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Slipping during side-step cutting: Anticipatory effects and familiarization
Human Movement Science 34 (2014) 128–136
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Human Movement Science journal homepage: www.elsevier.com/locate/humov
Slipping during side-step cutting: Anticipatory effects and familiarization Anderson Souza Castelo Oliveira a, Priscila Brito Silva a, Morten Enemark Lund b, Dario Farina c, Uwe Gustav Kersting a,⇑ a
Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark Department of Mechanical and Manufacturing Engineering, Aalborg University, Aalborg, Denmark c Department of Neurorehabilitation Engineering, Bernstein Focus Neurotechnology Göttingen, Bernstein Center for Computational Neuroscience, University Medical Center Göttingen, Georg-August University, Göttingen, Germany b
a r t i c l e
i n f o
Article history: Available online 22 February 2014 PsycINFO classification: 2330 Keywords: Side-step cutting Slips Anticipation EMG Kinematics
a b s t r a c t The aim of the present study was to verify whether the expectation of perturbations while performing side-step cutting manoeuvres influences lower limb EMG activity, heel kinematics and ground reaction forces. Eighteen healthy men performed two sets of 90° side-step cutting manoeuvres. In the first set, 10 unperturbed trials (Base) were performed while stepping over a moveable force platform. In the second set, subjects were informed about the random possibility of perturbations to balance throughout 32 trials, of which eight were perturbed (Pert, 10 cm translation triggered at initial contact), and the others were ‘‘catch’’ trials (Catch). Center of mass velocity (CoMVEL), heel acceleration (HAC), ground reaction forces (GRF) and surface electromyography (EMG) from lower limb and trunk muscles were recorded for each trial. Surface EMG was analyzed prior to initial contact (PRE), during load acceptance (LA) and propulsion (PRP) periods of the stance phase. In addition, hamstrings-quadriceps co-contraction ratios (CCR) were calculated for these time-windows. The results showed no changes in CoMVEL, HAC, peak GRF and surface EMG PRE among conditions. However, during LA, there were increases in tibialis anterior EMG (30–50%) concomitant to reduced EMG for quadriceps muscles, gluteus and rectus abdominis for Catch and Pert conditions (15–40%). In addition, quadriceps EMG was still reduced during PRP (p < .05). Consequently, CCR was greater for Catch and Pert in comparison to Base
⇑ Corresponding author. Address: Center for Sensory-Motor Interaction (SMI), Department of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7 D-3, DK-9220 Aalborg, Denmark. Tel.: +45 99408821; fax: +45 98154008. E-mail address: [email protected] (U.G. Kersting). http://dx.doi.org/10.1016/j.humov.2013.12.009 0167-9457/Ó 2014 Published by Elsevier B.V.
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(p < .05). These results suggest that there is modulation of muscle activity towards anticipating potential instability in the lower limb joints and assure safety to complete the task. Ó 2014 Published by Elsevier B.V.
1. Introduction Human locomotion requires a complex integration of commands from many sources such as the brain, spinal cord, muscles and skin (Cappellini, Ivanenko, Dominici, Poppele, & Lacquaniti, 2010; Lacquaniti, Ivanenko, & Zago, 2012; Rossignol, Dubuc, & Gossard, 2006). Muscle recruitment during locomotion is constantly tuned to assure safe displacement, which relies on superior inputs from the vestibular system and peripheral information regarding muscle tension and joint positioning (Daley & Biewener, 2006; Duysens, Beerepoot, Veltink, Weerdesteyn, & Smits-Engelsman, 2008; Rossignol et al., 2006). In this way, it is possible to also rapidly react to unexpected perturbations to balance while walking or running. Interestingly, there are other features of the central nervous system that may trigger protective motion patterns if locomotion deviates from what is anticipated. For example, Cappellini et al. (2010) have observed substantial changes in the walking pattern of healthy subjects while walking over a slippery surface. In addition, reduced ground reaction force, heel acceleration, flatter foot orientation and more vertical shank positioning are usual modifications of the gait pattern when expecting perturbations (Beschorner & Cham, 2008; Cham & Redfern, 2002; Marigold & Patla, 2002; Oliveira, Farina, & Kersting, 2012). Awareness of perturbations to balance also alters muscular activity (measured by electromyography, EMG) as an attempt to unconsciously provide a more cautious gait pattern, especially after experiencing perturbations (Bunday et al., 2006; Reynolds & Bronstein, 2003). Although there are many protective mechanisms to avoid falls, these features are not sufficient to provide stability and/or protection during some sports gestures such as cutting manoeuvres which are intimately related to knee injuries, especially sprains and ligament ruptures (Alentorn-Geli et al., 2009; Drakos et al., 2010; Zebis, Andersen, Bencke, Kjær, & Aagaard, 2009). Previous investigations have shown that the activation of knee flexor muscles at initial contact may not be fast and strong enough for maintaining joint stability during an injury event (Oliveira, Silva, Lund, Gizzi, et al., 2013; Zebis et al., 2009). In addition, the EMG patterns of activation during unplanned cutting manoeuvres are different from planned tasks (Besier, Lloyd, & Ackland, 2003), suggesting that muscle recruitment to perform cutting manoeuvres is susceptible to alterations on various levels of the neuromuscular system. As a result of this, unexpected changes in the environment during cutting manoeuvres may lead to severe injuries. Indeed, unexpected perturbations to balance while performing cutting manoeuvres may reduce activation of knee flexors and consequently reduce knee stability shortly after perturbation onset (Oliveira, Silva, Lund, Gizzi, et al., 2013). However, little is known about the effects of anticipation on the performance of cutting manoeuvres that might be perturbed. It has been suggested that protective mechanisms to maintain balance are formed by experiencing previous perturbations, allowing optimized proactive strategies for safer displacement (Cappellini et al., 2010; Marigold & Patla, 2002; Oliveira et al., 2012; Parijat & Lockhart, 2011). Therefore, the aim of the present investigation was to verify whether anticipation of perturbations while performing cutting manoeuvres influence lower limb EMG activity, heel kinematics and forces applied to the ground. We hypothesized that specific changes in loading, kinematics and lower limb muscle activity are triggered by expecting perturbations to balance during the execution of side-step cutting movements. The results from this investigation may have implications for the understanding of protective mechanisms during unexpected and expected perturbations to balance while performing sports gestures.
