online on 15 December 2016 as doi: /jeb Modular control during incline and level walking in humans

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1 First posted online on 15 December 2016 as /jeb J Exp Biol Advance Access Online the most Articles. recent version First at posted online on 15 December 2016 as doi: /jeb Access the most recent version at Modular control during incline and level walking in humans Lars Janshen 1, Alessandro Santuz 1,2, Antonis Ekizos 1,2, Adamantios Arampatzis 1,2 1 Department of Training and Movement Sciences, Humboldt-Universität zu Berlin, Philippstraße 13, Haus Berlin (Germany) 2 Berlin School of Movement Science, Humboldt-Universität zu Berlin, Philippstraße 13, Haus Berlin (Germany) Correspondence to: Adamantios Arampatzis Humboldt-Universität zu Berlin Dept. of Training and Movement Sciences Philippstr. 13, Haus Berlin a.arampatzis@hu-berlin.de Homepage: Phone: +49 (0) Fax: +49 (0) Keywords: locomotion, incline, muscle synergies, motor modules, neural control, quantification Published by The Company of Biologists Ltd.

2 Summary Statement: Identified differences in muscle synergies during incline and level walking provide further evidence of a flexible use of a consistent set of neural elements in human neuromuscular control. Abstract The neuromuscular control of human movement can be described by a set of muscle synergies factorized from myoelectric signals. There is some evidence that the selection, activation and flexible combination of these basic activation patterns are of a neural origin. We investigated the muscle synergies during incline and level walking to evaluate changes in the modular organization of neuromuscular control related to changes in the mechanical demands. Our results revealed five fundamental (not further factorizable) synergies for both walking conditions but with different frequencies of appearance of the respective synergies during incline compared to level walking. Low similarities across conditions were observed in the timing of the activation patterns (motor primitives) and the weightings of the muscles within the respective elements (motor modules) for the synergies associated with the touchdown, mid-stance and early push-off phase. The changes in the neuromuscular control could be attributed to changes in the mechanical demands in support, propulsion and medio-lateral stabilization of the body during incline compared to level walking. Our findings provide further evidence that the central nervous system flexibly uses a consistent set of neural control elements with a flexible temporal recruitment and modifications of the relative muscle weightings within each element to provide stable locomotion under varying mechanical demands during walking.

3 Introduction Based on the high redundancy of the motor system, a single motor task can be performed in multiple ways with a similar end result (Bernsteĭn, 1967). The individual control of every single muscle to perform each specific motor task or its variations would imply a very high complexity of the human brain. For this so-called degrees of freedom problem Bernsteĭn (1967) developed a well-accepted theory of motor control simplification, in which the complexity of the high movement diversity is reduced by combining a lower number of basic muscle activation patterns to accomplish a specific motor task (Chhabra and Jacobs, 2006). There is some evidence that the selection, activation and flexible combination of these basic patterns, called muscle synergies, are of a neural origin and follow a modular organization (Bizzi and Cheung, 2013; Bizzi et al., 2008; Cheung et al., 2009b; d'avella et al., 2003; Roh et al., 2011). These basic muscular activation patterns are supposed to be adjusted in their specific timing and force generation (Latash et al., 2010; Martin et al., 2009). It is commonly accepted that motor behaviors with a similar task complexity demonstrate the same number and congruent shapes of muscle synergies (Chhabra and Jacobs, 2006). From this, a changing number of synergies may be related to changes in the biomechanical demands of the motor tasks (Clark et al., 2010). Assuming a neural origin of muscle synergies, changes in the dynamic demands might be represented in the modular organization of the neuromuscular system. This could probably result in changes of the number of muscle synergies and/or the basic shape of the activation patterns as well as in alterations of the specific muscle contributions to the respective synergies. Based on this approach, previous studies on level walking reported highly consistent results for a number of four (Oliveira et al., 2012) to five synergies and their time structure across individuals (Cappellini et al., 2006; Chvatal and Ting, 2013; Ivanenko et al., 2006a; Sasaki and Neptune, 2006). The time structure of the synergies, also referred to as motor primitives (Dominici et al., 2011) were also consistent across different walking speeds (Ivanenko et al., 2004; Ivanenko et al., 2006a; Ivanenko et al., 2006b). Furthermore, the specific muscle contributions (motor modules) to the respective synergies have been found to change with varying loading conditions (Ivanenko et al., 2004; McGowan et al., 2010). In their experimental study Ivanenko et al. (2004) found that, although the individual muscle activation differed with changes in the velocity and in the gravitational load, the modular control of the muscles showed more or less similar basic patterns. Using a

