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16 results for “human locomotion”

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zenodo44/100

Neuromotor dynamics of human locomotion in challenging settings

Background Is the control of movement less stable when we walk or run in challenging settings? Intuitively, one might answer that it is, given that challenging locomotion externally (e.g. rough terrain) or internally (e.g. age-related impairments) makes our movements more unstable. Here, we investigated how young and old humans synergistically activate muscles during locomotion when different perturbation levels are introduced. Of these control signals, called muscle synergies, we analyzed the stability over time and the complexity (or irregularity). Surprisingly, we found that perturbations force the central nervous system to produce muscle activation patterns that are less unstable and less complex. These outcomes show that robust locomotion in challenging settings is achieved by producing less complex control signals which are more stable over time, whereas easier tasks allow for more unstable and irregular control. How to use the data set This supplementary data set contains: a) the metadata with anonymized participant information, b) the raw electromyographic (EMG) data acquired during locomotion, c) the touchdown and lift-off timings of the recorded limb, d) the filtered and time-normalized EMG, e) the muscle synergies extracted via non-negative matrix factorization and f) the code written in R (R Found. for Stat. Comp.) to process the data, including the scripts to calculate the short-term Maximum Lyapunov Exponents (sMLE) and Higuchi's fractal dimension (HFD) of motor primitives. In total, 476 trials from 86 participants are included in the supplementary data set. The file “participant_data.dat” is available in ASCII and RData (R Found. for Stat. Comp.) format and contains: <ul> <li>Code: the participant’s code</li> <li>Experiment: the experimental setup in which the participant was involved (E1 = walking and running, overground and treadmill; E2 = walking and running, even- and uneven-surface; E3 = unperturbed and perturbed walking, young and old)</li> <li>Group: the group to which the participant was assigned (see methods for the details)</li> <li>Sex: the participant’s sex (M or F)</li> <li>Speed: the speed at which the recordings were conducted in [m/s] (two values separated by a comma mean that recordings were done at two different speeds, i.e. walking and running)</li> <li>Age: the participant’s age in years (participants were considered old if older than 65 years, but younger than 80)</li> <li>Height: the participant’s height in [cm]</li> <li>Mass: the participant’s body mass in [kg].</li> </ul> The "RAW_DATA.RData" R list consists of elements of S3 class "EMG", each of which is a human locomotion trial containing cycle segmentation timings and raw electromyographic (EMG) data from 13 muscles of the right-side leg. Cycle times are structured as data frames containing two columns that correspond to touchdown (first column) and lift-off (second column). Raw EMG data sets are also structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. Please note that the running overground trials of participants P0001 and P0009 and the second uneven-surface running trial of participant P0048 consist of 22, 27 and 23 cycles, respectively. All the other trials consist of 30 gait cycles. Trials are named like “P0053_OW_02”, where the characters “P0053” indicate the participant number (in this example the 53rd), the characters “OW” indicate the locomotion type (E1: OW=overground walking, OR=overground running, TW=treadmill walking, TR=treadmill running; E2: EW=even-surface walking, ER=even-surface running, UW=uneven-surface walking, UR=uneven-surface running; E3: NW=normal walking, PW=perturbed walking), and the numbers “02” indicate the trial number (in this case the 2nd). The 10 trials per participant recorded for each overground session (i.e. 10 for walking and 10 for running) were concatenated into one. The filtered and time-normalized EMG data are named, following the same rules, like “FILT_EMG_P0053_OG_02”. Old versions not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a> The files containing the gait cycle breakdown are available in RData (R Found. for Stat. Comp.) format, in the file named “CYCLE_TIMES.RData”. The files are structured as data frames with 30 rows (one for each gait cycle) and two columns. The first column contains the touchdown incremental times in seconds. The second column contains the duration of each stance phase in seconds. Each trial is saved as an element of a single R list. Trials are named like “CYCLE_TIMES_P0020,” where the characters “CYCLE_TIMES” indicate that the trial contains the gait cycle breakdown times and the characters “P0020” indicate the participant number (in this example the 20th). Please note that the overground trials of participants P0001 and P0009 and the second uneven-surface running trial of participant P0048 only contain 22, 27 and 23 cycles, respectively. The files containing the raw, filtered and the normalized EMG data are available in RData (R Found. for Stat. Comp.) format, in the files named “RAW_EMG.RData” and “FILT_EMG.RData”. The raw EMG files are structured as data frames with 30000 rows (one for each recorded data point) and 14 columns. The first column contains the incremental time in seconds. The remaining thirteen columns contain