ENSEMBLE AVERAGE VS REPRESENTATIVE CYCLE: HOW SHOULD GAIT KINEMATICS BE PRESENTED? Richard E. Pimentel1 and James J. Carollo1 1 Center for Gait and Movement Analysis, Children's Hospital Colorado, Aurora, CO, USA E-mail:
[email protected] INTRODUCTION Kinematic data from an instrumented gait analysis (IGA) is typically portrayed in kinematic plots to envision the joint angles and general walking ability of the subject. Given the large amount and complexity of IGA data, these curves typically contain information from either a single representative cycle (RC) or an ensemble average (EA) of the cycles. During an IGA, multiple walking trials are collected and an EA of all the valid gait cycles is computed. Using the EA, a single RC that closely resembles the EA can also be selected for use in clinical interpretation and decision making. The benefits of using an EA are: 1) it contains summed information of all the gait cycles present during various trials; 2) the gait variability measurements provide additional information about the subject's gait [1]; and 3) the averaged waveform is more reliable than a single cycle [1]. The benefits of using the RC are: 1) it is an intact cycle, coming from one definitive gait cycle; 2) gait features (minima & maxima) are unchanged, these features may be lost during EA; 3) the waveform is not affected by any of the other gait cycles. These two techniques are mutually exclusive; the benefits of one are the drawbacks of the other. While it has been said that the EA forms the basis of clinical assessments [1], a carefullyselected RC may also serve that purpose. The purpose was to compare the RC and EA, across the kinematic root mean square (RMS) error between waveforms and how those differences may affect overall quality of gait measured by gait deviation index (GDI) [2]. These will be assessed between a group with cerebral palsy (CP) and an age-matched normal group (AMN). CLINICAL SIGNIFICANCE Representative gait cycles or ensemble averages of gait cycles can be used to portray a person's kinematic curves. It is important to understand the pros and cons of each data type and how each technique may affect data before making clinical decisions from that technique. METHODS Thirty-two participants were included in this analysis, 16 with CP and 16 AMNs. Mean characteristics for the group include: age 13.8 (4.7) years; height 154.9 (16.3) cm; and weight 46.9 (17.7) kg (Mean (SD)). Participants started from standstill and walked through the capture volume at a self-selected pace. The first stride on each side was excluded due to initiation of locomotion. An average of 14 valid strides was collected over at least 4 walking trials for each participant. Kinematics were collected using the Newington-Helen Hayes lower body marker-set and captured in Vicon Nexus (v 2.2.3, Oxford, UK). The Plug-in-Gait Model [3] was applied after the trajectory gap filling and filtering [4]. Using Matlab (v R2015A, Mathworks, Natick, MA, USA), kinematics were parsed from gait events and interpolated to 2% increments of the gait cycle. Lower body kinematics only included those used by GDI [2].
SUMMARY The purpose was to determine if there are differences between EA and RC data representation techniques. EA and RC are similar in accuracy to approximately 2° on average (0.5 SD). Overall gait quality is preserved in both techniques (GDI difference of approximately 1 score (2.5 SD)). In summary, kinematic gait data can be accurately depicted in both manners.
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AMN CP
RMS Error
GDI L
GDI R
Figure 1: EA and RC are similar between groups (P ≥ 0.236). RMS units are degrees, while GDI is unit-less (score based).
5 RMS Error (°)
DEMONSTRATION There were no significant differences between the CP and AMN groups in age, height or weight (P ≥ .468). Average RMS error between EA and RC was similar between groups (Figure 1). At the whole group level, RMS error was also similar across body segments (Figure 2) and planes (sagittal, frontal, and transverse; P = .155). GDI scores between EA and RC were similar between groups for both sides (Figure 1).
EA & RC Difference
In order for the RC to contain an actual intact gait cycle, RCs were selected primarily on the side with lower GDI and then the contralateral side was chosen from the cycle directly before or after the ipsilateral cycle closest to the EA. Each subject had only one RC and EA across the multiple trials collected. RMS error was the absolute error between waveforms and GDI difference was calculated as EA – RC. Analysis of variance was used to test for significance of the EA-RC difference between the two groups. Statistical significance was set at α = 0.05.
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REFERENCES Pelvis Hip Knee Ankle 1. Winter DA. 2009. Biomechanics and Motor Control of Human Movement, Fourth Edi. John Figure 2: RMS error between segments across Wiley & Sons Inc. the whole group. None of the body segments were different (P = 0.161). 2. Schwartz MH et al. 2008. Gait Posture. 28:351– 357. 3. Vicon. 2010. Plug-in Gait Product Guide — Foundation Notes. 4. Woltring HJ. 1986. Adv Eng Softw 8:104–113. ACKNOWLEDGMENTS Funding for this project was received from NIH/NINDS SBIR Grant # 1R43NS090756-01. Thanks to Colton Sauer for protocol development and coordination for the majority of this project. Additional thanks to CGMA staff for assistance with scheduling and data collection. DISCLOSURE STATEMENT The authors have no conflicts of interest to disclose.
