Fig 1.
Illustration of experimental design involving treadmill (blue) and overground running (red) from a representative subject. Tibia vertical acceleration used to identify gait cycles, and surface EMG are plotted in (A) (raw data for acceleration, envelope for sEMG). The gait cycles were defined from one left foot strike to the following left foot strike. The motor modules extracted from the concatenated surface EMG are represented as “muscle weightings” (panel B) and “activation signals “(panel C), in this case 5 consecutive gait cycles are reported. It is worth noting that by concatenating several gait cycles we accounted the inter-cycle variability in the analysis.
Fig 2.
Schematic representation of EMG data processing.
EMG data from treadmill (TRD) and overground running (OVG) were band-pass filtered, low-pass filtered and segmented into (20) running cycles. These segmented data were processed for the extraction of peak EMG and integrated EMG. In addition, the filtered and segmented data were processed using non-negative matrix factorization (NMF) in order to extract muscle weightings and respective activation signals for TRD and OVG separately. We reconstructed the original EMG from OVG (OVG EMG) by mixing muscle weightings and activation signals from TRD condition, generating a reconstructed OVG-RECWA. A second mixed model was the reconstruction of the original EMG from OVG by mixing TRD muscle weightings and OVG activation signals, generating a reconstructed OVG-RECW. The third mixed model was the reconstruction of the original EMG from OVG by mixing OVG muscle weightings and TRD activation signals, generating a reconstructed OVG-RECA. This same procedures were applied for the reconstruction of the original EMG from TRD.
Table 1.
Running temporal parameters and vertical tibial acceleration.
* denotes significant difference in relation to TRD running (p<0.05).
Fig 3.
EMG Comparison of treadmill vs overground.
Percentage of change between treadmill (TRD) and overground (OVG) running for the peak EMG (A) and integrated EMG (B) throughout a gait cycle. Data from OVG running were normalized by the values from TRD running. * denotes significant differences between treadmill and overground running (p<0.05).
Fig 4.
Reconstruction quality of surface EMG signals (variation accounted for—VAF) by means of different number of motor modules (A) for each subject separately. The horizontal solid line in each panel corresponds to VAF at 0.9, and the vertical solid line correspond to the extraction of four motor modules. Mean±SD VAF for each condition and EMG processing method is displayed in the panels. We did not observe differences between locomotion conditions (treadmill vs overground running). In B, motor modules extracted from 20 consecutive gait cycles from treadmill running (blue) and overground running (red) are displayed for all subjects (individual bars of the muscle weightings and lines of the activation signals).
Fig 5.
A: inter-subject similarities (Mean±SD) across the four identified muscle weightings and activation signals during treadmill running (grey) and overground running (black). B: intra-subject similarities between treadmill and overground running. * denotes significant difference in relation to overground running.
Fig 6.
Modular organization—treadmill vs overground.
A: Mean (black line) and SD (gray area) absolute subtraction of treadmill (TRD) from overground (OVG) activation signals. B: mean (SD) timing of the peak amplitude of activation signals from TRD and OVG running for each motor module (from M1 to M4). C: mean (SD) peak amplitude of activation signals for each motor module (from M1 to M4). D: reconstruction quality of multi-muscle EMG datasets from different combinations of muscle weightings and activation signals (please refer to Methods for explanation). † denotes significant difference in relation to TRD running (p<0.05). * denotes significant difference in relation to Regular and RECW only (p<0.001).