the raw EMG data, named with muscle abbreviations that follow those reported in the methods section. Each trial is saved as an element of a single R list. Trials are named like “RAW_EMG_P0053_OW_02”, where the characters “RAW_EMG” indicate that the trial contains raw emg data, the characters “P0053” indicate the participant number (in this example the 53rd), the characters “OW” indicate the locomotion type (E1: OW=overground walking, OR=overground running, TW=treadmill walking, TR=treadmill running; E2: EW=even-surface walking, ER=even-surface running, UW=uneven-surface walking, UR=uneven-surface running; E3: NW=normal walking, PW=perturbed walking), and the numbers “02” indicate the trial number (in this case the 2nd). The 10 trials per participant recorded for each overground session (i.e. 10 for walking and 10 for running) were concatenated into one. The filtered and time-normalized EMG data is named, following the same rules, like “FILT_EMG_P0053_OG_02”. The files containing the muscle synergies extracted from the filtered and normalized EMG data are available in RData (R Found. for Stat. Comp.) format, in the files named “SYNS_H.RData” and “SYNS_W.RData”. The muscle synergies files are divided in motor primitives and motor modules and are presented as direct output of the factorization and not in any functional order. Motor primitives are data frames with 6000 rows and a number of columns equal to the number of synergies (which might differ from trial to trial) plus one. The rows contain the time-dependent coefficients (motor primitives), one column for each synergy plus the time points (columns are named e.g. “Time, Syn1, Syn2, Syn3”, where “Syn” is the abbreviation for “synergy”). Each gait cycle contains 200 data points, 100 for the stance and 100 for the swing phase which, multiplied by the 30 recorded cycles, result in 6000 data points distributed in as many rows. This output is transposed as compared to the one discussed above to improve user readability. Each set of motor primitives is saved as an element of a single R list. Trials are named like “SYNS_H_P0012_PW_02”, where the characters “SYNS_H” indicate that the trial contains motor primitive data, the characters “P0012” indicate the participant number (in this example the 12th), ), the characters “PW” indicate the locomotion type (see above), and the numbers “02” indicate the trial number (in this case the 2nd). Motor modules are data frames with 13 rows (number of recorded muscles) and a number of columns equal to the number of synergies (which might differ from trial to trial). The rows, named with muscle abbreviations that follow those reported in the methods section, contain the time-independent coefficients (motor modules), one for each synergy and for each muscle. Each set of motor modules relative to one synergy is saved as an element of a single R list. Trials are named like “SYNS_W_P0082_PW_02”, where the characters “SYNS_W” indicate that the trial contains motor module data, the characters “P0082” indicate the participant number (in this example the 82nd) ), the characters “PW” indicate the locomotion type (see above), and the numbers “02” indicate the trial number (in this case the 2nd). Given the nature of the NMF algorithm for the extraction of muscle synergies, the supplementary data set might show non-significant differences as compared to the one used for obtaining the results of this paper. The files containing the sMLE calculated from motor primitives are available in RData (R Found. for Stat. Comp.) format, in the file named “sMLE.RData”. sMLE results are presented in a list of lists containing, for each trial, 1) the divergences, 2) the sMLE, and 3) the value of the R2 between the divergence curve and its linear interpolation made using the specified amount of points. The divergences are presented as a one-dimensional vector. sMLE are one number like the R2 value. Trials are named like “MLE_P0081_EW_01”, where the characters “sMLE” indicate that the trial containss sMLE data, the characters “P0081” indicate the participant number (in this example the 81st) ), the characters “EW” indicate the locomotion type (see above), and the numbers “01” indicate the trial number (in this case the 1st). The files containing the HFD calculated from motor primitives are available in RData (R Found. for Stat. Comp.) format, in the file named “HFD.RData”. HFD results are presented in a list of lists containing, for each trial, 1) the HFD, and 2) the interval time k used for the calculations. HFDs are presented as one number, as are the interval times k. Trials are named like “HFD_P0048_TR_01”, where the characters “HFD” indicate that the trial contains HFD data, the characters “P0048” indicate the participant number (in this example the 48th), the characters “TR” indicate the locomotion type (see above), and the numbers “01” indicate the trial number (in this case the 1st). All the code used for the preprocessing of EMG data, the extraction of muscle synergies, the calculation of sMLE and HFD is available in R (R Found. for Stat. Comp.) format. Explanatory comments are profusely present throughout the scripts (“SYNS.R”, which is the script to extract synergies, “fun_NMF.R”, which contains the NMF function, “sMLE.R”, which is the script to calculate the sMLE of motor primitives, “HFD.R”, which is the script to calculate the HFD of motor primitives, “fun_sMLE.R”, which contains the sMLE function and “fun_HFD.R”, which contains the HFD function).