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2. Methods 2.1. Subjects Eighteen healthy men (age: 27 ± 2 yrs; body mass: 70 ± 8 kg; body height: 173 ± 8 cm) volunteered for the experiment. All subjects were recreational practitioners of team sports (soccer, basketball, handball, ice hockey). They had no known history of neurological or motor disorders. All subjects provided written informed consent before participation and the procedures were approved by the ethical committee of Northern Jutland (N-20100042). 2.2. Experimental setup Subjects were asked to perform repeated running trials with a 90° change in direction (side-step cutting manoeuvre) during a single session. The task consisted of running from 6 to 7 m away of a moveable force platform, aiming to step with the right foot onto the plate, turn 90° to the left and continue running. Initially, subjects were familiarized to the side-cutting manoeuvre by performing 10– 15 trials in which the goal was run in a straight line towards the force platform, step with the right foot onto the platform and quickly turn to the left for continuing running. Subsequently, 10 unperturbed cutting manoeuvres were recorded as baseline trials (Base). After this, subjects were informed that the next set of trials would include perturbed trials. In this second set of trials subjects performed 32 cutting manoeuvres, in which 24 were not perturbed (Catch) and eight were perturbed (Pert), and the order of trials was randomized. Throughout the protocol there were 40–60 s rest intervals within each trial to reduce the effects of fatigue, and a longer rest interval (2 min) was offered after each 8th trial. The perturbations consisted of 10-cm translation lasting 150 ms (average speed 66.6 cm/s) in the original running direction (Oliveira, Silva, Lund, Gizzi, et al., 2013). The acceleration phase of such perturbations lasted approximately 80 ms, reaching peak acceleration after 40 ms (5.6 cms 2). The deceleration phase lasted approximately 72 ms, reaching peak deceleration after 35 ms (8.4 cms 2). Subjects wore the same type of court shoes (FZ 2600W, FORZAÒ, Brønderslev, DK) in order to reduce the effects of different footwear on the measurements. The performance of cutting manoeuvres was also evaluated by visual inspection. The successful trials were those in which the subjects performed straight running to approach the force platform, placed the whole foot onto the platform, and were able to exit the platform in a 90° direction illustrated on the floor. Trials that did not satisfy these three requirements were excluded from the analysis. 2.3. Data collection 2.3.1. Kinematics Retroreflective spherical markers were placed bilaterally on the skin overlying the following landmarks: calcaneus, first and fifth metatarso-phalangeal joint, lateral malleolus, lateral condyle; greater trochanter, anterior superior iliac spine, posterior superior iliac spine and acromion. In addition, one marker was placed on the seventh cervical vertebrae, upper and lower endpoint of sternum (suprasternal notch and xiphoid process). Extra markers were placed bilaterally on lower extremity segments: one on the thigh, four on the shank and one on the upper arm, serving as tracking markers to define the three-dimensional (3D) motion. Marker positions were tracked with a motion analysis system with eight infrared digital video cameras (Oqus 300 series, Qualisys, Gothenburg, Sweden). The kinematic data were recorded with a sampling frequency of 256 Hz and synchronized with the EMG and kinetic recordings. Subjects wore full stretch pants covering the EMG cables to minimize movement artifacts. 2.3.2. Kinetics The vertical (Fz), anterior-posterior (Fy) and medial-lateral (Fx) ground reaction forces and the corresponding reaction moments (Mx, My, Mz) were recorded at 1024 Hz by a force platform (AMTI, OR6-5, Watertown, MA) mounted on a hydraulic system (Oliveira, Brito, Farina, & Kersting, 2013; van
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Doornik & Sinkjaer, 2007). Software developed on the Labview platform (MrKick II, Aalborg University, Aalborg, Denmark) was used for data recording. Using a feedback electric circuit, the Fz force also served as trigger to initiate the force plate movement. 2.3.3. Electromyography Surface EMG signals were recorded in bipolar derivations with pairs of Ag/AgCl electrodes (Ambu Neuroline 720 01-K/12; Ambu, Ballerup, Denmark) with 22 mm of center-to-center spacing. Prior to electrode placement the skin was shaved and lightly abraded. The EMG signals were amplified with a gain of 2000 (EMG-USB, LISiN; OT Bioelettronica, Turin, Italy), sampled at 2048 Hz (12 bits/sample). A reference electrode was placed on the right wrist. The EMG signals were recorded from the following muscles of the right side according to the SENIAM recommendations (Hermens, Freriks, DisselhorstKlug, & Rau, 2000): tibialis anterior (TA), gastrocnemius medialis (GM), vastus medialis (VM), vastus lateralis (VL), rectus femoris (RF), long head of biceps femoris (BF), semitendinosus (ST), gluteus maximus (GMA), and rectus abdominis (RAB). 2.4. Data analysis The body of the subjects was modeled as an interconnected chain of rigid body segments: foot, shank, thigh, pelvis, trunk and arms as described by Andersen, Damsgaard, MacWilliams, and Rasmussen (2010). The left initial contact was defined from the foot kinematic data whereas the duration of the stance phase for the right leg was determined by the force plate recordings (when the vertical ground reaction force exceeded 20 N). The body center of mass and subsequent center of mass velocity (CoMVEL) was calculated prior to landing and during the period of contact to the force platform in the AnyBody Modeling System 5.1 (Anybody Technology, Aalborg, Denmark). Kinematic data were low-pass filtered (10 Hz, second-order, zero lag Butterworth) and CoMVEL was computed between 200 ms and 100 ms prior to right foot contact to the force platform in order to determine speed prior to landing (SPD1). In addition, CoMVEL was computed from the last 50 ms of the stance period to determine take-off speed (SPD2). From kinetic