Biomedical Engineering Reference
In-Depth Information
instructs players to hold the Wiimote as if they're holding pom-poms and Wii Sports
Boxing makes players raise their hands when preparing for a fight. Hardware and
recognition limitations will persist as technology improves, so make users believe
there is a valid reason in the story for this or guide them so that they never notice.
16.5 Conclusion
It may seem unlikely at first that a low cost video game controller could ever solve a
complex locomotion problem, however if you use the ideas and techniques discussed
above, success with these devices are easily plausible. In this chapter we have shown
you the details to three different popular game controllers, the Nintendo Wiimote,
Sony Move and Microsoft Kinect. You have seen how these devices work, some
considerations made during their design and what data the SDKs might provide
from them. You've also seen how to take that data, understand what you are dealing
with and use that to select the best algorithm for your problem. Tips were provided
on modifying these algorithms to reduce uncertainty and taking that final step toward
a real world solution. In our last section of the chapter, advice and considerations
were given toward developing an interface appropriate to the user and task. After
all, just because you think a gesture is natural does not mean the average person will
think so; or if they do, they might not like it anyway. Run tests to see what the users
themselves prefer before you build up training data that may end up being useless.
While using low cost hardware to recognize such interactions may be difficult, it can
be done, with the added benefit of using hardware users are familiar with and that
exists in their homes.
References
1. Azuma R, Bishop G (1994) Improving static and dynamic registration in an optical see-through
HMD. In: SIGGRAPH '94: Proceedings of the 21st annual conference on computer graphics
and interactive techniques. ACM, New York, pp 197-204
2. Bowman D, Kruijff E, LaViola J, Poupyrev I (2005) 3D user interfaces: theory and practice.
Addison-Wesley, Boston
3. Buxton B (2007) Sketching user experiences: getting the design right and the right design.
Morgan Kaufmann, San Francisco
4. Cohn G, Morris D, Patel S, Tan D (2012) Humantenna: using the body as an antenna for real-
time whole-body interaction. In: Proceedings of the 2012 ACM annual conference on human
factors in computing systems (CHI '12), pp 1901-1910
5. Crassidis JL, Markley FL (2003) Unscented filtering for spacecraft attitude estimation. J Guid
Control Dyn 26(4):536-542
6. Hoffman M, Varcholik P, LaViola J (2010) Breaking the status quo: improving 3D gesture
recognition with spatially convenient input devices. IEEE VR 2010
7. Julier S, Uhlmann J (1997) A new extension of the Kalman filter to nonlinear systems. In:
International symposium: aerospace/defense sensing, simulation and controls.
