Shotgun approaches to gait analysis: insights & limitations
 Ronald G Kaptein^{1},
 Daphne Wezenberg^{1, 2},
 Trienke IJmker^{1, 2},
 Han Houdijk^{1, 2},
 Peter J Beek^{1, 4},
 Claudine JC Lamoth^{3} and
 Andreas Daffertshofer^{1}Email author
https://doi.org/10.1186/1743000311120
© Kaptein et al.; licensee BioMed Central Ltd. 2014
Received: 14 April 2014
Accepted: 7 August 2014
Published: 12 August 2014
Abstract
Background
Identifying features for gait classification is a formidable problem. The number of candidate measures is legion. This calls for proper, objective criteria when ranking their relevance.
Methods
Following a shotgun approach we determined a plenitude of kinematic and physiological gait measures and ranked their relevance using conventional analysis of variance (ANOVA) supplemented by logistic and partial least squares (PLS) regressions. We illustrated this approach using data from two studies involving stroke patients, amputees, and healthy controls.
Results
Only a handful of measures turned out significant in the ANOVAs. The logistic regressions, by contrast, revealed various measures that clearly discriminated between experimental groups and conditions. The PLS regression also identified several discriminating measures, but they did not always agree with those of the logistic regression.
Discussion & conclusion
Extracting a measure’s classification capacity cannot solely rely on its statistical validity but typically requires proper posthoc analysis. However, choosing the latter inevitably introduces some arbitrariness, which may affect outcome in general. We hence advocate the use of generic expert systems, possibly based on machinelearning.
Keywords
Background
The assessment of movement is developing rapidly as a result of recent advances in data acquisition. Multiple signals can be readily recorded for considerable time spans. Parallel progress in data analysis allows for a combined application of more conventional, multivariate statistics like principal or independent components and stabilityrelated measures, e.g., standard deviation of relative phase, Lyapunov exponents, and Floquet multipliers [1–5]. In the study of human gait, this led to many important findings regarding, e.g., the coordinative stability and adaptability of walking in relation to speed, curved walking, age and various pathologies including stroke, Parkinson’s disease, cerebral palsy, pregnancyrelated pelvic pain, amputations and low back pain [6–16]. Collectively, these studies have demonstrated the expediency of various ‘novel’ measures in characterizing gait dynamics and their surplus value relative to ‘traditional’ kinematic measures pertaining to more isolated features of walking.
Despite the progress in data acquisition and analysis, several limitations have come to the fore. For instance, there is culminating evidence that stride fluctuations in young, healthy humans are characterized by a power lawlike behavior (i.e., longterm correlations) that tends to vanish with age [17] and pathology (e.g., [18, 19]). Although important as a general observation, the underlying measure (scaling exponent) proved not sufficiently specific to differentiate between gaitrelated pathologies. Likewise, the stabilityrelated measures have been instrumental in revealing task and patientspecific changes in gait dynamics, but, as it stands, it is not evident how these measures relate to gait stability in a biomechanical sense, to proneness to falls, and to the various variability measures (e.g., standard deviation and coefficient of variation of stride duration). These limitations can be seen as derivatives from what we view as a potential problem of current gait analysis, namely that often measures and qualifiers are used that are selected a priori without thorough (theoretical) considerations. When this is the case, a more generic, unbiased method to determine a measure’s relevance may be preferred.
We discuss this by following what might be considered a more objective approach: browse through a large set of possible measures and assess their relevance for discriminating gait patterns by evaluating them according to their information content. This approach is typically applied in order to pinpoint measures that are most defining in characterizing different types of gait. Such a task is laborious and timeconsuming but can be left to the computer when employing proper statistical evaluation. Statistics has the potential to provide (more) objective criteria for feature selection. This is expected particularly useful in more exploratory studies where, as said, indepth theoretical underpinnings are lacking.
Our main goal is to gain insight into the feasibility of such a shotgun approach for movement assessment. That is, our interest here is not in the specifics of experimental designs and outcomes but in assembling a plenitude of measures and evaluating their capacity for classifying gait. We illustrate this by using data of two previous studies, which provided profound insight into altered gait patterns in amputees and in stroke patients. We ‘blindly’ created a large set of candidate outcome measures and tested them with different statistical approaches. To anticipate, defining the relevance of measures and, by this, reducing the set of measures seems feasible on first sight. Not unexpected, however, results differed between studies and between statistical approaches. Such discrepancies already indicate that also shotgun approaches are confronted by challenges due to the inevitably arbitrary choices regarding variable selection and subsequent statistical evaluation.
Methods
To demonstrate the shotgun approach we used data from two experimental studies, referred to as study A and B, that involved stroke patients and amputees walking on a treadmill, respectively. In brief, the aim of study A was to investigate the effect of balance support on gait parameters in patients with stroke. Study B addressed the effect of gait speed and of a cognitive dual task on gait of persons with an amputation compared to healthy controls. Primary outcomes of studies A and B have been partly published in [20] and [21], respectively. Study A involved only a single population under three conditions calling for initial statistical assessments in terms of a oneway ANOVA, whereas study B involved two populations implying an initial twoway ANOVA with condition as the within factor and  for study B  group as the between factor. As said, these data merely serve to show benefits and pitfalls of shotgun approaches. For this reason we only provide a very concise outline of the experimental design and data acquisition. More details can be found in [20] and [21].