4 musculoskeletal model, McGowan et al. (2010) provided evidence that the timing of the modular control of human walking was unaffected by different mechanical demands and that a flexible modulation of motor modules may robustly reproduce human walking. In everyday life, changes in the environment, such as slope, require an appropriate recruitment and coordination of muscles and therefore are one important factor in human locomotion. For walking on inclined surfaces, changes in the resultant joint torque characteristics compared to level walking have been demonstrated especially for the touchdown and push-off phases (Devita et al., 2008; Lay et al., 2006; McIntosh et al., 2006; Silder et al., 2012). These mechanical differences result in changes of the demands on the activation magnitude and/or durations of the respective muscles (Fischer et al., 2015; Franz and Kram, 2012; Lay et al., 2007). Analyzing potential differences in muscle synergies during incline vs. level walking could provide further insights in motor strategies applied under different environmental walking conditions. In particular, it might help to understand modular control strategies and adjustments during human locomotion in relation to changes of the dynamic demand in everyday life. The purpose of the current study was to investigate the modular control of human locomotion during incline and level walking using the muscle synergies concept. To compare both movement conditions more quantitatively, we first calculated the similarities using the coefficient of determination (R2) and then set their repeatability (intraday) and reproducibility (interday) thresholds. We hypothesize differences in the modular organization of the neuromuscular system between incline compared to level walking, primarily based on changes in the specific muscle contributions to the respective synergies. On the other hand we expect the same number of synergies as well as rather consistent time structures for both walking tasks, based on the previously reported high robustness of these parameters across various walking conditions (Cappellini et al., 2006; Chvatal and Ting, 2013; Ivanenko et al., 2004; Ivanenko et al., 2006a; Ivanenko et al., 2006b; Sasaki and Neptune, 2006).

5 Materials and Methods Participants Ten female and ten male young adults (table 1) volunteered for the experiments. All participants were right dominant and physically active. They did not use orthotic insoles and had no known history of neurological or motor disorders or injuries over the last six months prior to the measurements. All participants had common but no advanced experience in incline walking. Written informed consent was given before participation and the local ethical committee approved the procedures. Experimental design To obtain the individual preferred walking speed for each participant, we used the method of limits (Treutwein, 1995) performed on a treadmill. Starting at 0.8 m/s the speed was randomly increased by 0.02 to 0.05 m/s at varying time intervals of five to ten seconds. After the participant confirmed the preferred speed, the procedure was repeated by decreasing the speed starting from a pace 0.5 to 1.0 m/s higher than the preferred speed. The used preferred speed was calculated by the average speed of both tests. If the difference between tests was larger than 10%, the test was repeated. Thereafter all participants randomly performed two trials of incline and two of level walking on the treadmill on each of two measurement days resulting in eight trials per participant. Time between trials on the same day was 10 minutes, time between measurement days was at least 48 h (137 ± 92 h). During this time, the participants were asked not to change their daily routine and not to have any hard training sessions the day prior to the measurements. Level walking was performed at the individual preferred speed (1.4 ± 0.2 m/s). Incline walking was performed at a slope of 10 (Devita et al., 2008) and 85% of the preferred speed. In each trial recordings began after a preliminary movement stabilization phase of around 60 s. Data recording The activity of 24 muscles of the right body side was recorded by surface EMG. The pairs of Ag/AgCl electrodes (N-00-S, Ambu, Denmark) for bipolar derivation were applied according to the SENIAM standards (Hermens et al., 2000). Signals were recorded at 1000 Hz and 16 bit A/D converter resolution using two synchronized

