Cutoff frequency for doing inverse dynamic

[Jeroen Smeets]

Re: Cutoff frequency for doing inverse dynamic

This paper might be interesting:
Schreven S, Beek PJ, Smeets JBJ (2015) Optimising filtering parameters for a 3d motion analysis system. Journal of Electromyography and Kinesiology 25: 808-814

[Allan Carman]

Re: Cutoff frequency for doing inverse dynamic

This may be getting more off topic than the original post, but here is a little more than my two cents worth on methods and filtering :-)

“Don't expect your results to match others who analyze their data differently”

We should expect our results to resemble others who are studying a similar population and activity, especially joint angle data in normal gait. It is not good enough to except that inter-session and inter-examiner results will be unrelated, even on the same participant, for something as reproducible as gait. The 3DMA literature does show no similarity in what has been presented as normal non-sagital knee joint rotations during gait, this includes the poor results of 3D gait reliability studies. However, this reflects poor methods and understanding of 3DMA, rather than the variability of gait or what we should be expecting from 3DMA. If the quality of data (filtering and monitoring RMS errors in least squares are part of this) and axes misalignment were addressed in a more thorough and informed manner then you will find that in gait the non-sagital rotations are more reliable that sagital plane rotations and knee abd/add is one of the most reproducible lower limb joint rotations across individuals. So much so that knee abd/add can be used as a guide to the alignment of lower limb axes. Making the methods largely independent of marker placement and eliminating time consuming and ineffective practices such as knee alignment devices, wands and defining functional axes through a series of hip-leg waving movements.

“The difference in handling the data obviously underlies the differences in presented/published discrete values and time-data curves of joint torques and powers.”
“I think the studies to date on this topic suggest is that we need to work on improving the realisticness (?) of the models we use for inverse dynamics and the accuracy of the kinematic data”

It is more than just differences in handling of data, but as mentioned the reliability of the underling methods (models) we use and ability to get repeatable results. No matter what your outcomes measures you need valid and reliable data describing the position and orientation of segment axes. From which joint rotation curves and discrete variables are derived or moment and powers are expressed relative to. Even if the activity does not involve gait or even joint kinematics I would suggest you should collect normal gait as part of the model validation procedure. In gait knee abd/add range of motion is small, patterns are highly reproducible, and are very sensitive to axis misalignment. This can be used to verify axes alignment prior to analysis of other activities (jumps, side-steps etc.) or calculation of joint moments and powers. If your derived joint angle data is erroneous, reflecting large random errors is axes alignment and significant cross-talk, then it is not OK to carry on and expect that your joint moment data will be fine.

“I'd argue that the answer to how to process the data is up to you…”

Yes and No. Sound procedures need to be followed, filtering is only part of it. There are some key steps that I feel need to be done regardless of how you filter the data. So I would say understand it, design it, test it and when you are happy with the reliability stick to it. It is worth noting that the method and the implementation (how the method is interpreted and applied by the examiner) are two different aspects to reliability. No current method appearing in the 3DMA literature is reliable, however some implementations show reliability for some joint angles in gait but this is the exception in published reliability studies.

Bernard asked is there is a reference or precedence for filtering outcome measures post analysis. As you may have guested I don’t follow the norm or expected and don’t know of any references I could add to support not filtering raw coordinate prior to the least squares reconstruction of axes. However, I did present the rationale, methods and results of a reliability study of my approach to 3DMA in a seminar (see YouTube, search under SPRINZ seminars). As mentioned by several of the contributors to the discussion so far, a far more critical look is needed into 3DMA methods and validity and reliability of outcome measures than has been done in the past.