Study A  stroke patients
Eighteen patients with stroke participated in study A. Participants walked on a treadmill for five minutes in three different conditions. In the first condition they were not allowed to hold the handrail for support and walked at their corresponding preferred speed. In the second condition participants used the rail for support and walked at their, typically altered, preferred speed. In the third condition, participants still held onto the rail but walking speed was set to the preferred speed of the unsupported condition [20].
Vertical ground reaction forces were obtained using an instrumented treadmill equipped with a force plate. From the ground reaction forces we determined the centerofpressure (COP) trajectories, C O P_{ x } and C O P_{ y } (mediolateral [ML] and anteriorposterior [AP] direction, respectively). Trunk accelerations a_{ x }, a_{ y }, and a_{ z } (ML, AP, and vertical direction, respectively) were measured using a triaxial accelerometer that was mounted near the level of the third lumbar spine segment. In addition, respiration was assessed breathbybreath using a pulmonary gas exchange system which measured ventilation rate (${\stackrel{\u0307}{V}}_{\text{E}}$), oxygen uptake (${\stackrel{\u0307}{V}}_{{\text{O}}_{2}}$), carbon dioxide production (${\stackrel{\u0307}{V}}_{{\text{CO}}_{2}}$), respiratory exchange ratio (RER), metabolic costs (C_{met}) and heart rate (HR). C_{met} was computed via the oxygen uptake $(4.960\xb7\mathit{\text{RER}}+16.040)\left(\right)close="/">{\stackrel{\u0307}{V}}_{{\text{O}}_{2}}$ normalized by bodyweight and walking speed [22]. The data of four participants had to be discarded because of technical problems leaving 14 sets for subsequent analysis.
Study B  amputees
Study B involved in total 46 participants: 26 with a lowerlimb amputation and 21 abledbodied controls. Of the former group, 16 had a transtibial and 10 a transfemoral amputation [21]. Participants walked on an instrumented treadmill at a comfortable walking speed for four minutes in two different conditions. The first one consisted solely of walking, whereas in the second condition participants performed while walking a cognitive task inducing the Stroop effect: Participants saw a color name printed in color while listening to a spoken color name which either matched or not matched the color of the printed word. Whenever spoken and depicted color disagreed, participants had to press a button. This task had previously been shown to elicit changes in postural control in lower limb amputees during quiet stance [23].
Similar to study A a treadmill with builtin force plate served to measure vertical ground reaction forces during walking. Offline computation yielded C O P_{ x } and C O P_{ y }. Participants also wore a triaxial accelerometer mounted near the level of the third lumbar spine segment. The corresponding a_{ x }, a_{ y }, and a_{ z }signals were sampled at a rate of 100 Hz. Oxygen uptake was assessed breathbybreath using opencircuit respirometry providing ${\stackrel{\u0307}{V}}_{\text{E}}$, ${\stackrel{\u0307}{V}}_{{\text{O}}_{2}}$, ${\stackrel{\u0307}{V}}_{{\text{CO}}_{2}}$, RER, C_{met}, BF, and HR. Furthermore, bilateral surface EMGs were recorded from the tibialis anterior (TA), gastrocnemius (G), vastus medialis (VM), semitendinosus (S), and the tensor fascia latae (TFL), from which the envelopes were estimated (after highpass filtering at 140 Hz, followed by fullwave rectification using the Hilbert transform, and lowpass filtering at 2 Hz) [24, 25]. Data of several participants had to be discarded because of acquisition problems leaving sets of 19 controls and 19 persons with amputation for analysis.
Data analysis

select an epoch around every step moment (we used a temporal window of 40% of the mean stride duration, which was estimated via the dominant frequency in the spectral distribution);

align the selected epochs and average over step moments – this averaging yields a template;

determine per event the time lag at which the seriallag crosscovariance between epoch and template is maximal and shift the event by that time lag;

repeat the entire procedure until convergence.
With the sodefined COPevents we estimated mean step width [28], mean stride length, mean stride duration, and their respective coefficients of variation (CV). For both force plate and EMG signals we further derived the mean value and variance of the template over time representing the average mean COP position over the aforementioned temporal window (for the EMG the average rectified value) as well as its mean deviation. Moreover, we determined the mean variance over events averaged over time as measure of the template’s consistency, i.e. the ‘regularity’ of walking and muscle activation. That is, for each data point of the template we calculated the variance using the corresponding points in all epochs, and all these variances were averaged. Regularity of signals wasalso assessed in terms of the corresponding sample entropy [29, 30] following the approach of Lake and coworkers [31] (parameter values: m=3 and r varying from 0.01 to 0.07). We computed the sample entropy of (Hilbert)amplitude and (Hilbert)phase of the force plate, EMG and accelerometer signal.