6 wireless EMG systems (one 16-channel and one 8-channel, myon AG; Switzerland). The EMG systems had a built-in band-pass filter (5-500 Hz, 3dB/oct, 4th order). Lower leg muscles recorded were gastrocnemius medialis (GM) and lateralis (GL), soleus (SO), peronaeus longus (PL) and tibialis anterior (TA). Upper leg and hip muscles recorded were vastus medialis (VM) and lateralis (VL), rectus femoris (RF), semitendinosus (ST), tensor fasciae latae (TFL), adductor longus (AL) the long head of the biceps femoris (BFS) and gluteus medius (GMED) and maximus (GMAX). Trunk EMG signals were obtained from the lower part (L2) of the erector spinae (ESL2), the rectus abdominis (RA), anterior external obliquii (AEO), latissimus dorsi (LD) and trapezius (TRS). In addition, biceps brachii (BB), the long head of triceps brachii (TB), the anterior (DA) and posterior (DP) head of the deltoideus and the splenius capitis (SPL) muscles were recorded. During the walking tasks 50 (49 ± 4) consecutive gait cycles were measured (Oliveira et al., 2014). Plantar pressure distributions were measured at 120 Hz by a pressure plate (FDM-THM-S, zebris Medical GmbH, Germany) integrated in the treadmill (mercury, H-p-cosmos Sports & Medical GmbH, Germany). The pressure plate data were acquired using the proprietary software (WinFDM-T v2.5.1, zebris Medical GmbH, Isny im Allgäu, Germany) and then extracted in a raw format for autonomous post-processing using a validated custom algorithm (Santuz et al., 2016). The pressure plate was synchronized with the EMG data by an analogue signal. Data processing EMG signals were high-pass filtered, fully rectified and low-pass filtered at cut-off frequencies of 50 and 20 Hz respectively using 4 th order zero-lag IIR Butterworth filters. Each EMG envelope was time-normalized to 200 data points per gait cycle (Cheung et al., 2009a). The 50 cycles of each trial were averaged (Oliveira et al., 2014) and the amplitude was normalized setting the highest peak for each muscle of each participant for each measurement day to one (Bizzi et al., 2008; Devarajan and Cheung, 2014; Karamanidis et al., 2004). The gait cycles (i.e. stance and swing phase) were obtained from the pressure plate. The touchdown and toe-off were determined from the ground reaction forces using a validated custom post-processing algorithm (Santuz et al., 2017), where the touchdown was identified as the first non-zero pressure matrix after the last toe-off. In addition to the contact times, the pressure data were used to quantify step lengths, cadence, vertical ground reaction forces (VGRFs) and total impulse of the stance phase for the gait cycles. For each step the average

7 VGRF of the first and second half of the stance phase was calculated and the total impulse was obtained by integrating the VGRFs over the contact time. Forces and impulses were then related to body weight [BW]. Again, the data of 50 gait cycles for each trial were averaged. Muscle synergies: Muscle synergy extraction was performed by applying a customized non-negative matrix factorization (NMF) algorithm (Lee and Seung, 1999; Lee and Seung, 2001). The original data matrix (V 24x200) consisted of 24 rows (number of muscles) and 200 columns (number of data points in time per normalized gait cycle). To determine basic time curves and the respective weights of underlying muscle synergies across all EMG signals, the original matrix was factorized into two smaller matrices. The matrix (H) representing the synergy time curves will be referred to as motor primitives (Dominici et al., 2011) and consists of 200 rows and N columns, where N is the number of synergies. The matrix (W) consisting of 24 rows and N columns contains the time-invariant muscle weights and will be referred to as motor modules (Gizzi et al., 2011). The combination of H and W describes the synergies necessary to accomplish a movement. The reconstruction of the original EMG matrix was performed by multiplying the motor module with the motor primitive matrices (V R = W 24xN H 200xN). The reconstruction quality was assessed by calculating the coefficient of determination R² between the original and the reconstructed matrix (Cheung et al., 2005). The R² is expressed by (1-RSS/SST), where RSS is the residual sum of squares and SST is the total sum of squares. The R 2 has been calculated as follows (Equation 1): R 2 = 1 RSS SST = 1 (V V R ) 2 VV R (V V ) 2 V 2 (1) The limit of convergence was reached when a change in the calculated R 2 between V and V R was smaller than 0.01% in the last 20 iterations (Cheung et al., 2005). This was done for a number of 1 to 10 synergies. The computation was repeated 10 times for each synergy, each time creating new randomized initial matrices, in order to avoid local minima (d'avella and Bizzi, 2005). These results were plotted as a curve of R² vs. the number of synergies. To determine a sufficient number of synergies required to represent the original EMG signals, we applied a largely used method based on the cross-validation of the R 2 values obtained for each number of synergies (Cheung et al., 2005). The curve of R 2 vs. the number of synergies was fitted by a simple

