The preferred movement path paradigm: influence of running shoes on joint movement

PURPOSE: (a) to quantify differences in lower extremity joint kinematics for groups of runners subjected to different running footwear conditions, and (b) to quantify differences in lower extremity joint kinematics on an individual basis for runners subjected to different running footwear conditions. METHODS: Three-dimensional ankle and knee joint kinematics were collected for 35 heel-toe runners when wearing three different running shoes and when running barefoot. Absolute mean differences in ankle and knee joint kinematics were computed between running shoe conditions. The percentage of individual runners who displayed differences below a 2°, 3° and 5° threshold were also calculated. RESULTS: The results indicate that the mean kinematics of the ankle and knee joints were similar between running shoe conditions. Aside from ankle dorsi-flexion and knee flexion, the percentage of runners maintaining their movement path between running shoes (i.e. less than 3°) was in the order of magnitude of about 80 to 100%. Many runners showed ankle and knee joint kinematics that differed between a conventional running shoe and barefoot by more than 3°, especially for ankle dorsiflexion and knee flexion CONCLUSION: Many runners stay in the same movement path (the preferred movement path) when running in various different footwear conditions. The percentage of runners maintaining their preferred movement path depends on the magnitude of the change introduced by the footwear condition

Task-Dependent Intermuscular Motor Unit Synchronization between Medial and Lateral Vastii Muscles during Dynamic and Isometric Squats

<div><p>Purpose</p><p>Motor unit activity is coordinated between many synergistic muscle pairs but the functional role of this coordination for the motor output is unclear. The purpose of this study was to investigate the short-term modality of coordinated motor unit activity–the synchronized discharge of individual motor units across muscles within time intervals of 5ms–for the Vastus Medialis (VM) and Lateralis (VL). Furthermore, we studied the task-dependency of intermuscular motor unit synchronization between VM and VL during static and dynamic squatting tasks to provide insight into its functional role.</p><p>Methods</p><p>Sixteen healthy male and female participants completed four tasks: Bipedal squats, single-leg squats, an isometric squat, and single-leg balance. Monopolar surface electromyography (EMG) was used to record motor unit activity of VM and VL. For each task, intermuscular motor unit synchronization was determined using a coherence analysis between the raw EMG signals of VM and VL and compared to a reference coherence calculated from two desynchronized EMG signals. The time shift between VM and VL EMG signals was estimated according to the slope of the coherence phase angle spectrum.</p><p>Results</p><p>For all tasks, except for singe-leg balance, coherence between 15–80Hz significantly exceeded the reference. The corresponding time shift between VM and VL was estimated as 4ms. Coherence between 30–60Hz was highest for the bipedal squat, followed by the single-leg squat and the isometric squat.</p><p>Conclusion</p><p>There is substantial short-term motor unit synchronization between VM and VL. Intermuscular motor unit synchronization is enhanced for contractions during dynamic activities, possibly to facilitate a more accurate control of the joint torque, and reduced during single-leg tasks that require balance control and thus, a more independent muscle function. It is proposed that the central nervous system scales the degree of intermuscular motor unit synchronization according to the requirements of the movement task at hand.</p></div

A wavelet based time frequency analysis of electromyograms to group steps of runners into clusters that contain similar muscle activation patterns

<div><p>Purpose</p><p>To wavelet transform the electromyograms of the vastii muscles and generate wavelet intensity patterns (WIP) of runners. Test the hypotheses: 1) The WIP of the vastus medialis (VM) and vastus lateralis (VL) of one step are more similar than the WIPs of these two muscles, offset by one step. 2) The WIPs within one muscle differ by having maximal intensities in specific frequency bands and these intensities are not always occurring at the same time after heel strike. 3) The WIPs that were recorded form one muscle for all steps while running can be grouped into clusters with similar WIPs. It is expected that clusters might have distinctly different, cluster specific mean WIPs.</p><p>Methods</p><p>The EMG of the vastii muscles from at least 1000 steps from twelve runners were recorded using a bipolar current amplifier and yielded WIPs. Based on the weights obtained after a principal component analysis the dissimilarities (1-correlation) between the WIPs were computed. The dissimilarities were submitted to a hierarchical cluster analysis to search for groups of steps with similar WIPs. The clusters formed by random surrogate WIPs were used to determine whether the groups were likely to be created in a non-random manner.</p><p>Results</p><p>The steps were grouped in clusters showing similar WIPs. The grouping was based on the frequency bands and their timing showing that they represented defining parts of the WIPs. The correlations between the WIPs of the vastii muscles that were recorded during the same step were higher than the correlations of WPIs that were recorded during consecutive steps, indicating the non-randomness of the WIPs.</p><p>Conclusions</p><p>The spectral power of EMGs while running varies during the stance phase in time and frequency, therefore a time averaged power spectrum cannot reflect the timing of events that occur while running. It seems likely that there might be a set of predefined patterns that are used upon demand to stabilize the movement.</p></div

Overall EMG Intensity of VM and VL for each task.

<p>Graph shows mean values (+SE) normalized to the SLS across 16 participants.</p

Movement tasks.

<p>Single-leg balance (SLB) (a), isometric squat (ISO) (b), single-leg squat (SLS) (c), bipedal squat (BPS) (d).</p

Procedure to separate individual data sequences according to peaks in knee flexion angle.

<p>Example for a bipedal squat (top panel) and corresponding isometric squat (bottom panel). Dashed vertical lines to the left and right of peaks in the knee flexion angle trace indicate the boundaries of individual data sequences (shaded) that were used for further analysis.</p

Phase angle between VM and VL EMG currents.

<p>Mean phase angle spectra across 16 participants for each task.</p

Task-dependent coherence between VM and VL EMG currents.

<p>Mean coherence spectra (+SE) (a) and coherence of interest (±SE) (b) across 16 participants for each task; asterisks indicate significant differences at α = 0.05.</p

Coordinated oscillations in the global muscle activation intensity of VM and VL.

<p>Average coherence spectra (+SE) between total intensities of VM and VL during a bipedal squat (BPS) of one representative participant compared to the reference coherence (REF) (a); total intensities (b) and intensity patterns (c) of VL and VM during one bipedal squat.</p

Decay of coherence of interest (<i>CoI</i>) with increasing time shift during a bipedal squat.

<p>Decay of coherence of interest (<i>CoI</i>) with increasing time shift during a bipedal squat.</p