To address other complexityrelated features we analyzed the signals’ temporal correlation structure using detrended fluctuation analysis (DFA) [32, 33]. DFA yields a parameter α (scaling index or selfsimilarity parameter) that equals the socalled Hurst exponent [18] under the assumption that the generating process represents fractal Gaussian noise: A fully random signal (white noise) corresponds to α=0.5; signals containing antipersistent correlations have α<0.5, and persistent correlations imply α>0.5. Finally, we determined the maximum Lyapunov exponents as measure for (local, dynamic) gait stability [34–37]. We employed Rosenstein and coworkers’ algorithm with embedding dimension M=7 (estimated via the number of false nearest neighbors) and τ=20 samples embedding delay (estimated via the first minimum of the average mutual information) [38]. Maximum Lyapunov exponents were determined for the EMG and acceleration signals as well as for their principal components. The latter were determined through conventional principal component analysis (PCA) [39], from which we also stored the corresponding eigenvalue spectrum for subsequent statistical assessment [36] (i.e. three and five eigenvalues for accelerometer and EMG signals, respectively).
Measures
The following measures entered our statistical assessments – we sorted measures by recording modality; where numbers in between parentheses indicate for how many dimensions, channels, or muscles a measure was determined (e.g., x, y, and z for accelerometer data and C O P_{ x }, C O P_{ y }, and F_{ z } for force plate data).
Ground reaction force: mean and CV of step width, stride length, and stride duration; DFA α of stride duration, mean and CV of event position (3), DFA α of event position (3), template mean (3), mean variance over events (3), sample entropy (3), standard error of sample entropy (3), sample entropy of phase (3), standard error of sample entropy of phase (3).
Accelerometry: max. Lyapunov exponent (3), sample entropy (3), standard error of sample entropy (3), PCA eigenvalues (3), max. Lyapunov exponent of principal components (3).
Metabolism: mean and CV of ${\stackrel{\u0307}{V}}_{E}$, ${\stackrel{\u0307}{V}}_{{\text{O}}_{2}}$, ${\stackrel{\u0307}{V}}_{{\text{CO}}_{2}}$, RER, BF, C_{met}, and HR; no breathingfrequency data were available for stroke patients, i.e. there we used only twelve metabolic measures.
EMG: median frequency (5), template mean (5), mean variance over events (5), sample entropy (5), standard error of sample entropy (5), max. Lyapunov exponent (5), PCA eigenvalues (5), max. Lyapunov exponents of PCA components (5), sample entropy of phase (5), standard error of sample entropy of phase (5).
The total number of distinct (though possibly correlated) measures was 61 for study A and 113 for study B.
Hypothesis testing
As a first step, we tested main and interaction effects for all measures considered. We conducted separate repeated measures ANOVAs with condition as the within factor and–for study B–group as the between factor. To facilitate comparison of study A with study B we also performed supplementary analyses for study B by determining first the relative difference (i.e., the difference between the measures of the two conditions divided by their mean) that were entered into a oneway ANOVA as in study A. Because the aim of our shotgun approach was to “identify relevant measures in (changes of) gait patterns” we preferred individual ANOVAs over multivariate approaches (e.g., MANOVA, PCA) [40]. Note that the very large number of measures typically incorporated in shotgun approaches renders MANOVAs in general less feasible.
Multinomial logistic regression
In order to determine which of the measures constitute the best predictors for an experimental factor (group or condition), we used nominal multinomial logistic regression models. In a nutshell, such models fit the logprobability that a nominal factor falls in a certain category, expressed as a fraction of the probability that it falls in another category, i.e. $log\left({\pi}_{j}/{\pi}_{k}\right)={\beta}_{0,jk}+\sum _{i=1}^{n}{\beta}_{i,jk}{x}_{i}$. In this expression π_{ j } denotes the probability that the factor of interest is in category j, k represents the reference category, x_{ i } are the values of the i^{th} measurement, β_{ ij } are the regression coefficients, and n is the number of included measures. Since we incorporated three conditions in study A and three groups in study B, the regression initially returned two vectors of βcoefficients, namely β_{13} and β_{23} (number 3 is the reference group). However, the third coefficient β_{12} simply equals β_{13}−β_{23}. To estimate the overall importance of a measure, we finally determined the βnorm (i.e. $\sqrt{{\beta}_{13}^{2}+{\beta}_{23}^{2}+{\beta}_{12}^{2}}$). Here it is important to realize that we considered more measures than observations. Therefore the number of measures in the regression had to be constrained. We selected measures using the corresponding pvalues resulting from the individual ANOVAs; see Figure 2, left panel. We used either only the measures with p<0.05/N or sorted all measures by pvalue in descending order and considered the first seven values only^{a}. All measures were normalized to zscores before entering the regression. For study B we used the relative difference between the measures of the two conditions as input for the regression.
The overall performance of the regression was quantified in terms of the correct rate. For this, we first computed the βcoefficients. Next, we determined the predicted probabilities for each category and classified the observation according to the highest probability. Then, the correct rate was given by the fraction of observations classified properly. Note that it would have been more appropriate to divide the observations into a training and a test set, but the number of observations was not large enough to do so.
Partial least squares regression
Partial least squares (PLS) regression deals with correlated measures by decomposing in and output into factor scores which are linearly independent. PLS is similar to principal component analysis (PCA) but PCA relies on the covariance between input variables, whereas PLS capitalizes on the covariance between the input and response variables. In brief, PLS decomposes the input X (n observations ×m measures) and the output Y (n observations ×k categories) into X=T P^{ t }+δ and Y=U Q^{ t }+ε, respectively. T and U are the score matrices (both n × l), P and Q are the loading matrices (n×m and k × l, respectively), δ and ε are residuals, and l denotes the number of components. Presuming a linear relation between T and U, these equations can be combined in the form Y≈Xβ, which is the searchedafter linear model [41].