data the vertical peak forces (VTPF) and horizontal peak forces in the anterior-posterior direction (APPF) and medial-lateral direction (LAPF) were also recorded during the first half of stance period and normalized to body mass (N/kg). Unloaded platform translations were recorded prior to the experiment, in order to calculate the inertial components of the platform movement. These inertial components were subsequently subtracted from the generated forces during perturbations. The right heel marker was used in order to calculate the vertical heel acceleration (HAC), which has been used as predictor of cautiousness during locomotion analysis (Beschorner & Cham, 2008; Oliveira et al., 2012). Concerning surface EMG, the signals were initially band-pass filtered (second-order, zero lag Butterworth, bandwidth 10–500 Hz) and full-wave rectified prior to further processing. For each muscle the maximum EMG during stance from all trials (including Base, Catch and Pert) was used as an index to normalize across trials. Average EMG amplitude (%maximum) was calculated in three time epochs for all nine muscles: (1) 10 ms before initial contact (Pre-Activation); (2) from 50 to 150 ms after initial contact, related to load acceptance (LA) and (3) a 50-ms time window around the peak CoM power (Oliveira, Silva, Lund, Kersting, & Farina, 2013) during the propulsion phase of cutting manoeuvres (PRP). In addition, co-contraction ratio (CCR) for the relationship between knee flexors and extensors, and between dorsiflexors and plantarflexors was computed for each of the time epochs (Besier et al., 2003). The CCR for knee joint (KCCR) was defined as the average knee flexors EMG activity ((BF + ST)/2) divided by the knee extensors activity ((VM + VL + RF)/3), whereas the CCR for the ankle joint (ACCR) was defined as the TA EMG divided by GM EMG. 2.5. Statistical analysis The effects of perturbation on the dependent variables (VTPF, APPF, LAPF, stance duration, SPD1, SPD2, HAC, Average EMG, KCCR and ACCR) were investigated using one-way ANOVA with Bonferroni correction for multiple comparisons. The significance level was set to p < .05.
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3. Results No changes in stance duration, horizontal anterior-posterior and lateral peak forces, CoM speed prior to landing and at exiting the force platform and vertical heel acceleration were found among Base, Catch and Pert conditions (p > .05, Table 1). No effect of anticipation was found on the iEMG prior to initial contact (Fig. 1A). On the other hand, in the period between 50 and 150 ms after perturbation there was a substantial increase in TA iEMG for Catch and Pert conditions (ANOVA p < .001, corrected p < .05, Fig. 1B). In addition, reductions in iEMG for knee extensors (VL, VM and RF) were observed, as well as for Gmax and RAB for Catch and Pert (ANOVA p < .05, corrected p < .05). During PRP, a reduction in the knee extensors iEMG for Catch and Pert was also verified in comparison to Base (p < .05, Fig. 1C). Co-contraction ratios calculated prior to initial contact (CCR-PRE) were not different among Base, Catch and Pert for both ankle and knee joints (p > .05, Fig. 2). For the ankle joint, ACCR from catch trials and perturbed trials were greater in comparison to Base during LA phase of cutting (ANOVA p < .05, corrected p < .05), with no changes during PRP phase. Concerning the knee joint, KCCR from catch trials was greater than Base during LA phase of cutting (ANOVA p < .01, corrected p < .05). Moreover, KCCR from perturbations were greater than Base for both LA and PRP phases of cutting manoeuvres (ANOVA p < .01, corrected p < .05). 4. Discussion The main finding of the present investigation was that the awareness about possible perturbations to balance while performing cutting manoeuvres influenced muscle activity in different muscular groups depending on the phase of the movement. Increased co-contraction ratios at the ankle and knee joint were induced by the chance of perturbations, as a protective mechanism to assure joint stability in the early phase of the stance period. These results suggest that awareness about perturbations to balance during cutting tasks induces changes in the neuromuscular control of the lower limb muscles to assure an increased safety margin. In practical terms, the presented results show that the modulation of muscle activity due to expectation of environmental change is also found in sports gestures. Changes in ground reaction forces, as well as trunk and limb kinematics are expected during walking, if perturbations to balance are experienced (Cappellini et al., 2010; Cham & Redfern, 2002; Heiden, Sanderson, Inglis, & Siegmund, 2006; Marigold & Patla, 2002). These adaptations result from increased readiness in case of perturbations to balance, as well as some inability to return to a natural behavior after experiencing these perturbations (Reynolds & Bronstein, 2003). Therefore, after experiencing perturbations to balance, adaptations are triggered in subsequent execution of such movements, for a safer completion of the task, which cannot be voluntarily ignored by the subjects (Heiden et al., 2006; Oliveira et al., 2012; Reynolds & Bronstein, 2003). In the present investigation, no changes were found in kinematic and kinetic variables, which can be partially related to the different motor task (side-step cutting), that is performed at a higher speed and subjects did not report fear of performing the task during the randomly perturbed trials. In case of running tasks, no results on anticipatory responses to unpredictable changes in the environment are available for humans. However, Daley and Biewener (2006) have investigated lower limb stability in guinea fowls while running over rough terrains. It was observed that these animals
Table 1 Mean ± standard deviation of stance duration (STC), vertical peak force (VTPF), lateral peak force (LAPF), anterior-posterior peak force (APPF), center of mass speed prior to landing (SPD1) and at take-off (SPD2) and vertical heel acceleration (HAC) during baseline cutting manoeuvres (Base), catch trials (Catch) and perturbed trials (Pert).