opencc-by-4.0Sep 2019View details →

zenodo40/100

Muscle activation patterns are more constrained and regular in treadmill than in overground human locomotion

The use of motorized treadmills as convenient tools for the study of locomotion has been in vogue for many decades. However, despite the widespread presence of these devices in many scientific and clinical environments, a full consensus on their validity to faithfully substitute free overground locomotion is still missing. Specifically, little information is available on whether and how the neural control of movement is affected when humans walk and run on a treadmill as compared to overground. Here, we made use of linear and nonlinear analysis tools to extract information from electromyographic recordings during walking and running overground and on an instrumented treadmill. We extracted synergistic activation patterns from the muscles of the lower limb via non-negative matrix factorization. We then investigated how the motor modules (or time-invariant muscle weightings) were used in the two locomotion environments. Subsequently, we examined the timing of motor primitives (or time-dependent coefficients of muscle synergies) by calculating their duration, the time of main activation, and their Hurst exponent, a nonlinear metric derived from fractal analysis. We found that motor modules were not influenced by the locomotion environment, while motor primitives resulted overall more regular in treadmill than in overground locomotion, with the main activity of the primitive for propulsion shifted earlier in time. Our results suggest that the spatial and sensory constraints imposed by the treadmill environment forced the central nervous system to adopt a different neural control strategy than that used for free overground locomotion. A data-driven indication that treadmills induce perturbations to the neural control of locomotion.   In this supplementary data set we made available: a) the metadata with anonymized participant information; b) the raw EMG, already concatenated for the overground trials; c) the touchdown and lift-off timings of the recorded limb, d) the filtered and time-normalized EMG; e) the muscle synergies extracted via NMF; f) the code to process the data. In total, 120 trials from 30 participants are included in the supplementary data set. The file “metadata.dat” is available in ASCII and RData format and contains: <ul> <li>Code: the participant’s code</li> <li>Sex: the participant’s sex (M or F)</li> <li>Locomotion: the type of locomotion (W=walking, R=running)</li> <li>Environment: to distinguish between overground (O) and treadmill (T)</li> <li>Speed: the speed at which the recordings were conducted in [m/s] (1.4 m/s for walking, 2.8 m/s for running)</li> <li>Age: the participant’s age in years</li> <li>Height: the participant’s height in [cm]</li> <li>Mass: the participant’s body mass in [kg].</li> </ul> The "RAW_DATA.RData" R list consists of elements of S3 class "EMG", each of which is a human locomotion trial containing cycle segmentation timings and raw electromyographic (EMG) data from 13 muscles of the right-side leg. Cycle times are structured as data frames containing two columns that correspond to touchdown (first column) and lift-off (second column). Raw EMG data sets are also structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. Please note that the running overground trials of participants P0001, P0007, P0008 and P0009 consist of 21, 29, 29 and 26 cycles, respectively. All the other trials consist of 30 gait cycles. Trials are named like “P0003_OR_01”, where the characters “P0003” indicate the participant number (in this example the 3rd), the characters “OR” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. The filtered and time-normalized emg data are named, following the same rules, like “FILT_EMG_P0003_OR_01”. Old versions not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a> The files containing the gait cycle breakdown are available in RData format, in the file named “CYCLE_TIMES.RData”. The files are structured as data frames with 30 rows (one for each gait cycle) and two columns. The first column contains the touchdown incremental times in seconds. The second column contains the duration of each stance phase in seconds. Each trial is saved as an element of a single R list. Trials are named like “CYCLE_TIMES_P0020_TW_01,” where the characters “CYCLE_TIMES” indicate that the trial contains the gait cycle breakdown times, the characters “P0020” indicate the participant number (in this example the 20th), the characters “TW” indicate the locomotion type and environment (O=overground, T=treadmill, W=walking, R=running), and the numbers “01” indicate the trial number. Please note that the running overground trials of participants P0001, P0007, P0008 and P0009 only contain 21, 29, 29 and 26 cycles, respectively. The files containing the raw, filtered, and the normalized EMG data are available in RData format, in the files named “RAW_EMG.RData” and “FILT_EMG.RData”. The raw EMG files are structured as data frames with 30000 rows (one for each recorded data point) and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with muscle abbreviations that follow those reported above. Each trial is saved as an element of a single R list. Trials are named like “RAW_EMG_P0003_OR_01”, where the characters “RAW_EMG” indicate that the trial contains raw emg data, the characters “P0003” indicate the participant number (in this example the 3rd), the characters “OR” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. The filtered and time-normalized emg data is named, following the same rules, like “FILT_EMG_P0003_OR_01”. The files containing the muscle synergies extracted from the filtered and normalized EMG data are available in RData format, in the file named “SYNS.RData”. Each element of this R list represents one trial and contains the factorization rank (list element named “synsR2”), the motor modules (list element named “M”), the motor primitives (list element named “P”), the reconstructed EMG (list element named “Vr”), the number of iterations needed by the NMF algorithm to converge (list element named “iterations”), and the reconstruction quality measured as the coefficient of determination (list element named “R2”). The motor modules and motor primitives are presented as direct output of the factorization and not in any functional order. Motor modules are data frames with 13 rows (number of recorded muscles) and a number of columns equal to the number of synergies (which might differ from trial to trial). The rows, named with muscle abbreviations that follow those reported above, contain the time-independent coefficients (motor modules M), one for each synergy and for each muscle. Motor primitives are data frames with 6000 rows and a number of columns equal to the number of synergies (which might differ from trial to trial) plus one. The rows contain the time-dependent coefficients (motor primitives P), one column for each synergy plus the time points (columns are named e.g. “time, Syn1, Syn2, Syn3”, where “Syn” is the abbreviation for “synergy”). Each gait cycle contains 200 data points, 100 for the stance and 100 for the swing phase which, multiplied by the 30 recorded cycles, result in 6000 data points distributed in as many rows. This output is transposed as compared to the one discussed in the methods section to improve user readability. Trials are named like “SYNS_ P0012_OW_01”, where the characters “SYNS” indicate that the trial contains muscle synergy data, the characters “P0012” indicate the participant number (in this example the 12th), the characters “OW” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. Given the nature of the NMF algorithm for the extraction of muscle synergies, the supplementary data set might show non-significant differences as compared to the one used for obtaining the results of this paper. All the code used for the pre-processing of EMG data and the extraction of muscle synergies is available in R format. Explanatory comments are profusely present throughout the script “muscle_synergies.R”.

