# Pyramis

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bio website steinbock.org location Stanford, CA age 34 member for 2 years seen Feb 23 at 6:02 profile views 6

Ph.D. researcher in Anthropology and Education at Stanford University. Techie background with a B.S. in Computer Science and M.S. in Design Engineering.

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 Aug13 comment Measuring 'synchrony' with time series correlations ...For the dissertation, I generated a time series of the number of people moving at any given moment (above a minimum threshold). I then made simple probability calculations: e.g. if no one is currently moving, what is the likelihood that if someone starts moving that others will move also? In general I found that about 90% of the time, others will move too. For publication, I'd like to do more sophisticated stats and these references are really helpful. Aug13 comment Measuring 'synchrony' with time series correlations Great article. Thank you. Like the authors, I used Motion Energy Analysis (frame differencing). The multi-person cross-correlation was also a problem point. For the dissertation, I ended up just visualizing the number of people moving Feb10 comment Measuring 'synchrony' with time series correlations This is immensely helpful. Thank you. Dec8 comment Measuring 'synchrony' with time series correlations I think I am using the term 'synchrony' in a different sense than the Kuramoto model assumes. I don't think human bodies aren't adequately modeled as periodic oscillators. A closer model might be Per Bak's sandpile or other dynamic cascade. But in any case, I'm not trying to create a model that corresponds to my data. I'm adding an analytic layer to reveal patterns in it. Dec8 comment Measuring 'synchrony' with time series correlations @MattAlbrecht Great question. The generated time series should give low values when nobody is moving much. (But you make me realize that the rolling correlation would also be useful!) For your multiplication suggestion, do I normalize the data to [0.0, 1.0] or stay in the range of positive integers (number of pixels)? Dec8 comment Measuring 'synchrony' with time series correlations @jbowman Averaging is a decent first pass. But it gives too large a value when only one person is moving, especially when the group is small. I want to discount occasions when only one person is moving but highlight occasions when at least two people are moving. Dec7 awarded Student Dec7 awarded Autobiographer Dec7 comment Measuring 'synchrony' with time series correlations ps: I'll give credit for the best answers in my ph.d. dissertation. Dec7 asked Measuring 'synchrony' with time series correlations