8 linear regression model that initially uses all the ten synergies. The mean squared error was then calculated. This procedure was repeated, each time removing the lowest point of the R² curve for the applied linear fitting until the amount of the mean squared error was less than The procedure described above resulted in a specific number of synergies for each of the total 160 trials (20 participants, 2 days, 4 trial per day) necessary to sufficiently reconstruct the respective original EMG dataset. Based on the shape of the motor primitives we classified fundamental and combined synergies. A fundamental synergy can be defined as an activation pattern that cannot be factorized any further. When two (or more) fundamental synergies are blended into one, a combined synergy appears. An example of combined synergies is reported in Figure 1. The fundamental synergies recognition was implemented using a custom learning algorithm based on a curvefitting model. The first implementation step consists in choosing some examples of single-peak activation patterns, which might represent a fundamental primitive. The code is then fed by these manually-picked examples of fundamental primitives and a search of similar shapes is done across the whole dataset of factorized curves. With a first iteration, the primitives that have a high similarity (R 2 > 0.95) with the ones present in the manually-created database are added to the set. The number of fundamental primitives is then selected by looking at the motor modules and merging possible repetitions. After updating the database, the code starts the recognition across the entire dataset searching, synergy-by-synergy, for similar primitives (we found R 2 > 0.5 to be a good threshold in this phase). Non-recognized curves can then be visually inspected with an interactive routine or automatically identified as new fundamental or combined primitives. This approach, validated in a pilot study, can reproduce the results of a completely manual selection of the curves with a margin of error of ± 5%. Statistical analysis: The R² was calculated to assess the similarity of the motor primitives and motor modules respectively between incline and level walking. In our analysis, we only evaluated the fundamental synergies, leaving out the combined (Figure 1). The outcomes are the mean R² values of day 1 and day 2 obtained by averaging the two

9 trials of day 1 and the two trials of day 2 and comparing incline (first condition) and level walking (second condition). Corresponding to the notion that muscle synergies are neurophysiological entities (Bizzi and Cheung, 2013) this analysis was performed separately for each synergy identified by the NMF. Since for each condition we recorded two trials per day in two different days, we could investigate the intraday and interday measurements repeatability and reproducibility. The intraday similarities were computed for both conditions by comparing the two trials of each measurement day. The interday similarities were found by comparing the average of the first with the average of the second day s two trials. For classification purposes, we calculated a similarity threshold defined by the averages of four intraday R² similarities (both measurement days and both walking conditions). The difference between incline and level walking was classified as relevant when the R² similarity between the walking conditions for the respective synergies was lower than the similarity threshold defined above. This allowed for the identification of global differences between walking conditions in the motor primitives and motor modules within a specific synergy. Wilcoxon signed-rank tests were used to identify differences in the average number of muscle synergies (factorization rank) and the frequencies of appearance of each of the synergies observed during incline vs. level walking. An ANOVA for repeated measures was performed for the similarities of the motor primitives and motor modules respectively. If the normality assumptions necessary for the validity of the ANOVA was not satisfied, the Wilcoxon test was used. For any other comparison, e.g. of the pressure plate data, we used a paired samples t-test or a Wilcoxon signed-rank test when the data did not satisfy the normality assumptions. All levels of significance were set to α = All the statistical analyses were conducted using R v3.2.2 (R Found. for Stat. Comp.).

