My clients are medical researchers researching methods for characterizing patients' gait from raw accelerometry by matching the data stream against a set of "templates." for the various characteristics.
Their (labyrinthine) pseudocode appears to slide a snippet ("template;" kernel?) across a data stream looking for goodness-of-fit by correlation coefficient. This is for each of several templates, so performance is at a premium.
As I read the name and the 13-word description, vDSP.correlate(_:withKernel:) does this — in some way. However, the set of numbers that emerge from my playground experiments don't make sense: Identical segments are scored 0.0 (should be 1.0, right?). Merely similar matches show values barely distinguishable from the rest, and are often well outside the range -1.0 ... 1.0.
Clearly I'm doing it wrong. Web searches don't tell me anything, but I'm naïve on the subject.
Their (labyrinthine) pseudocode appears to slide a snippet ("template;" kernel?) across a data stream looking for goodness-of-fit by correlation coefficient. This is for each of several templates, so performance is at a premium.
As I read the name and the 13-word description, vDSP.correlate(_:withKernel:) does this — in some way. However, the set of numbers that emerge from my playground experiments don't make sense: Identical segments are scored 0.0 (should be 1.0, right?). Merely similar matches show values barely distinguishable from the rest, and are often well outside the range -1.0 ... 1.0.
Clearly I'm doing it wrong. Web searches don't tell me anything, but I'm naïve on the subject.
Am I mistaken in hoping this vDSP function does what I want? "Yes, you're mistaken" is an acceptable answer. Bonus if you can point me to a correct solution.
If I'm on the right track, how can I generate inputs so I can interpret the output as fits my needs?