opencc-by-4.0Jul 2020View details →

zenodo40/100

Lower complexity of motor primitives ensures robust control of high-speed human locomotion

Walking and running are mechanically and energetically different locomotion modes. For selecting one or another, speed is a parameter of paramount importance. Yet, both are likely controlled by similar low-dimensional neuronal networks that reflect in patterned muscle activations called muscle synergies. Here, we investigated how humans synergistically activate muscles during locomotion at different submaximal and maximal speeds. We analysed the duration and complexity (or irregularity) over time of motor primitives, the temporal components of muscle synergies. We found that the challenge imposed by controlling high-speed locomotion forces the central nervous system to produce muscle activation patterns that are wider and less complex relative to the duration of the gait cycle. The motor modules, or time-independent coefficients, were redistributed as locomotion speed changed. These outcomes show that robust locomotion control at challenging speeds is achieved by modulating the relative contribution of muscle activations and producing less complex and wider control signals, whereas slow speeds allow for more irregular control.   In this supplementary data set we made available: a) the metadata with anonymized participant information, b) the raw EMG, c) the touchdown and lift-off timings of the recorded limb, d) the filtered and time-normalized EMG, e) the muscle synergies extracted via NMF and f) the code to process the data, including the scripts to calculate the Higuchi's fractal dimension (HFD) of motor primitives. In total, 180 trials from 30 participants are included in the supplementary data set. The file “metadata.dat” is available in ASCII and RData format and contains: <ul> <li>Code: the participant’s code</li> <li>Group: the experimental group in which the participant was involved (G1 = walking and submaximal running; G2 = submaximal and maximal running)</li> <li>Sex: the participant’s sex (M or F)</li> <li>Speeds: the type of locomotion (W for walking or R for running) and speed at which the recordings were conducted in 10*[m/s]</li> <li>Age: the participant’s age in years</li> <li>Height: the participant’s height in [cm]</li> <li>Mass: the participant’s body mass in [kg]</li> <li>PB: 100 m-personal best time (for G2).</li> </ul> The "RAW_DATA.RData" R list consists of elements of S3 class "EMG", each of which is a human locomotion trial containing cycle segmentation timings and raw electromyographic (EMG) data from 13 muscles of the right-side leg. Cycle times are structured as data frames containing two columns that correspond to touchdown (first column) and lift-off (second column). Raw EMG data sets are also structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. Please note that the following trials include less than 30 gait cycles (the actual number shown between parentheses): P16_R_83 (20), P16_R_95 (25), P17_R_28 (28), P17_R_83 (24), P17_R_95 (13), P18_R_95 (23), P19_R_95 (18), P20_R_28 (25), P20_R_42 (27), P20_R_95 (25), P22_R_28 (23), P23_R_28(29), P24_R_28 (28), P24_R_42 (29), P25_R_28 (29), P25_R_95 (28), P26_R_28 (29), P26_R_95 (28), P27_R_28 (28), P27_R_42 (29), P27_R_95 (24), P28_R_28 (29), P29_R_95 (17). All the other trials consist of 30 gait cycles. Trials are named like “P20_R_20,” where the characters “P20” indicate the participant number (in this example the 20th), the character “R” indicate the locomotion type (W=walking, R=running), and the numbers “20” indicate the locomotion speed in 10*m/s (in this case the speed is 2.0 m/s). The filtered and time-normalized emg data is named, following the same rules, like “FILT_EMG_P03_R_30”. Old versions not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a> The files containing the gait cycle breakdown are available in RData format, in the file named “CYCLE_TIMES.RData”. The files are structured as data frames with as many rows as the available number of gait cycles and two columns. The first column named “touchdown” contains the touchdown incremental times in seconds. The second column named “stance” contains the duration of each stance phase of the right foot in seconds. Each trial is saved as an element of a single R list. Trials are named like “CYCLE_TIMES_P20_R_20,” where the characters “CYCLE_TIMES” indicate that the trial contains the gait cycle breakdown times, the characters “P20” indicate the participant number (in this example the 20th), the character “R” indicate the locomotion type (W=walking, R=running), and the numbers “20” indicate the locomotion speed in 10*m/s (in this case the