Base Catch Pert
STC (ms)
VTPF (Nkg
327.9 ± 53.7 340.4 ± 57.5 333.6 ± 57.8
28.1 ± 6.2 29.5 ± 8.6 30.3 ± 7.7
1
)
LAPF (Nkg 6.9 ± 2.0 6.2 ± 2.2 6.9 ± 2.3
1
)
APPF (Nkg 10.0 ± 3.0 9.5 ± 3.7 11.0 ± 4.3
1
)
SPD1 (ms 2.1 ± 0.3 2.0 ± 0.5 2.1 ± 0.4
1
)
SPD2 (ms 0.83 ± 0.1 0.81 ± 0.2 0.83 ± 0.1
1
)
HAC (ms 15.3 ± 7.5 15.6 ± 6.3 16.6 ± 6.2
2
)
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A − Pre-activation
iEMG (%max)
80
60
40
20
0 TA
GM
VM
iEMG (%max)
RF
BF
ST
GMAX
RAB
B − Load absorption
80
*
60
40
VL
*
*
*
*
*
20
0 TA
GM
VM
VL
RF
BF
ST
GMAX
RAB
ST
GMAX
RAB
C − Propulsion iEMG (%max)
80
*
60
*
*
40
20
0 TA
GM
Base
VM
VL
RF
Catch
BF
Pert
Fig. 1. Mean (SD) integral EMG (iEMG) calculated for the time window 10 ms prior to initial contact (A), from 50 ms to 150 ms after initial contact (B) and during propulsion phase of cutting manoeuvres (C). Baseline trials were performed in the beginning of the protocol (white bars), subsequently the subjects were informed that the next set of trials could present perturbations to balance. Some trials were not perturbed (gray bars) while some other trials had perturbations elicited at initial contact (black bars). ⁄ Denotes significant difference in relation to Catch and Pert (p < .05).
adapted limb contact angle, reduced ground reaction forces and stride length while running over lowfriction surface for these animals for safety reinforcement (Clark & Higham, 2011). The changes in muscle recruitment for safety reinforcement during side-cutting manoeuvres, such as increased CCR, were present whether perturbations occurred or not, as previously verified in human and animal
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5.0
Ankle Co-contraction ratio
CCR (%max)
4.0
*
3.0
*
2.0 1.0 0.0 CCR-PRE 3.0
CCR-LA
CCR-PRP
Knee Co-contraction ratio
CCR (%max)
2.5 2.0 1.5
* *
1.0
*
0.5 0.0 CCR-PRE Base
CCR-LA Catch
CCR-PRP Pert
Fig. 2. Mean (SD) co-contraction ratio calculated for the time window 10 ms prior to initial contact (CCR-PRE), from 50 ms to 150 ms after initial contact (CCR-LA) and during propulsion phase of cutting manoeuvres (CCR-PRP). Co-contraction ratios were calculated during baseline cutting trials (Base), catch trials (Catch) and perturbed trials (Pert). ⁄ Denotes significant difference in relation to Base (p < .05).
locomotion (Clark & Higham, 2011; Heiden et al., 2006). Therefore, the present results corroborate the assumption that pre-established biomechanical experiences of perturbations are used when perturbations are expected/anticipated, as it was the case in this experiment for both catch and perturbed trials. Despite similar ground reaction forces and body displacement, the knowledge that the platform could move during cutting trials made subjects in the present study to adapt their neuromuscular behavior immediately. Muscular activity was changed, especially for the quadriceps muscle group during the stance phase, consequently increasing KCCR and potentially enhancing the stability margins for the knee joint in case of perturbations. In a recent study, we observed that fully unexpected perturbations to balance while performing cutting manoeuvres may reduce hamstrings activity during LA, and therefore the knee joint may present reduced protection from neuromuscular mechanisms (Oliveira, Silva, Lund, Gizzi, et al., 2013). The results of the present investigation demonstrate that in the case of anticipation of perturbations, there is no such reduction in case of similar perturbations, suggesting that it is possible to counteract the effects of these perturbations based on previous knowledge. Further investigations are needed to demonstrate the effects of interventions that enhance postural responses, such as balance training, on the reactive recovery of balance following perturbations during sports gestures. Increased activation of the TA muscle early in the stance phase of cutting manoeuvres could contribute to increase stability at the ankle joint (Neptune, Wright, & Van den Bogert, 1999), being an essential protective adaptation to the expected slips (Eils & Rosenbaum, 2001; Holmes & Delahunt, 2009; Oliveira, Silva, Farina, & Kersting, 2013). This increased stability at the ankle joint could be further confirmed by the ACCR during LA phase. A higher stiffness at the ankle joint during catch and perturbed trials may protect the joint against harmful torques that eventually cause injuries in more
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dangerous perturbed situations. In the same way, reduced EMG activity for the right GMAX and RAB muscles could be associated to specific strategies to diminish muscle power and assure safer completion of the task. The results from the present investigation point out that expectation/anticipation of slips or perturbations during ground contact also modulate neural control during non-linear locomotor task. It is well known that intrinsic mechanisms may regulate biomechanical output when perturbations to balance are expected. Increased responsiveness from sensory receptors certainly play an essential role in altering gait patterns in this case (Cappellini et al., 2010). Therefore, the experience of perturbations while performing cutting manoeuvres may elicit similar responses when compared to normal gait in which neural adjustments are required to modulate muscle recruitment. In conclusion, the expectation of perturbations to balance while performing cutting manoeuvres elicits changes in neuromuscular control that are extended to both perturbed and unperturbed conditions. The neuromuscular strategies to avoid loss of balance during cutting manoeuvres might originate from common pathways as in steady walking, reinforcing the concept of in-built programs that modulate muscle actions to assure safety.
