Re: A resource for axes misalignment, nonlinear error and normal gait data
This is to answer questions about the ideal gait curve used and to help understand the approach presented in the excel spread sheet. Which as pointed out can be difficult to follow from someone else's work.
The ideal gait curves were derived from an in-house reliability study of 11 healthy young adults tested on three separate days over a one week period. I used a 7 segment lower body, 6DF, least squares, marker cluster design, using virtual joint centers as part of the cluster for each segment. The same model was used for my 30 participant normal gait example in the spread sheet, with the same assumptions:
- The static offset in axes alignment (during subject calibration procedure) is constant within a session (same marker placement) but variable between sessions.
- The dynamic offset in axes alignment (skin movement during movement trial) is variable within a movement trial but is repeatable between sessions for a given subject, marker placement and movement pattern.
In the original reliability study the process of introducing a static correction to refine axes alignment within the subject calibration procedure was very much experimental. Where offsets were adjusted, gaining an understanding how joint angle data were affected, while looking for common dynamic offsets (skin movement artifact patterns) within subjects, as well as similar skin movement artifact patterns across subjects and similar gait joint angle patterns across all subjects. There is the influence of my own perceptions and judgment on the differences ROM and variability of knee abd/add between stance and swing phases.
This ideal joint angle data is therefore my best guess given a surface marker cluster design and an understanding of static (defining segment axes) and dynamic (skin movement) errors and how they affect joint angle data.
The approach to correcting the three examples and the data from the literature was a two part process, also using the two assumptions mentioned. Initially the static offsets were introduced. There was no set procedure to defining the static offsets, it again was a matter of adjusting offsets to minimise difference to the ideal curves, while looking for similarities in the dynamic offsets. In this first step dynamic offsets were automatically produced for the given static offset so as to minimise the difference to the ideal curves. Then common (mean) dynamic offsets were calculated for different trials of the same participant or across different studies with similar design. In the second part of the approach, the whole procedure was repeated with the same static offsets used in the first part but with the common dynamic offsets representing common patterns of skin movement artifact.
In the initial reliability study and the 30 participant example the approach is different and represents how identifying and correcting static (subject calibration) and dynamic (skin movement) offsets may be implemented in 3DMA. Deriving the static segment axes offsets were completed in the subject calibration procedure. In an iterative process the traditional marker based static subject calibration was performed as an initial estimate of axes alignment, a sample gait trial was analysed, and refinements to the static axes alignment made (offsets introduced) and calibration and analysis processes repeated as the offsets were refined to minimise differences between knee non-sagittal rotations and the ideal. The spread sheet shows the second part of the approach in identifying the dynamic offset component of axes misalignment. This was achieved by calculating the common differences between the hip and knee non-sagittal rotations relative to the respective ideal gait curves. This common differences to the ideal for abd/add and ext/int rotations for the hip and knee was used to correct thigh dynamic alignment during the gait cycle (skin movement) given the assumption that the shank has minimal skin movement. When correcting static and dynamic offsets for the 30 participants, each subject was analysed individually.
You may have noticed that when identifying and correcting dynamic axes alignment for the 30 participants I used one of two alternative hip abd/add rotation patterns and one of three alternative Hip ext/int patterns instead of the respective single ideal curve for each participant. This was purely speculative given that there appeared to be several alternative normal gait patterns for these two joint rotations. It will take a new study to see if this holds up or was something peculiar to this group.
My marker based ideal produced greater knee abd/add during stance and a leading shift in joint angles in the swing phase when compared to the bone pin study of Lafortune et al. (1992). If there were a second bone pin or radiographic study to support this result then I would replace my marker based ideal used in the spread sheets with this ideal. In so doing changing the marker based dynamic skin movement artefact profile during gait.
This will be difficult to fully understand from the examples and the best way is to collect data from several individuals on repeated days, apply the approach and see what you find. What I have presented is not perfect and is still evolving. I would recommend that you do not try this initially with global optimization methods as this will introduce a new random source of axes misalignment that will be difficult or impossible to correct.
Last edited by Allan Carman; 02-10-2017 at 05:56 AM.
Reason: Hip abd.add and Hip ext/int labeled incorrectly