How to capture conducting motion?

In recent months I’ve been looking at how conducting motion has been captured in the past, and the applications for which it has been used. Conducting motion has been explored in a number of different ways previously. While we are planning to use infra-red retro-reflective marker-based motion capture, it is useful to review work by others in the field, both for performance studies and for computational analysis and control.

In general, four main types of transducer have been used: optical, inertial, electromagnetic and bioelectrical, each of which has its own advantages and disadvantages. In general, bioelectrical can be discrete, but is an indirect measure of motion, subject to noise and other issues; electromagnetic detection can cover a large capture volume and has no line-of sight problems, but suffers from electromagnetic interference and historically relatively large transmitters; likewise inertial sensors can cover a large space with no line-of sight problems, but usually suffer from drift and is rather intrusive; optical systems range from simple video (with computational analysis) through to high-precision 3D retro-reflective or active marker IR tracking, all of which require clear lines-of-sight and can have other issues such as ease of portability and susceptibility to lighting issues.

Bioelectrical sensors have been used to read muscle activity from the bicep/tricep combination, notably Marrin’s Conductor’s Jacket used EMG sensors to provide arm movement data for control of other systems or for study.

Electromagnetic sensors can provide 3-axis position and orientation data from a small sensing object placed in a generated magnetic field. These were also used in one version of the Conductor’s Jacket, and also Ilmonen and Takala for gesture recognition. Max Matthews’ Radio Baton  electromagnetic controller has also been used in conducting experiments.

Inertial sensors have been used in a number of studies, including using wiimotes (e.g. Bradshaw and Ng), as well as other MEMS devices in projects such as Augmented Conductor, mConduct, and Conducting Master. Inertial devices measure acceleration and orientation (and magnetic) data which can be used to calculate the position of the of the sensor as it is moved, although drift remains an issue with these.

Optical approaches vary from simple video capture and manual analysis, to baton mounted LED’s with computational video analysis, the use of 3D gaming controllers (Microsoft Kinect) and high-end optical mocap systems from the biomechanics and entertainment industries. In CtCC (this project!) we will be taking this final approach due to the potentially very high level of detail and precision achievable, but we may supplement this with data from other devices for comparison with other studies and assessment of the quality of other systems.