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Human Movement Science journal homepage: www.elsevier.com/locate/humov
Slipping during side-step cutting: Anticipatory effects and familiarization Anderson Souza Castelo Oliveira a, Priscila Brito Silva a, Morten Enemark Lund b, Dario Farina c, Uwe Gustav Kersting a,⇑ a
Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark Department of Mechanical and Manufacturing Engineering, Aalborg University, Aalborg, Denmark c Department of Neurorehabilitation Engineering, Bernstein Focus Neurotechnology Göttingen, Bernstein Center for Computational Neuroscience, University Medical Center Göttingen, Georg-August University, Göttingen, Germany b
a r t i c l e
i n f o
Article history: Available online 22 February 2014 PsycINFO classification: 2330 Keywords: Side-step cutting Slips Anticipation EMG Kinematics
a b s t r a c t The aim of the present study was to verify whether the expectation of perturbations while performing side-step cutting manoeuvres influences lower limb EMG activity, heel kinematics and ground reaction forces. Eighteen healthy men performed two sets of 90° side-step cutting manoeuvres. In the first set, 10 unperturbed trials (Base) were performed while stepping over a moveable force platform. In the second set, subjects were informed about the random possibility of perturbations to balance throughout 32 trials, of which eight were perturbed (Pert, 10 cm translation triggered at initial contact), and the others were ‘‘catch’’ trials (Catch). Center of mass velocity (CoMVEL), heel acceleration (HAC), ground reaction forces (GRF) and surface electromyography (EMG) from lower limb and trunk muscles were recorded for each trial. Surface EMG was analyzed prior to initial contact (PRE), during load acceptance (LA) and propulsion (PRP) periods of the stance phase. In addition, hamstrings-quadriceps co-contraction ratios (CCR) were calculated for these time-windows. The results showed no changes in CoMVEL, HAC, peak GRF and surface EMG PRE among conditions. However, during LA, there were increases in tibialis anterior EMG (30–50%) concomitant to reduced EMG for quadriceps muscles, gluteus and rectus abdominis for Catch and Pert conditions (15–40%). In addition, quadriceps EMG was still reduced during PRP (p < .05). Consequently, CCR was greater for Catch and Pert in comparison to Base
⇑ Corresponding author. Address: Center for Sensory-Motor Interaction (SMI), Department of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7 D-3, DK-9220 Aalborg, Denmark. Tel.: +45 99408821; fax: +45 98154008. E-mail address: [email protected] (U.G. Kersting). http://dx.doi.org/10.1016/j.humov.2013.12.009 0167-9457/Ó 2014 Published by Elsevier B.V.
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(p < .05). These results suggest that there is modulation of muscle activity towards anticipating potential instability in the lower limb joints and assure safety to complete the task. Ó 2014 Published by Elsevier B.V.
1. Introduction Human locomotion requires a complex integration of commands from many sources such as the brain, spinal cord, muscles and skin (Cappellini, Ivanenko, Dominici, Poppele, & Lacquaniti, 2010; Lacquaniti, Ivanenko, & Zago, 2012; Rossignol, Dubuc, & Gossard, 2006). Muscle recruitment during locomotion is constantly tuned to assure safe displacement, which relies on superior inputs from the vestibular system and peripheral information regarding muscle tension and joint positioning (Daley & Biewener, 2006; Duysens, Beerepoot, Veltink, Weerdesteyn, & Smits-Engelsman, 2008; Rossignol et al., 2006). In this way, it is possible to also rapidly react to unexpected perturbations to balance while walking or running. Interestingly, there are other features of the central nervous system that may trigger protective motion patterns if locomotion deviates from what is anticipated. For example, Cappellini et al. (2010) have observed substantial changes in the walking pattern of healthy subjects while walking over a slippery surface. In addition, reduced ground reaction force, heel acceleration, flatter foot orientation and more vertical shank positioning are usual modifications of the gait pattern when expecting perturbations (Beschorner & Cham, 2008; Cham & Redfern, 2002; Marigold & Patla, 2002; Oliveira, Farina, & Kersting, 2012). Awareness of perturbations to balance also alters muscular activity (measured by electromyography, EMG) as an attempt to unconsciously provide a more cautious gait pattern, especially after experiencing perturbations (Bunday et al., 2006; Reynolds & Bronstein, 2003). Although there are many protective mechanisms to avoid falls, these features are not sufficient to provide stability and/or protection during some sports gestures such as cutting manoeuvres which are intimately related to knee injuries, especially sprains and ligament ruptures (Alentorn-Geli et al., 2009; Drakos et al., 2010; Zebis, Andersen, Bencke, Kjær, & Aagaard, 2009). Previous investigations have shown that the activation of knee flexor muscles at initial contact may not be fast and strong enough for maintaining joint stability during an injury event (Oliveira, Silva, Lund, Gizzi, et al., 2013; Zebis et al., 2009). In addition, the EMG patterns of activation during unplanned cutting manoeuvres are different from planned tasks (Besier, Lloyd, & Ackland, 2003), suggesting that muscle recruitment to perform cutting manoeuvres is susceptible to alterations on various levels of the neuromuscular system. As a result of this, unexpected changes in the environment during cutting manoeuvres may lead to severe injuries. Indeed, unexpected perturbations to balance while performing cutting manoeuvres may reduce activation of knee flexors and consequently reduce knee stability shortly after perturbation onset (Oliveira, Silva, Lund, Gizzi, et al., 2013). However, little is known about the effects of anticipation on the performance of cutting manoeuvres that might be perturbed. It has been suggested that protective mechanisms to maintain balance are formed by experiencing previous perturbations, allowing optimized proactive strategies for safer displacement (Cappellini et al., 2010; Marigold & Patla, 2002; Oliveira et al., 2012; Parijat & Lockhart, 2011). Therefore, the aim of the present investigation was to verify whether anticipation of perturbations while performing cutting manoeuvres influence lower limb EMG activity, heel kinematics and forces applied to the ground. We hypothesized that specific changes in loading, kinematics and lower limb muscle activity are triggered by expecting perturbations to balance during the execution of side-step cutting movements. The results from this investigation may have implications for the understanding of protective mechanisms during unexpected and expected perturbations to balance while performing sports gestures.
