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filtering arm movement data, and finding proper cutoff freq.

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  • filtering arm movement data, and finding proper cutoff freq.

    My name is Nathan, and I am a student doing researching arm
    movements for mechanical assistors. I realize there have been many
    previous discussions on properly differentiating and filtering data in
    leg movements, but unfortunately I wasn't a subscriber to Biomch-L at
    the time. I read several of the posts including those that refer to
    David Winter, Graham Wood, as well as Pezzack, but after reading some of
    these publications I find myself still somewhat confused. There seems to
    be an inherent ambiguity in choosing a filter- meaning different filters
    work better depending on the data and what you are looking for in this
    data. This I understand. It seems however that splines and doubled or
    reversing Butterworths are currently the most mainstream and widely
    accepted forms of filters, with of course the added extrapolation
    techniques (prediction and least squares for example) proposed by Giakas
    and Baltzopoulos. Please let me know if I have misinterpreted anything
    thus far.
    With this being said, my questions then are these. If indeed
    these are the most accepted forms of filtering, how does one go about
    finding the best cut-off frequencies without having the actual
    acceleration data to compare a RMSE to? Again if there are newer or
    better filters please inform me of these as well. Also, in the paper
    "Optimal Digital Filtering Requires a Different Cut-off Frequency
    Strategy for the Determination of Higher Derivatives" by Giakas and
    Baltzopoulos it refers to a procedure in which "displacement was
    filtered with a different cut-off frequency depending upon optimal 0th,
    1st, and 2nd derivatives." This procedure yielded stronger results than
    others but again what are these cut-off frequencies and how can they be
    determined without comparison data?
    I don't mean to beat a dead horse by asking answered
    questions, but any information would help. Please let me know if I'm on
    the right track and additional information (i.e. papers, articles, etc.)
    is always appreciated. Thank you all for your time.

    Nathan Manning
    University of Washington

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