Since we included nominal variables (condition and group), we used an indicator matrix as the response variable [41]. As in conventional PCA, we here limited the number of new components used in the regression, i.e. l ≪ m. In particular, we used seven components in the PLS regression in agreement with the aforementioned multinomial logistic regression^{1}; cf. Results. Note that testing for significance of the PLS regression is possible but not straightforward [42]. For the sake of brevity we here decided not to add such a test. Quantification of performance was realized in the same way as in the multinomial logistic regression, i.e. using the correct rate; see Figure 2.
Results
Hypothesis testing
Measures from the participants with stroke that show a significant condition effect
Modality  Measure  Condition 

Metabolism  Mean ${\stackrel{\u0307}{V}}_{\text{E}}$  0.00007 (0.72) 
Mean ${\stackrel{\u0307}{V}}_{{\text{O}}_{2}}$  0.00005 (0.73)  
Mean ${\stackrel{\u0307}{V}}_{{\text{CO}}_{2}}$  0.0002 (0.66) 
Measures from the participants with an amputation that showed a significant effect
Modality  Measure  Condition  Group  Interaction 

Force plate  Mean step width    0.00004 (0.95)   
Mean stride duration    0.0004 (0.94)    
CV of stride duration    0.0002 (0.66) [1]    
Template mean (x)    0.0003 (0.34) [1]    
Mean variance events (z)    0.0004 (0.77)    
SE of sample entropy (y)    0.00001 (0.94)    
SE of sample entropy (z)    0.0002 (0.75)    
Sample entropy of phase (z)    0.0004 (0.98)    
SE of sample entropy of phase (y)    0.00001 (0.97)    
EMG  Sample entropy (S)    0.0001 (0.87)   
Lyapunov exponent (G)    0.0004 (0.50)    
Accelerometer  Sample entropy (x)    0.00003 (0.69)   
PCA eigenvalues (1)    0.0002 (0.48)    
Metabolism  Mean ${\stackrel{\u0307}{V}}_{\text{E}}$  0.0002 (0.32)     
Mean BF  0.0002 (0.32)      
Mean C_{met}    < 0.00001 (0.96)   
Multinomial logistic regression
β coefficients of the logistic regression for study A, using the 7 measures with the highest p value (2nd column)
Measure  p  β _{13}  β _{23}  β _{12}   β 

Metabolism mean ${\stackrel{\u0307}{V}}_{\text{E}}$  0.00007  5.2*  4.1*  1.1  6.7 
Accelerometer lyapunov exponent (y)  0.01  2.0  4.6*  2.6  5.7 
Metabolism mean C_{met}  0.02  3.5  3.4*  0.1  4.8 
Metabolism mean ${\stackrel{\u0307}{V}}_{{\text{CO}}_{2}}$  0.0002  2.5  1.3  3.8  4.7 
Metabolism mean ${\stackrel{\u0307}{V}}_{{\text{O}}_{2}}$  0.00005  0.5  3.4  2.9  4.5 
Accelerometer lyapunov of PC (1)  0.02  2.3  0.5  2.8  3.6 
Force plate DFA α of stride duration  0.02  2.6*  1.3*  1.3  3.2 
β coefficients of the logistic regression for study B, using the relative difference between nonStroop and Stroop results as measure
Measure  p _{1}  p _{2}  β _{13}  β _{23}  β _{12}   β 

Force plate SE of sample entropy (y)  0.02  0.03  2.8  0.6  2.1  3.5 
EMG sample entropy (TA)  0.006  0.005  0.3  1.8  2.1  2.8 
Force plate DFA α of stride duration  0.09  0.1  1.2*  0.7  1.9  2.4 
EMG SE of sample entropy of phase (VM)  0.05  0.7  1.2  0.6  1.8  2.3 
Metabolism mean (HR)  0.07  0.08  1.5  0.2  1.3  2.0 
EMG PCA eigenvalues (5)  0.07  0.02  0.8  1.3  0.5  1.6 
Force plate mean template (z)  0.07  0.4  0.7  0.8  0.1  1.1 
PLS regression
Regression coefficients of the PLS regression for study A
Measure  β _{1}  β _{2}  β _{3}   β 

Force plate DFA α of stride duration  0.41  0.25  0.16  0.51 
Force plate DFA α of event position (z)  0.14  0.22  0.36  0.44 
Force plate mean template (z)  0.14  0.15  0.29  0.36 
Force plate DFA α of event position (y)  0.25  0.25  0.01  0.35 
Metabolism cv of RER  0.11  0.17  0.28  0.35 
Force plate SE of sample entropy (z)  0.07  0.24  0.17  0.30 
Force plate mean event position (z)  0.01  0.21  0.19  0.29 
Regression coefficients of the PLS regression for study B
Measure  β _{1}  β _{2}  β _{3}   β 

Force plate SE of sample entropy (y)  0.21  0.21  0.02  0.29 
Force plate DFA α of stride duration  0.17  0.23  0.05  0.29 
EMG SE of sample entropy (VM)  0.16  0.22  0.06  0.28 
Force plate sample entropy (y)  0.19  0.09  0.14  0.25 
Accelerometer sample entropy (z)  0.17  0.05  0.15  0.24 
EMG PCA eigenvalues (5)  0.16  0.07  0.11  0.21 
EMG SE of sample entropy of phase (VM)  0.01  0.14  0.14  0.20 
EMG sample entropy (TA)  0.13  0.02  0.14  0.19 
EMG PCA eigenvalues (1)  0.11  0.01  0.15  0.19 
EMG SE of sample entropy of phase (S)  0.12  0.14  0.02  0.19 
Discussion
Shotgun approaches for analyzing multivariate gait signals can provide important insights into gait classification but they are also confronted with considerable challenges. We used data of two experimental studies to illustrate these challenges. Study A had a oneway design as the one patient group was tested in three conditions: walking unsupported at their preferred walking speed, supported at their preferred speed, and walking supported at their preferred speed of the unsupported condition. Study B incorporated three groups, two types of lowerlimb amputees and a control group, that were assessed in two conditions (i.e. a twoway design). In both cases we determined large sets of outcome measures that underwent different statistical assessments; see Figure 2 for a schematic overview.
