Stomp identification challenges and opportunities
I have come quite a way with my research since the first post with preliminary WiiMote data. Following this, I began to attempt to identify, through raw WiiMote data, how a stomp vs. a shuffle vs. a kick might work. The results are mixed:
Flat stomp
This is a baseline stomp for reference. Please note there is no “wide-up” here; simply a wiimote connected to a shoe slammed on the ground. The idea here is to capture what the impact with the floor looks like.
Shuffle stomp
This is a simple shuffle-stomp wearing the WiiMote in the same way. You can see a similar “pop” here as in the first graph – the Y and Z-axises bouncing off of the charts suddenly.
This last graph is the running man performed in a full cycle. We can see that the data becomes rather skewed here because of the high degree of movement. Despite all of this, we can still identify those “pops” seen in earlier graphs.
What does this all mean? Basically, false positives will be a challenge here. Since stomping and stepping is so fundamental to the dance, incorrect beats may be identified prematurely. Ultimately, the prototype (under development now) needs to have a varied degree amplitude.
As an additional challenge, my Xcode development is hampered by the DarwiinRemote project using the 10.4 OSX SDK. This causes undue warnings in my 10.6 build, so I’m currently exploring (and looking for) the 10.4 SDK to leverage. Wish me luck!