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2. Methods 2.1. Subjects Eighteen healthy men (age: 27 ± 2 yrs; body mass: 70 ± 8 kg; body height: 173 ± 8 cm) volunteered for the experiment. All subjects were recreational practitioners of team sports (soccer, basketball, handball, ice hockey). They had no known history of neurological or motor disorders. All subjects provided written informed consent before participation and the procedures were approved by the ethical committee of Northern Jutland (N-20100042). 2.2. Experimental setup Subjects were asked to perform repeated running trials with a 90° change in direction (side-step cutting manoeuvre) during a single session. The task consisted of running from 6 to 7 m away of a moveable force platform, aiming to step with the right foot onto the plate, turn 90° to the left and continue running. Initially, subjects were familiarized to the side-cutting manoeuvre by performing 10– 15 trials in which the goal was run in a straight line towards the force platform, step with the right foot onto the platform and quickly turn to the left for continuing running. Subsequently, 10 unperturbed cutting manoeuvres were recorded as baseline trials (Base). After this, subjects were informed that the next set of trials would include perturbed trials. In this second set of trials subjects performed 32 cutting manoeuvres, in which 24 were not perturbed (Catch) and eight were perturbed (Pert), and the order of trials was randomized. Throughout the protocol there were 40–60 s rest intervals within each trial to reduce the effects of fatigue, and a longer rest interval (2 min) was offered after each 8th trial. The perturbations consisted of 10-cm translation lasting 150 ms (average speed 66.6 cm/s) in the original running direction (Oliveira, Silva, Lund, Gizzi, et al., 2013). The acceleration phase of such perturbations lasted approximately 80 ms, reaching peak acceleration after 40 ms (5.6 cms 2). The deceleration phase lasted approximately 72 ms, reaching peak deceleration after 35 ms (8.4 cms 2). Subjects wore the same type of court shoes (FZ 2600W, FORZAÒ, Brønderslev, DK) in order to reduce the effects of different footwear on the measurements. The performance of cutting manoeuvres was also evaluated by visual inspection. The successful trials were those in which the subjects performed straight running to approach the force platform, placed the whole foot onto the platform, and were able to exit the platform in a 90° direction illustrated on the floor. Trials that did not satisfy these three requirements were excluded from the analysis. 2.3. Data collection 2.3.1. Kinematics Retroreflective spherical markers were placed bilaterally on the skin overlying the following landmarks: calcaneus, first and fifth metatarso-phalangeal joint, lateral malleolus, lateral condyle; greater trochanter, anterior superior iliac spine, posterior superior iliac spine and acromion. In addition, one marker was placed on the seventh cervical vertebrae, upper and lower endpoint of sternum (suprasternal notch and xiphoid process). Extra markers were placed bilaterally on lower extremity segments: one on the thigh, four on the shank and one on the upper arm, serving as tracking markers to define the three-dimensional (3D) motion. Marker positions were tracked with a motion analysis system with eight infrared digital video cameras (Oqus 300 series, Qualisys, Gothenburg, Sweden). The kinematic data were recorded with a sampling frequency of 256 Hz and synchronized with the EMG and kinetic recordings. Subjects wore full stretch pants covering the EMG cables to minimize movement artifacts. 2.3.2. Kinetics The vertical (Fz), anterior-posterior (Fy) and medial-lateral (Fx) ground reaction forces and the corresponding reaction moments (Mx, My, Mz) were recorded at 1024 Hz by a force platform (AMTI, OR6-5, Watertown, MA) mounted on a hydraulic system (Oliveira, Brito, Farina, & Kersting, 2013; van
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Doornik & Sinkjaer, 2007). Software developed on the Labview platform (MrKick II, Aalborg University, Aalborg, Denmark) was used for data recording. Using a feedback electric circuit, the Fz force also served as trigger to initiate the force plate movement. 2.3.3. Electromyography Surface EMG signals were recorded in bipolar derivations with pairs of Ag/AgCl electrodes (Ambu Neuroline 720 01-K/12; Ambu, Ballerup, Denmark) with 22 mm of center-to-center spacing. Prior to electrode placement the skin was shaved and lightly abraded. The EMG signals were amplified with a gain of 2000 (EMG-USB, LISiN; OT Bioelettronica, Turin, Italy), sampled at 2048 Hz (12 bits/sample). A reference electrode was placed on the right wrist. The EMG signals were recorded from the following muscles of the right side according to the SENIAM recommendations (Hermens, Freriks, DisselhorstKlug, & Rau, 2000): tibialis anterior (TA), gastrocnemius medialis (GM), vastus medialis (VM), vastus lateralis (VL), rectus femoris (RF), long head of biceps femoris (BF), semitendinosus (ST), gluteus maximus (GMA), and rectus abdominis (RAB). 2.4. Data analysis The body of the subjects was modeled as an interconnected chain of rigid body segments: foot, shank, thigh, pelvis, trunk and arms as described by Andersen, Damsgaard, MacWilliams, and Rasmussen (2010). The left initial contact was defined from the foot kinematic data whereas the duration of the stance phase for the right leg was determined by the force plate recordings (when the vertical ground reaction force exceeded 20 N). The body center of mass and subsequent center of mass velocity (CoMVEL) was calculated prior to landing and during the period of contact to the force platform in the AnyBody Modeling System 5.1 (Anybody Technology, Aalborg, Denmark). Kinematic data were low-pass filtered (10 Hz, second-order, zero lag Butterworth) and CoMVEL was computed between 200 ms and 100 ms prior to right foot contact to the force platform in order to determine speed prior to landing (SPD1). In addition, CoMVEL was computed from the last 50 ms of the stance period to determine take-off speed (SPD2). From kinetic