Conventional ANOVAs returned significant main effects for study A and B, in the absence of significant interaction effects for study B. In study A, three metabolic measures were found to be significant (c.f., [20]), in study B, stride length and metabolic costs revealed group effects (e.g., [43–45]). The absence of more main effects that could be expected based on the established literature might be explained by the correction for multiple comparisons. The Bonferroni correction that we used is the most common one, but it is also quite conservative. Of course, we could have chosen alternatives like the Šidák correction, or even could have followed an entirely different route, like the BenjaminiHochberg procedure [46]. However, this would not have helped to overcome the fundamental problem: A shotgun approach may involve such an arbitrarily large set of measures that conventional statistical assessments (e.g., ANOVAs) simply become unfeasible; just adding measures will inevitably suppress significance and, on top of that, measures may be correlated, which may hamper analysis too.
We followed alternative routes, albeit equally arbitrary, and employed a multinomial logistic as well as a partial least squares regression. For the logistic regression, we used the ANOVA results to limit the number of input measures, while we used all measures in the PLS regression. Both regressions performed well in terms of success rate, which varied from 77% to 100%, indicating that the regression weights are indicative of a measure’s relative classification capacity. The logistic regressions also returned multiple significant coefficients. Notably, the regressions for study B, targeted at examining the interaction effect, performed very well while no interaction effect was found in the ANOVA.
It may be difficult to find clear correlations between many of the chosen measures and complex gait patterns. The ‘insufficiency’ of the conventional ANOVAs and the ‘improved’ performance of the logistic and PLS regression in revealing the measures’ relationships with experimental groups and conditions may be selfevident. However, when comparing the results of the logistic regression with those of the PLS regression one can realize that the measures identified as relevant differ between approaches. For example, the mean ventilation rate ${\stackrel{\u0307}{V}}_{\text{E}}$ has the highest weight in the logistic regression for study A (Table 3), but it does not appear at all in the results of the PLS regression (Table 5). For study A, only one measure appears in both regressions, namely the DFA α of stride duration. For study B, the results are more similar, with 5 of the 7 measures appearing in both tables. Both statistical posthoc assessments appear valid, at least from a more mathematical perspective, with the addition that PLS regression accounts for possible correlations between measures. We did expect results to differ but having said that the question remains: How can one decide which measures are appropriate? As the number of measures continues to increase, this question will become more important.
There are several more caveats to mention. For example, for study B we used the relative difference between the measures of the two conditions in the regression analysis. We could have chosen the ratio, the logdifference, or any other measure defining a proper metric. Similarly arbitrary was fixing the threshold to seven when ranking pvalues in the logistic regression, or in fact choosing pvalues for ranking instead of, for example, effect sizes. Moreover, the calculation of some of the outcome measures required making other arbitrary choices, such as the temporal window size in the event optimization procedure and the embedding parameters for the Lyapunov exponent, et cetera et cetera.
The logistic and PLS regressions are two selected additional methods to identify relevant measures. Statistics comes with a plethora of assessment methods from which most can be used in various (overlapping) circumstances. Our goal to stay as objective as possible is once more at stake because again we have to make somewhat arbitrary choices. That is, pushing the shotgun approach as far as possible by keeping it largely objective by including as little as possible a priori knowledge turned out to be quite challenging. While the feature definition progressed profoundly, the feature selection awaits to be implemented in a similarly advanced way. Especially in view of ‘big data’, i.e. the massive amount of data being collected to date, an apropriate feature selection is mandatory. One possibility for this would be to supplement the measure extraction by more generic, datadriven expert systems that capitalize on a priori data reduction [39, 47] – though here one simply pushes the selection problem back to the definition of measures. Alternatives are machinelearning techniques for pattern classification that are slowly penetrating gait analysis (e.g., [48–62]). We are currently developing a partly unsupervised learning approach that incorporates the literaturebased educated guesses for measure selection. In future work we will compare this to the current study. In fact, all the here discussed signalprocessing steps have already been implemented as an opensource toolbox [63].
We advocate to progress data analysis towards machine learning in combination with theorybased (i.e. educated) selection criteria. By the same token, however, we believe that shotgun approaches can be a valid addition when datamining. As we illustrated in detail how posthoc assessments may point at relevant measures and unravel interesting effects that may warrant further investigation. As it stands, however, proper insight into measure selection and posthoc statistics remains mandatory.