speed is 2.0 m/s). Please note that the following trials include less than 30 gait cycles (the actual number shown between parentheses): P16_R_83 (20), P16_R_95 (25), P17_R_28 (28), P17_R_83 (24), P17_R_95 (13), P18_R_95 (23), P19_R_95 (18), P20_R_28 (25), P20_R_42 (27), P20_R_95 (25), P22_R_28 (23), P23_R_28(29), P24_R_28 (28), P24_R_42 (29), P25_R_28 (29), P25_R_95 (28), P26_R_28 (29), P26_R_95 (28), P27_R_28 (28), P27_R_42 (29), P27_R_95 (24), P28_R_28 (29), P29_R_95 (17). The files containing the raw, filtered and the normalized EMG data are available in RData format, in the files named “RAW_EMG.RData” and “FILT_EMG.RData”. The raw EMG files are structured as data frames with as many rows as the amount of recorded data points and 13 columns. The first column named “time” contains the incremental time in seconds. The remaining 12 columns contain the raw EMG data, named with muscle abbreviations that follow those reported above. Each trial is saved as an element of a single R list. Trials are named like “RAW_EMG_P03_R_30”, where the characters “RAW_EMG” indicate that the trial contains raw emg data, the characters “P03” indicate the participant number (in this example the 3rd), the character “R” indicate the locomotion type (see above), and the numbers “30” indicate the locomotion speed (see above). The filtered and time-normalized emg data is named, following the same rules, like “FILT_EMG_P03_R_30”. The files containing the muscle synergies extracted from the filtered and normalized EMG data are available in RData format, in the files named “SYNS_H.RData” and “SYNS_W.RData”. The muscle synergies files are divided in motor primitives and motor modules and are presented as direct output of the factorisation and not in any functional order. Motor primitives are data frames with 6000 rows and a number of columns equal to the number of synergies (which might differ from trial to trial) plus one. The rows contain the time-dependent coefficients (motor primitives), one column for each synergy plus the time points (columns are named e.g. “time, Syn1, Syn2, Syn3”, where “Syn” is the abbreviation for “synergy”). Each gait cycle contains 200 data points, 100 for the stance and 100 for the swing phase which, multiplied by the 30 recorded cycles, result in 6000 data points distributed in as many rows. This output is transposed as compared to the one discussed in the methods section to improve user readability. Each set of motor primitives is saved as an element of a single R list. Trials are named like “SYNS_H_P12_W_07”, where the characters “SYNS_H” indicate that the trial contains motor primitive data, the characters “P12” indicate the participant number (in this example the 12th), the character “W” indicate the locomotion type (see above), and the numbers “07” indicate the speed (see above). Motor modules are data frames with 12 rows (number of recorded muscles) and a number of columns equal to the number of synergies (which might differ from trial to trial). The rows, named with muscle abbreviations that follow those reported above, contain the time-independent coefficients (motor modules), one for each synergy and for each muscle. Each set of motor modules relative to one synergy is saved as an element of a single R list. Trials are named like “SYNS_W_P22_R_20”, where the characters “SYNS_W” indicate that the trial contains motor module data, the characters “P22” indicate the participant number (in this example the 22nd), the character “W” indicates the locomotion type (see above), and the numbers “20” indicate the speed (see above). Given the nature of the NMF algorithm for the extraction of muscle synergies, the supplementary data set might show non-significant differences as compared to the one used for obtaining the results of this paper. The files containing the HFD calculated from motor primitives are available in RData format, in the file named “HFD.RData”. HFD results are presented in a list of lists containing, for each trial, 1) the HFD, and 2) the interval time k used for the calculations. HFDs are presented as one number (mean HFD of the primitives for that trial), as are the interval times k. Trials are named like “HFD_P01_R_95”, where the characters “HFD” indicate that the trial contains HFD data, the characters “P01” indicate the participant number (in this example the 1st), the character “R” indicates the locomotion type (see above), and the numbers “95” indicate the speed (see above). All the code used for the pre-processing of EMG data, the extraction of muscle synergies and the calculation of HFD is available in R format. Explanatory comments are profusely present throughout the script “muscle_synergies.R”.