10 Results Gait Parameters Comparing incline to level walking, all participants demonstrated significantly (p < 0.01) longer contact time (0.564 ± s vs ± s), shorter (p < 0.001) step length (0.64 ± 0.07 m vs ± 0.07 m) and lower (p < 0.01) cadence (step frequency) of 106 ± 8 to 114 ± 6 steps/minute. The mean values of the left and right vertical ground reaction forces (VGRFs) in the first half of the stance phase were significantly lower (p<0.001) in the incline condition. Expressing the mean VGRFs in body weights [BW], participants showed 0.92 ± 0.1 BW during incline compared to 0.99 ± 0.08 BW level walking. The total impulse related to body weight was significantly (p = 0.001) higher during incline (0.52 ± 0.04 BW s) compared to level walking (0.50 ± 0.04 BW s). Muscle Synergies Resulting from the NMF and reconstruction quality criteria, no significant differences for the number of synergies was observed across walking conditions (p = 0.30). During incline walking 5.0 ± 0.6 synergies and during level walking 4.8 ± 0.8 synergies were necessary to reconstruct the original dataset. The average reconstruction quality of the NMF represented by the coefficient of determination (R²) was 0.90 and 0.92 for incline and level walking respectively. Across all 160 trials, we also observed no significant differences (p = 0.22) in the number of recognized fundamental synergies during incline and level walking. In both conditions, up to five fundamental synergies were observed (Figure 2 and 3). The first synergy (Syn 1, peak at ~4% of the gait cycle) was strongly related to foot contact and the early stance phase (Figure 2 and 3). As shown by the motor modules, Syn 1 mainly represented the activation of the hip extensors (GMED, GMAX) and knee extensors (VM, VL, RF). The second synergy (Syn 2, peak at ~40% of the gait cycle) is associated to the mid-stance and the beginning of the push-off phase, with large contributions of the plantar flexor muscles (GM, GL, SOL and PL). The third synergy (Syn 3, peak at ~50% of the gait cycle) occurred at the push-off phase. Dominated by the back extensor (ESL1) it mainly accounts for the upper body muscles (LD, DP, TB). The fourth synergy (Syn 4, peak at ~60% of the gait cylce) mainly appeared during the

11 transition from stance to swing and ranged until the mid-swing phase. During incline walking, synergy four represented the knee extensors and abductors (RF, TFL, AL) as well as the LD muscle. During level walking, the contributions additionally focused on the trunk muscles (SPL, RA, AEO). The fifth synergy (Syn 5, peak at ~90% gait cycle) was related to the late swing phase including the preparation of the next foot contact and represented the activation of the knee adductors and flexors (ST, BFL) and the dorsi flexors (TA). Figure 2 also shows the motor primitives of the fundamental synergies for each trial of the two walking conditions separately including the specific frequencies of appearance. Significantly higher frequencies of appearance (p = 0.02) were observed for Syn 1 and Syn 2 with values of 99% and 98% during incline compared to 70% and 86% during level walking, respectively (Figure 2). Syn 3 was observed in 72% and 74%, respectively. Syn 4 demonstrated a significantly higher (p = 0.04) frequency of appearance of 21% during incline compared to 5% during level walking, whereas Syn 5 was present in 73% and 71% of all incline and level walking tasks, respectively. The combined synergies, which are not presented in these graphs, demonstrated no significant differences in the frequency of appearance (i.e. 29 % for incline and 30% for level walking, p = 0.67) across both walking conditions. Muscle synergies similarities The R 2 was used to assess the similarities between incline and level walking for the motor primitives and motor modules. Based on the intraday and interday R² similarities (Table 2) we defined synergy-specific thresholds for the motor primitives and motor modules. These thresholds incorporate any bias included in the process of measuring, pre-processing and factorizing the EMG signals. Table 3 reports the similarity values when comparing the incline and level walking conditions. The similarities of the motor primitives (H) for the synergies one, two, three and five are lower than the respective thresholds. For the motor modules, values lower than the thresholds were observed for the synergies one, two and five. The number of recognized fundamental primitives for synergy four was so small that the similarity analysis could not be performed due to missing data (indicated as NA in Table 2 and 3).