Conclusion
Shotgun approaches can help identifying measures for gait assessment. This is particularly true if measures are incorporated that still lack a theoretical underpinning in the context of gait analysis. In order to extract a specific measure’s classification capacity, however, one cannot solely rely on its statistical validity (e.g., the outcome of an ANOVA). For the current examples we showed that both logistic as well as partial least squares regression produced interesting albeit diverging results. Both posthoc analyses can be defended from a statistical/mathematical perspective. This difference is seminal for what we believe is a major concern: At some point in the analysis arbitrary choices are inevitable. While the outlined procedure may be useful in many situations, we believe that in order to create a blind approach, more generic methods are required. In this context one can think of machinelearning techniques or even more general expert systems borrowed from the field of artificial intelligence.
Consent
Written informed consent was obtained from all the participants for the publication of this report.
Endnote
^{a} Introducing a threshold always implies a degree of arbitrariness. Selecting p<0.05 to define significance is as arbitrary as selecting the seven largest pvalues as done here. In fact we chose that cutoff at will because in the current study it only served to improve legibility, although there is a limit to the number of measures that can be included due to the finite sample size.
Declarations
Acknowledgements
RK and AD received financial support from the Netherlands Organisation for Scientific Research (NWO grant #40008127).
Authors’ Affiliations
References
 Dingwell J, Cusumano J, Sternad D, Cavanagh P: Slower speeds in patients with diabetic neuropathy lead to improved local dynamic stability of continuous overground walking. J Biomech. 2000, 33 (10): 12691277.View ArticlePubMedGoogle Scholar
 Dingwell JB, Kang HG: Differences between local and orbital dynamic stability during human walking. J Biomech Eng. 2007, 129 (4): 586593.View ArticlePubMedGoogle Scholar
 Hausdorff JM: Gait variability: methods, modeling and meaning. J Neuroeng Rehabil. 2005, 2 (1): 19View ArticlePubMedPubMed CentralGoogle Scholar
 Lamoth CJC, Beek PJ, Meijer OG: Pelvis–thorax coordination in the transverse plane during gait. Gait Posture. 2002, 16 (2): 101114.View ArticlePubMedGoogle Scholar
 Yamasaki T, Nomura T, Sato S: Phase reset and dynamic stability during human gait. BioSystems. 2003, 71 (1): 221232.View ArticlePubMedGoogle Scholar
 Courtine G, Schieppati M: Tuning of a basic coordination pattern constructs straightahead and curved walking in humans. J Neurophysiol. 2004, 91 (4): 15241535.View ArticlePubMedGoogle Scholar
 Desloovere K, Molenaers G, De Cat J, Pauwels P, Van Campenhout A, Ortibus E, Fabry G, De Cock P: Motor function following multilevel botulinum toxin type a treatment in children with cerebral palsy. Dev Med Child Neurol. 2007, 49 (1): 5661.View ArticlePubMedGoogle Scholar
 Diedrich FJ, Warren WHJr: The dynamics of gait transitions: effects of grade and load. J Mot Behav. 1998, 30 (1): 6078.View ArticlePubMedGoogle Scholar
 Donker SF, Beek PJ: Interlimb coordination in prosthetic walking: effects of asymmetry and walking velocity. Acta Psychologica. 2002, 110 (2): 265288.View ArticlePubMedGoogle Scholar
 Haken H, Kelso JS, Fuchs A, Pandya AS: Dynamic pattern recognition of coordinated biological motion. Neural Netw. 1990, 3 (4): 395401.View ArticleGoogle Scholar
 Kang HG, Dingwell JB: Effects of walking speed, strength and range of motion on gait stability in healthy older adults. J Biomech. 2008, 41 (14): 28992905.View ArticlePubMedGoogle Scholar
 Lamoth CJC, Daffertshofer A, Huys R, Beek PJ: Steady and transient coordination structures of walking and running. Hum Mov Sci. 2009, 28 (3): 371386.View ArticlePubMedGoogle Scholar
 Lamoth CJC, Meijer OG, Daffertshofer A, Wuisman PIJM, Beek PJ: Effects of chronic low back pain on trunk coordination and back muscle activity during walking: changes in motor control. Eur Spine J. 2006, 15 (1): 2340.View ArticlePubMedPubMed CentralGoogle Scholar
 Lamoth CJC, Ainsworth E, Polomski W, Houdijk H: Variability and stability analysis of walking of transfemoral amputees. Med Eng Phys. 2010, 32 (9): 10091014.View ArticlePubMedGoogle Scholar
 van Emmerik R, McDermott W, Haddad JM, van Wegen E: Agerelated changes in upper body adaptation to walking speed in human locomotion. Gait Posture. 2005, 22 (3): 233239.View ArticlePubMedGoogle Scholar