opencc-by-4.0Apr 2020View details →

dryad40/100

Data for: Mobility of the human foot's medial arch helps enables upright bipedal locomotion

Developing the ability to habitually walk and run upright on two feet is one of the most significant transformations to have occurred in human evolution. Many musculoskeletal adaptations enabled bipedal locomotion, including dramatic structural changes to the foot and, in particular, the evolution of an elevated medial arch. The foot's arched structure has previously been assumed to play a central role in directly propelling the center of mass forward and upward through leverage about the toes and a spring-like energy recoil. However, it is unclear whether or how the plantarflexion mobility and height of the medial arch support its propulsive lever function. Here we show, using high-speed biplanar x-ray, that regardless of intraspecific differences in medial arch height, arch recoil enables a longer contact time and favorable propulsive conditions at the ankle for walking upright on an extended leg. This mechanism may have helped drive the evolution of the longitudinal arch after our last common ancestor with chimpanzees, who lack this plantarflexion mobility during push-off. We discovered that the generally overlooked navicular-medial cuneiform joint is primarily responsible for arch recoil in human arches, suggesting that future morphological investigations of this joint will provide new interpretations of the fossil record. Our work further suggests that enabling longitudinal arch recoil in footwear and surgical interventions may be critical for maintaining the ankle's natural propulsive ability.

opencc-zeroApr 2023View details →

dryad40/100

Data for: Mobility of the human foot's medial arch helps enables upright bipedal locomotion

Open the record for dataset details and reuse information.