data the vertical peak forces (VTPF) and horizontal peak forces in the anterior-posterior direction (APPF) and medial-lateral direction (LAPF) were also recorded during the first half of stance period and normalized to body mass (N/kg). Unloaded platform translations were recorded prior to the experiment, in order to calculate the inertial components of the platform movement. These inertial components were subsequently subtracted from the generated forces during perturbations. The right heel marker was used in order to calculate the vertical heel acceleration (HAC), which has been used as predictor of cautiousness during locomotion analysis (Beschorner & Cham, 2008; Oliveira et al., 2012). Concerning surface EMG, the signals were initially band-pass filtered (second-order, zero lag Butterworth, bandwidth 10–500 Hz) and full-wave rectified prior to further processing. For each muscle the maximum EMG during stance from all trials (including Base, Catch and Pert) was used as an index to normalize across trials. Average EMG amplitude (%maximum) was calculated in three time epochs for all nine muscles: (1) 10 ms before initial contact (Pre-Activation); (2) from 50 to 150 ms after initial contact, related to load acceptance (LA) and (3) a 50-ms time window around the peak CoM power (Oliveira, Silva, Lund, Kersting, & Farina, 2013) during the propulsion phase of cutting manoeuvres (PRP). In addition, co-contraction ratio (CCR) for the relationship between knee flexors and extensors, and between dorsiflexors and plantarflexors was computed for each of the time epochs (Besier et al., 2003). The CCR for knee joint (KCCR) was defined as the average knee flexors EMG activity ((BF + ST)/2) divided by the knee extensors activity ((VM + VL + RF)/3), whereas the CCR for the ankle joint (ACCR) was defined as the TA EMG divided by GM EMG. 2.5. Statistical analysis The effects of perturbation on the dependent variables (VTPF, APPF, LAPF, stance duration, SPD1, SPD2, HAC, Average EMG, KCCR and ACCR) were investigated using one-way ANOVA with Bonferroni correction for multiple comparisons. The significance level was set to p < .05.
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3. Results No changes in stance duration, horizontal anterior-posterior and lateral peak forces, CoM speed prior to landing and at exiting the force platform and vertical heel acceleration were found among Base, Catch and Pert conditions (p > .05, Table 1). No effect of anticipation was found on the iEMG prior to initial contact (Fig. 1A). On the other hand, in the period between 50 and 150 ms after perturbation there was a substantial increase in TA iEMG for Catch and Pert conditions (ANOVA p < .001, corrected p < .05, Fig. 1B). In addition, reductions in iEMG for knee extensors (VL, VM and RF) were observed, as well as for Gmax and RAB for Catch and Pert (ANOVA p < .05, corrected p < .05). During PRP, a reduction in the knee extensors iEMG for Catch and Pert was also verified in comparison to Base (p < .05, Fig. 1C). Co-contraction ratios calculated prior to initial contact (CCR-PRE) were not different among Base, Catch and Pert for both ankle and knee joints (p > .05, Fig. 2). For the ankle joint, ACCR from catch trials and perturbed trials were greater in comparison to Base during LA phase of cutting (ANOVA p < .05, corrected p < .05), with no changes during PRP phase. Concerning the knee joint, KCCR from catch trials was greater than Base during LA phase of cutting (ANOVA p < .01, corrected p < .05). Moreover, KCCR from perturbations were greater than Base for both LA and PRP phases of cutting manoeuvres (ANOVA p < .01, corrected p < .05). 4. Discussion The main finding of the present investigation was that the awareness about possible perturbations to balance while performing cutting manoeuvres influenced muscle activity in different muscular groups depending on the phase of the movement. Increased co-contraction ratios at the ankle and knee joint were induced by the chance of perturbations, as a protective mechanism to assure joint stability in the early phase of the stance period. These results suggest that awareness about perturbations to balance during cutting tasks induces changes in the neuromuscular control of the lower limb muscles to assure an increased safety margin. In practical terms, the presented results show that the modulation of muscle activity due to expectation of environmental change is also found in sports gestures. Changes in ground reaction forces, as well as trunk and limb kinematics are expected during walking, if perturbations to balance are experienced (Cappellini et al., 2010; Cham & Redfern, 2002; Heiden, Sanderson, Inglis, & Siegmund, 2006; Marigold & Patla, 2002). These adaptations result from increased readiness in case of perturbations to balance, as well as some inability to return to a natural behavior after experiencing these perturbations (Reynolds & Bronstein, 2003). Therefore, after experiencing perturbations to balance, adaptations are triggered in subsequent execution of such movements, for a safer completion of the task, which cannot be voluntarily ignored by the subjects (Heiden et al., 2006; Oliveira et al., 2012; Reynolds & Bronstein, 2003). In the present investigation, no changes were found in kinematic and kinetic variables, which can be partially related to the different motor task (side-step cutting), that is performed at a higher speed and subjects did not report fear of performing the task during the randomly perturbed trials. In case of running tasks, no results on anticipatory responses to unpredictable changes in the environment are available for humans. However, Daley and Biewener (2006) have investigated lower limb stability in guinea fowls while running over rough terrains. It was observed that these animals
Table 1 Mean ± standard deviation of stance duration (STC), vertical peak force (VTPF), lateral peak force (LAPF), anterior-posterior peak force (APPF), center of mass speed prior to landing (SPD1) and at take-off (SPD2) and vertical heel acceleration (HAC) during baseline cutting manoeuvres (Base), catch trials (Catch) and perturbed trials (Pert).