12 Discussion As expected, we observed a consistent number of fundamental synergies for each of both walking tasks, indicating a convergent global activation pattern within the general movement type of locomotion. In both walking tasks, we observed up to five fundamental synergies in young adults. However, in many walking trials not all of the fundamental synergies could be observed, thus resulting in different frequencies of appearance. For instance, Syn 4 was only present in 5% of the level and 21% of the incline walking condition. In these cases, the minimum number of fundamental synergies needed to reconstruct the original EMG signals (i. e. the factorization rank) was four or less. In addition, the frequencies of appearance of the fundamental synergies partly resulted from the presence of combined synergies. During incline walking the frequencies of appearance of the muscle synergies Syn 1, 2 and 4 were significantly increased compared to level walking. To date there is no explicit interpretation regarding changes in the frequency of appearance of muscle synergies. However, motor activity that is critical to successfully achieve a motor task has been found to be less variable compared to motor activity that does not directly contribute or interfere with the task (Bernsteĭn, 1967). Therefore, it can be argued that the nervous system focuses on the regulation of motor outputs that are directly related to task goals (Ting et al., 2015). From the neural perspective of modularity, an increase in the appearance of fundamental synergies may represent a more differentiated control of specific movement tasks (Clark et al., 2010; Ting et al., 2015). Our findings support the idea of a flexible use of five fundamental synergies during incline and level walking. The higher frequencies of appearance of Syn 1, 2 and 4 during incline walking could reflect a more constrained locomotor drive when dealing with activities that include higher mechanical demands. In fact, during incline walking more muscle work has to be done to lift-up and to stabilize the body (Fischer et al., 2015; Franz and Kram, 2012; Lay et al., 2007). The greater resultant joint torques during incline walking (Devita et al., 2008; Silder et al., 2012), especially in the touchdown and pushoff phase, correspond to the significantly larger vertical impulses in incline walking observed in our data. Furthermore, the four-times higher frequency of appearance of Syn 4 during incline walking might be associated with the higher mechanical demand to stabilize the trunk due to its increase forward lean (Prentice et al., 2004). The higher demand to stabilize the trunk during incline walking may increase the need to more

13 distinctly attribute the required muscle activation to a specific synergy compared to level walking, where the activation is (distributed across / included in) other synergies. While walking the leg muscles can be characterized by three major functions: (1) generation of support during touchdown by counteracting gravity, (2) generation of propulsion, accelerating the body forward and (3) control the mediolateral balance during each step (Perry, 1967; Winter, 1995). In the comparison of incline to level walking, the motor primitives (time structure of muscle activation patterns) and the motor modules (muscle weightings) of the first, second and fifth synergy demonstrated relevant dissimilarities across both walking conditions. The differences found in the muscle synergies in the early and mid-stance phase as well as during the push-off phase between incline and level walking provide evidence for adaptive modifications of the neuromuscular control to account for increased mechanical demands during incline walking. It has been shown that muscles generating vertical support and forward propulsion as for example gluteus medius, vasti medialis and lateralis, soleus and gastrocnemii also substantially contribute to the mediolateral stability (Pandy et al., 2010). Furthermore, the hip adductors and the plantarflexor everters accelerate the center of mass laterally controlling the mediolateral balance during the stance phase of walking (Perry and Burnfield, 2010). Comparing joint kinetics during incline and level walking, Lay et al. (2006) reported increased knee and hip extensor moments in the support phase as well as increased plantar flexor moments in the propulsion phase. The substantially higher muscular demand in incline compared to level walking increases also the external hip and knee adduction moments (Karamanidis and Arampatzis, 2009) indicating a higher need to stabilize the body in the mediolateral direction during incline walking. The main changes in the modular control between incline and level walking have been found during the stance phase (Syn 1 and Syn 2) and in the preparation for the stance phase (Syn 5) indicating that the increased muscular demand was the origin for the alteration in the neural control elements. The alterations were observed in the upper leg muscles for Syn 1 and in the lower leg muscles for Syn 2 likely to generate support and control balance during touchdown and to generate propulsion during the mid-stance and the beginning of the push-off phase. The modifications in Syn 5 indicate a specific to incline walking preparation of the stance phase where the larger motor module in TA presumably reflects the increased activity to lift the tip of the foot higher at the end of swing phase.

14 Conclusion The aim of our study was to investigate the modular control of human locomotion during incline and level walking using the muscle synergy concept. As hypothesized, our results revealed changes in the modular organization of muscle activity during incline compared to level walking. The different modular organization was displayed in changes in the frequency of occurrence of specific muscle synergies as well as changes in the shape of the motor primitives (time-dependent activation patterns) and the motor modules (specific muscle contribution within the respective synergies) both represented by lower R² similarities with respect to the found thresholds. The observed changes are associated with the increased mechanical demand in incline compared to level walking. Our findings provide evidence that the central nervous system flexibly uses a consistent set of neural control elements (e.g. up to five fundamental synergies). A flexible temporal recruitment of motor primitives as well as a flexible modification of the motor modules within each element is used to achieve stable locomotion adapted to the mechanical demand during walking. Although there are several studies investigating clinical outcomes using the muscle synergies concept in the diagnosis and rehabilitation of pathologies as for example stroke (Cheung et al., 2012) and Parkinson s disease (Falaki et al., 2016), a direct translation to training and rehabilitation procedures is still limited. In the future a better understanding of neuromuscular mechanisms through the analysis of muscle synergies may help to reliably identify motor control strategies, which is essential to improve the specificity of training and rehabilitation protocols in healthy and pathological population.