publicApr 2023View details →

zenodo36/100

Human locomotion dataset

The zip file contains 128 npy files. Each npy file is a python numpy array file that contains one dictionary with the following keys: ['info', 'emg_r', 'kinematic_r', 'kinematic_l', 'emg_l']. info: Is a pandas dataframe with the following columns. file_name ⟶ refers to subject ID fs_emg (Hz) ⟶ refers to sample frequency of EMG data fs_kin (Hz) ⟶ refers to sample frequency of kinematic data mass (kg) ⟶ subject mass in kg trailing_leg ⟶ leg used as trailing leg during unilateral skipping. right (r) or left (l) vel (km/h) ⟶ speed on km/h emg_r: List of n elements. n is equivalent to the number of strides registered. Each element of the list present a DataFrame containig 14 EMG signals of right and left legs corresponding to a one stride segmented using the heel strike of the right leg. emg_l: List of n elements. n is equivalent to the number of strides registered. Each element of the list present a DataFrame containig 14 EMG signals of right and left legs corresponding to a one stride segmented using the heel strike of the left leg. kinematic_r: List of n elements. n is equivalent to the number of strides registered. Each element of the list present a DataFrame containig x, y and z coordinates of 18 reflective markers used in a MOCAP system during one stride identified using the heel strike of the right leg. kinematic_l: List of n elements. n is equivalent to the number of strides registered. Each element of the list present a DataFrame containig x, y and z coordinates of 18 reflective markers used in a MOCAP system during one stride identified using the heel strike of the left leg.   <pre><code class="language-python">#example of use data = np.load('IT_R_110.npy',allow_pickle=True,encoding='latin1').item() #load a file emg5r = data['emg_r'][5] #Load all emg data of the 5th stride defined from right leg heel strike kinematic5l = data['kinematic_l'][5] #Load all kinematic data of the 5th stride defined from right leg heel strike</code></pre>

opencc-by-4.0Oct 2020View details →

zenodo36/100

Energetics of human locomotion near the walk-run transition speed.

