive and versatile mocap system would enable the collection of extremely large sets of data. This enhanced infrastructure could then support large-scale analysis of human motion, including its style, efficiency and adaptability.

D. Vlasic, R. Adelsberge, G. Vannucci, J. Barnwell, M. Gross, W. Matusik, J. Popovic. “Practical Motion Capture in Everyday Surroundings”. Computer Science and Artifical Intelligence Laboratory, Massachesetts Institute of Technology, Mitsubishi Electric Research Laboratories, ETH Zurich.