 Wu W, Meijer OG, Jutte PC, Uegaki K, Lamoth CJC: Sander de Wolf G, van Dieën JH, Wuisman PIJM, Kwakkel G, de Vries JIP, Beek PJ: Gait in patients with pregnancyrelated pain in the pelvis: an emphasis on the coordination of transverse pelvic and thoracic rotations. Clin Biomech. 2002, 17 (9–10): 678686.View ArticleGoogle Scholar
 Lipsitz LA, Goldberger AL: Loss of complexity and aging. JAMA. 1992, 267 (13): 18061809.View ArticlePubMedGoogle Scholar
 Hausdorff JM, Mitchell SL, Firtion R, Peng C, Cudkowicz ME, Wei JY, Goldberger AL: Altered fractal dynamics of gait: reduced strideinterval correlations with aging and huntington’s disease. J Appl Physiol. 1997, 82 (1): 262269.PubMedGoogle Scholar
 Hausdorff JM: Gait dynamics in parkinson’s disease: common and distinct behavior among stride length, gait variability, and fractallike scaling. Chaos: Interdiscip J Nonlinear Sci. 2009, 19 (2): 026113026113.View ArticleGoogle Scholar
 IJmker T, Houdijk H, Lamoth CJ, Jarbandhan AV, Rijntjes D, Beek PJ, van der Woude LH: Effect of balance support on the energy cost of walking after stroke. Arch Phys Med Rehabil. 2013, 94 (11): 22552261.View ArticlePubMedGoogle Scholar
 Wezenberg D, van der Woude LH, Faber WX, de Haan A, Houdijk H: Relation between aerobic capacity and walking ability in older adults with a lowerlimb amputation. Arch Phys Med Rehabil. 2013, 94 (9): 17141720.View ArticlePubMedGoogle Scholar
 Wezenberg D, de Haan A, van Bennekom C, Houdijk H: Mind your step: metabolic energy cost while walking an enforced gait pattern. Gait Posture. 2011, 33 (4): 544549.View ArticlePubMedGoogle Scholar
 Geurts ACH, Mulder TW, Nienhuis B, Rijken R: Dualtask assessment of reorganization of postural control in persons with lower limb amputation. Arch Phys Med Rehabil. 1991, 72 (13): 10591064.PubMedGoogle Scholar
 Potvin JR, Brown SHM: Less is more: high pass filtering, to remove up to 99% of the surface emg signal power, improves emgbased biceps brachii muscle force estimates. J Electromyogr Kinesiol. 2004, 14 (3): 389View ArticlePubMedGoogle Scholar
 Myers L, Lowery M, O’malley M, Vaughan C, Heneghan C, St Clair Gibson A, Harley Y, Sreenivasan R: Rectification and nonlinear preprocessing of emg signals for corticomuscular analysis. J Neurosci Methods. 2003, 124 (2): 157165.View ArticlePubMedGoogle Scholar
 Plotnik M, Giladi N, Hausdorff JM: A new measure for quantifying the bilateral coordination of human gait: effects of aging and Parkinson’s disease. Exp Brain Res. 2007, 181 (4): 561570.View ArticlePubMedGoogle Scholar
 Jordan K, Challis JH, Newell KM: Walking speed influences on gait cycle variability. Gait Posture. 2007, 26 (1): 128134.View ArticlePubMedGoogle Scholar
 Roerdink M, Coolen BH, Clairbois BH, Lamoth CJC, Beek PJ: Online gait event detection using a large force platform embedded in a treadmill. J Biomech. 2008, 41 (12): 26282632.View ArticlePubMedGoogle Scholar
 Richman JS, Moorman JR: Physiological timeseries analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol. 2000, 278 (6): 20392049.Google Scholar
 Lamoth C, van Deudekom F, van Campen J, Appels B, de Vries O, Pijnappels M: Gait stability and variability measures show effects of impaired cognition and dual tasking in frail people. J Neuroeng Rehabil. 2011, 8 (1): 2View ArticlePubMedPubMed CentralGoogle Scholar
 Lake DE, Richman JS, Griffin MP, Moorman JR: Sample entropy analysis of neonatal heart rate variability. Am J Physiol Regul Integr Comp Physiol. 2002, 283 (3): 789797.View ArticleGoogle Scholar
 Peng CK, Mietus J, Hausdorff J, Havlin S, Stanley HE, Goldberger A: Longrange anticorrelations and nongaussian behavior of the heartbeat. Phys Rev Lett. 1993, 70 (9): 1343View ArticleGoogle Scholar
 Jordan K, Challis JH, Newell KM: Long range correlations in the stride interval of running. Gait Posture. 2006, 24 (1): 120125.View ArticlePubMedGoogle Scholar
 Dingwell JB, Marin LC: Kinematic variability and local dynamic stability of upper body motions when walking at different speeds. J Biomech. 2006, 39 (3): 444452.View ArticlePubMedGoogle Scholar
 Bruijn SM, Ten Kate WRT, Faber GS, Meijer OG, Beek PJ, van Dieën JH: Estimating dynamic gait stability using data from nonaligned inertial sensors. Ann Biomed Eng. 2010, 38: 25882593.View ArticlePubMedPubMed CentralGoogle Scholar
 Federolf P, Tecante K, Nigg B: A holistic approach to study the temporal variability in gait. J Biomech. 2012, 45 (7): 11271132.View ArticlePubMedGoogle Scholar
 Bruijn SM, Bregman DJJ, Meijer OG, Beek PJ, van Dieën JH: Maximum Lyapunov exponent as predictors of global gait stability: a modelling approach. Med Eng Phys. 2012, 34: 428436.View ArticlePubMedGoogle Scholar
 Rosenstein MT, Collins JJ, De Luca CJ: A practical method for calculating largest Lyapunov exponents from small data sets. Phys Nonlinear Phenom. 1993, 65 (1–2): 117134.View ArticleGoogle Scholar