Base Catch Pert
STC (ms)
VTPF (Nkg
327.9 ± 53.7 340.4 ± 57.5 333.6 ± 57.8
28.1 ± 6.2 29.5 ± 8.6 30.3 ± 7.7
1
)
LAPF (Nkg 6.9 ± 2.0 6.2 ± 2.2 6.9 ± 2.3
1
)
APPF (Nkg 10.0 ± 3.0 9.5 ± 3.7 11.0 ± 4.3
1
)
SPD1 (ms 2.1 ± 0.3 2.0 ± 0.5 2.1 ± 0.4
1
)
SPD2 (ms 0.83 ± 0.1 0.81 ± 0.2 0.83 ± 0.1
1
)
HAC (ms 15.3 ± 7.5 15.6 ± 6.3 16.6 ± 6.2
2
)
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A − Pre-activation
iEMG (%max)
80
60
40
20
0 TA
GM
VM
iEMG (%max)
RF
BF
ST
GMAX
RAB
B − Load absorption
80
*
60
40
VL
*
*
*
*
*
20
0 TA
GM
VM
VL
RF
BF
ST
GMAX
RAB
ST
GMAX
RAB
C − Propulsion iEMG (%max)
80
*
60
*
*
40
20
0 TA
GM
Base
VM
VL
RF
Catch
BF
Pert
Fig. 1. Mean (SD) integral EMG (iEMG) calculated for the time window 10 ms prior to initial contact (A), from 50 ms to 150 ms after initial contact (B) and during propulsion phase of cutting manoeuvres (C). Baseline trials were performed in the beginning of the protocol (white bars), subsequently the subjects were informed that the next set of trials could present perturbations to balance. Some trials were not perturbed (gray bars) while some other trials had perturbations elicited at initial contact (black bars). ⁄ Denotes significant difference in relation to Catch and Pert (p < .05).
adapted limb contact angle, reduced ground reaction forces and stride length while running over lowfriction surface for these animals for safety reinforcement (Clark & Higham, 2011). The changes in muscle recruitment for safety reinforcement during side-cutting manoeuvres, such as increased CCR, were present whether perturbations occurred or not, as previously verified in human and animal
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5.0
Ankle Co-contraction ratio
CCR (%max)
4.0
*
3.0
*
2.0 1.0 0.0 CCR-PRE 3.0
CCR-LA
CCR-PRP
Knee Co-contraction ratio
CCR (%max)
2.5 2.0 1.5
* *
1.0
*
0.5 0.0 CCR-PRE Base
CCR-LA Catch
CCR-PRP Pert
Fig. 2. Mean (SD) co-contraction ratio calculated for the time window 10 ms prior to initial contact (CCR-PRE), from 50 ms to 150 ms after initial contact (CCR-LA) and during propulsion phase of cutting manoeuvres (CCR-PRP). Co-contraction ratios were calculated during baseline cutting trials (Base), catch trials (Catch) and perturbed trials (Pert). ⁄ Denotes significant difference in relation to Base (p < .05).
locomotion (Clark & Higham, 2011; Heiden et al., 2006). Therefore, the present results corroborate the assumption that pre-established biomechanical experiences of perturbations are used when perturbations are expected/anticipated, as it was the case in this experiment for both catch and perturbed trials. Despite similar ground reaction forces and body displacement, the knowledge that the platform could move during cutting trials made subjects in the present study to adapt their neuromuscular behavior immediately. Muscular activity was changed, especially for the quadriceps muscle group during the stance phase, consequently increasing KCCR and potentially enhancing the stability margins for the knee joint in case of perturbations. In a recent study, we observed that fully unexpected perturbations to balance while performing cutting manoeuvres may reduce hamstrings activity during LA, and therefore the knee joint may present reduced protection from neuromuscular mechanisms (Oliveira, Silva, Lund, Gizzi, et al., 2013). The results of the present investigation demonstrate that in the case of anticipation of perturbations, there is no such reduction in case of similar perturbations, suggesting that it is possible to counteract the effects of these perturbations based on previous knowledge. Further investigations are needed to demonstrate the effects of interventions that enhance postural responses, such as balance training, on the reactive recovery of balance following perturbations during sports gestures. Increased activation of the TA muscle early in the stance phase of cutting manoeuvres could contribute to increase stability at the ankle joint (Neptune, Wright, & Van den Bogert, 1999), being an essential protective adaptation to the expected slips (Eils & Rosenbaum, 2001; Holmes & Delahunt, 2009; Oliveira, Silva, Farina, & Kersting, 2013). This increased stability at the ankle joint could be further confirmed by the ACCR during LA phase. A higher stiffness at the ankle joint during catch and perturbed trials may protect the joint against harmful torques that eventually cause injuries in more
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dangerous perturbed situations. In the same way, reduced EMG activity for the right GMAX and RAB muscles could be associated to specific strategies to diminish muscle power and assure safer completion of the task. The results from the present investigation point out that expectation/anticipation of slips or perturbations during ground contact also modulate neural control during non-linear locomotor task. It is well known that intrinsic mechanisms may regulate biomechanical output when perturbations to balance are expected. Increased responsiveness from sensory receptors certainly play an essential role in altering gait patterns in this case (Cappellini et al., 2010). Therefore, the experience of perturbations while performing cutting manoeuvres may elicit similar responses when compared to normal gait in which neural adjustments are required to modulate muscle recruitment. In conclusion, the expectation of perturbations to balance while performing cutting manoeuvres elicits changes in neuromuscular control that are extended to both perturbed and unperturbed conditions. The neuromuscular strategies to avoid loss of balance during cutting manoeuvres might originate from common pathways as in steady walking, reinforcing the concept of in-built programs that modulate muscle actions to assure safety.
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