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17 Tables Table 1: Anthropometric data, results for the self-selected speed test and foot contact times during incline and level walking (means and standard deviations) of male [n = 10] and female [n = 10] participants. Total Male Female n Height [cm] 174 ± ± ± 8 Body mass [kg] 69 ± ± 8 60 ± 8 BMI [kg/m 2 ] 22 ± 2 24 ± 2 21 ± 2 Age [years] 29 ± 6 31 ± 7 28 ± 5 Pref. walking speed [m/s] 1.4 ± ± ± 0.2

18 Table 2: Similarities between the incline and level walking condition for both, motor primitives (H) and motor modules (W). The intraday repeatability thresholds are reported for both day 1 and 2 and indicated with the suffixes intra1 and intra2. The interday repeatability thresholds, reported with the suffix inter, are calculated by comparing the average of day 1 (two trials) with the average of day 2 (two trials). Where the number of recognized fundamental primitives was not big enough, missing data were created (here reported as NA ). Incline walking Level walking H W H W intra1 intra2 inter intra1 intra2 inter intra1 intra2 inter intra1 intra2 inter Syn Syn Syn Syn NA NA 0.99 NA NA Syn

19 Table 3: Similarities between incline and level walking for the motor primitives (H) and motor modules (W). The thresholds resulting from the intraday repeatability analysis are indicated with H.th and W.th and are the average of day 1 and day 2 intraday values of both walking conditions reported in Table 2. Smaller values below indicate the standard deviations. Average values lower than the intraday similarity thresholds are highlighted with the symbol *.). Where the number of recognized fundamental primitives was not big enough, missing data were created (here reported as NA ). H H.th W W.th Syn * * Syn * * Syn * Syn 4 NA 0.93 NA NA 0.06 Syn * *

20 Figures Figure 1: Example of two fundamental synergies combined into one. The curves A and B the two respective time normalized primitives and the histograms D and E represent the two fundamental sets of motor modules. The combined motor primitives and modules are shown in figures C and F, respectively. All amplitudes are normalized to one.

21 Figure 2: Individual (gray) and averaged (black) Motor primitives and motor modules during incline (left) and level walking (right) for the five fundamental synergies (Syn 1 to 5, top to bottom). The motor primitives representing the time structure of the muscle synergies are normalized to gait cycles. Percentage values give relative frequencies of appearance of the recognized fundamental synergies across all trials of the respective condition. Motor modules are presented (from left to right) for the upper limb = latissimus dorsi (LD), trapezius (TRS), deltoid anterior (DA) and posterior (DP), biceps (BB) and triceps brachii (TB), the trunk = splenius capitis (SPL), rectus abdominis (RA), obliquii anterior exterior (AEO), erector spinae (ESL1), the upper leg = glutaeus medius (GMED) and maximus (GMAX), rectus femoris (RF) vastus medialis (VM), lateralis (VL), biceps femoris (BFL), semitendinosus (ST), tensor fasciae latae (TFL), adductor longus (AL), and the lower leg = tibialis anterior (TA), gastrocnemius medialis (GM) and lateralis (GL), soleus (SOL) and peronaeus longus (PL). All amplitudes of the primitives and modules are normalized to one. Means were calculated from four trials per walking condition (two repetition trials on each of two measurement days; n = 20).

22 Figure 3: Motor primitives (left) and motor modules (right) of the respective synergies (Syn 1-5) during incline (black) and level (transparent gray) walking. All values are means of four trials per walking condition (two repetition trials on each of two measurement days; n = 20). The motor modules of the single muscles are presented in the same order as named in figure 2. All amplitudes of the primitives and modules are normalized to one.