 Daffertshofer A, Lamoth CJC, Meijer OG, Beek PJ: PCA in studying coordination and variability: a tutorial. Clin Biomech. 2004, 19 (4): 415428.View ArticleGoogle Scholar
 Huberty CJ, Morris JD: Multivariate analysis versus multiple univariate analyses. Psychol Bull. 1989, 105 (2): 302View ArticleGoogle Scholar
 Rosipal R, Krämer N: Overview and recent advances in partial least squares. Subspace, Latent Structure and Feature Selection. 2006, Berlin: Springer, 3451.View ArticleGoogle Scholar
 Abdi H, Williams LJ: Partial least squares methods: partial least squares correlation and partial least square regression. Computational Toxicology. 2013, Berlin: Springer, 549579.View ArticleGoogle Scholar
 Sadeghi H, Allard P, Duhaime M: Muscle power compensatory mechanisms in belowknee amputee gait. Am J Phys Med Rehabil. 2001, 80 (1): 2532.View ArticlePubMedGoogle Scholar
 Powers CM, Rao S, Perry J: Knee kinetics in transtibial amputee gait. Gait Posture. 1998, 8 (1): 17.View ArticlePubMedGoogle Scholar
 Waters R, Perry J, Antonelli D, Hislop H: Energy cost of walking of amputees: the influence of level of amputation. J Bone Joint Surg. 1976, 58 (1): 4246.PubMedGoogle Scholar
 Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc B. 1995, 57 (1): 289300.Google Scholar
 Troje NF: Decomposing biological motion: a framework for analysis and synthesis of human gait patterns. J Vis. 2002, 2 (5): 371387.View ArticlePubMedGoogle Scholar
 Alaqtash M, SarkodieGyan T, Yu H, Fuentes O, Brower R, Abdelgawad A: Automatic classification of pathological gait patterns using ground reaction forces and machine learning algorithms. Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE. 2011, Boston: IEEE, 453457.View ArticleGoogle Scholar
 Barton J, Lees A: An application of neural networks for distinguishing gait patterns on the basis of hipknee joint angle diagrams. Gait Posture. 1997, 5 (1): 2833.View ArticleGoogle Scholar
 Begg R, Kamruzzaman J: A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data. J Biomech. 2005, 38 (3): 401408.View ArticlePubMedGoogle Scholar
 Begg RK, Palaniswami M, Owen B: Support vector machines for automated gait classification. IEEE Trans Biomed Eng. 2005, 52 (5): 828838.View ArticlePubMedGoogle Scholar
 Chau T: A review of analytical techniques for gait data. part 2: neural network and wavelet methods. Gait Posture. 2001, 13 (2): 102120.View ArticlePubMedGoogle Scholar
 Holzreiter SH, Köhle ME: Assessment of gait patterns using neural networks. J Biomech. 1993, 26 (6): 645651.View ArticlePubMedGoogle Scholar
 Kamruzzaman J, Begg RK: Support vector machines and other pattern recognition approaches to the diagnosis of cerebral palsy gait. IEEE Trans Biomed Eng. 2006, 53 (12): 24792490.View ArticlePubMedGoogle Scholar
 Köhle ME, Merkl D, Kastner J: Clinical gait analysis by neural networks: issues and experiences. Proceedings of the 10th IEEE Symposium on ComputerBased Medical Systems. 1997, Maribor: IEEE, 138143.Google Scholar
 Lafuente R, Belda J, SanchezLacuesta J, Soler C, Prat J: Design and test of neural networks and statistical classifiers in computeraided movement analysis: a case study on gait analysis. Clin Biomech. 1998, 13 (3): 216229.View ArticleGoogle Scholar
 Mannini A, Sabatini AM: Machine learning methods for classifying human physical activity from onbody accelerometers. Sensors. 2010, 10 (2): 11541175.View ArticlePubMedPubMed CentralGoogle Scholar
 Mezghani N, Husse S, Boivin K, Turcot K, Aissaoui R, Hagemeister N, de Guise JA: Automatic classification of asymptomatic and osteoarthritis knee gait patterns using kinematic data features and the nearest neighbor classifier. IEEE Trans Biomed Eng. 2008, 55 (3): 12301232.View ArticlePubMedGoogle Scholar
 Pogorelc B, Bosnić Z, Gams M: Automatic recognition of gaitrelated health problems in the elderly using machine learning. Multimed Tool Appl. 2012, 58 (2): 333354.View ArticleGoogle Scholar
 Preece SJ, Goulermas JY, Kenney LP, Howard D, Meijer K, Crompton R: Activity identification using bodymounted sensors—a review of classification techniques. Physiol Meas. 2009, 30 (4): 1View ArticleGoogle Scholar
 Simon SR: Quantification of human motion: gait analysis  benefits and limitations to its application to clinical problems. J Biomech. 2004, 37 (12): 18691880.View ArticlePubMedGoogle Scholar
 von Tscharner V, Enders H, Maurer C: Subspace identification and classification of healthy human gait. PLoS one. 2013, 8 (7): 65063View ArticleGoogle Scholar
 Kaptein R, Daffertshofer A: UPMOVE: a Matlab Toolbox for the Analysis and Classification of Human Gait. [http://www.upmove.org]
Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.