Energetics of human locomotion near the walk-run transition speed.   This dataset includes the raw metabolic and mechanical  data of 28 young subjects during locomotion at variable speed, walking and running on a treadmill at different speeds and gaits. Characteristics of the experimental group: - Sex: 28 Males - Age: 32. 53 (10.99SD)- height: 175.0 cm (0.008 SD)- weight: 72.96 kg (9.51 SD) Equipment: - Cosmed K5 portable metabolic analyzer- Cosmed Omnia Software v.1.6.5 - Vicon Nexus 2.14 (Vicon Motion Systems Ltd, Oxford, UK) Experimental Design: The walking stroke transition speed (W-R Ts) was determined experimentally. Each subject was asked to perform 3 trials on a treadmill (GE T2100, General Electric, USA), with an escalating speed ladder protocol. The ramp was designed to start with a comfortable ride (3.0 km h-1), and to increase speed by 0.5 km.h-1 every 15 s. When the subject began to run, the ramp stopped and the speed was marked on a worksheet. The mean or modal transition speed was taken as the T of the subject. All treadmill tests were performed at the Biomechanics and Motion Analysis Research Laboratory (LIBiAM) of the University of the Republic in Paysandú (Uruguay), at a controlled temperature of 25ºC. The theoretical transition velocity tTs was calculated according to the Froude number equation (Alexander. 1976): v = (nFr g LL) 0.5, where v is the theoretical velocity, g is gravity, LL is the leg length, and nFr the Froude number, which was set to the constant value of 0.5,  corresponding to the W-R transition (Alexander & Jayes, 1983; Alejandro, 2003; Bona et al., 2019). Experimental speed ramp: -A custom ascending and descending speed ramp was designed, focused on the transition speed and varied from (Ts = Transition Speed) Ts-20% to Ts+20%, each step with a duration of 5 s. Each ramp cycle lasted 50 s, and was repeated 5 times, for a total test time of 250 s. The trial was repeated twice.   Mechanical Work (Mechanical cost of transport) The time course of the trajectory by BcoM was used to infer changes in the mechanical energies (potential and kinetics) involved. The horizontal work (Wh) was defined as the sum of the increments of the kinetic energy of the BcoM along the forward and mediolateral axes; the vertical work (Wv) was determined by the sum of the increments of gravitational potential energy and kinetic energy along the vertical axis; the external work (Wext ), the mechanical work done to lift and accelerate the BcoM, was computed as the sum of the increments of the total mechanical energy of the BcoM (potential plus kinetic) (Cavagna et al., 1976; Willems et al., 1995). The internal work (Wint), the work necessary to accelerate the body segments with respects to the BcoM, was estimated with the methodology proposed by Cavagna & Kaneko (1977).  Wint and Wext were summed to give the total mechanical work (Wtot) (Cavagna & Kaneko, 1977; Willems et al., 1995).  During locomotion cycles, especially in W, part of the potential energy of the BcoM is converted into kinetic energy, and vice versa, so that the sum of Wh and Wv is greater than the actual work done (Wext). The difference, expressed as percentage, corresponds to the energy recovery R% (Cavagna et al., 1976), which formula is: R% = (Wh + Wv - Wext) (Wh + Wv)  Cost of transport (Metabolic transport cost) Oxygen uptake and respiratory quotient were measured breath-by-breath by a portable metabolimeter (K5, Cosmed, Italy). Reference resting values were measured during 5 min in orthostatic quiet position. Each trial was started when the metabolic parameters were near the reference resting values. The 2 (mlO2.kg-1.min-1) and RQ of the last 50 s of each recorded trial, corresponding to the last complete ramp, were averaged. The reference resting 2 was subtracted to the measured one to obtain the net oxygen uptake. VO2NET was then converted to mass-specific metabolic rate (W kg-1) using a RQ based energetic equivalent(P. E. Di Prampero et al., 2015). The C (J kg-1 m-1) was finally obtained by dividing the metabolic rate for the average speed:                  (2)   Apparent Mechanical Efficiency (AE) The AE was calculated as proposed by Cavagna and Kaneko, ie, AE = Wto C where Wtot is the total mechanical work and C the cost of transport (G. A. Cavagna & Kaneko, 1977).   Data processing and calculation Image preprocessing was performed in Vicon Nexus 2.14 (Vicon Motion Systems Ltd, Oxford, UK), kinematic variable calculation performed with Python 2.7 and ProCalc 1.6 (Vicon Motion Systems Ltd, Oxford, UK), the calculation of mechanical variables was implemented in MatLab  (The MathWorks, Inc., California, USA). The calculation of C was performed in Microsoft Excel (Microsoft Office 365).   Note: Not all subjects performed the entire protocol. In particular, some data lack follow-up. Analysis of the cost of transportation: All participants signed an informed consent. The protocol was approved by the University's Ethics Committee (#311170-000921-19).   The legend of the dataset. There are 3 excel sheets  where each row is associated with subjects from 1 to 28. Energy sheet: Subject: Subject Age Weigth Heigth(m) IMC Km x week: kilometers per week Background: history of injuries INT1 km/h: attempt 1 INT2 km/h: attempt 2 INT3 km/h: Attempt 3 Average transition (km/h): average walk-race transition speed Froude estimated PST(m/s) Froude Estimated PST(km/h) Basal Vo2: Basal oxygen consumption  in orthostasis VO2/kg/min: Oxygen consumption in the test RQ: RQ in the test VO2 Net VO2kg/min): VO2 net in the test VO2/kg/s J/kg/s = W/kg C (J/kg/m): transport cost obtained in the test   Test: test performed Gait: type of gait that has been evaluated ASC/DESC: place on the ramp (ascending or descending) Stride: stride identification  for each type of gait Duty Factor_tr: duty factor Stride Frequency_tr: stride frequency Stride Time_tr: stride time Time: time in the stride Speed in treadmill : speed that occurs in treadmill Distance: distance traveled by the stride Step frequency: frequency of passage Wext: External Work Rec: recovery Wv: trabajo vertical Wh: horizontal work WintTOT: Total internal work

opencc-by-4.0Apr 2023View details →

dryad36/100

Data for: Detecting artificially impaired balance in human locomotion: metrics, perturbation effects and detection thresholds

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publicMay 2025View details →

dryad32/100

Data from: The foot is more than a spring: human foot muscles perform work to adapt to the energetic requirements of locomotion

The foot has been considered both as an elastic mechanism that increases the efficiency of locomotion by recycling energy, as well as an energy sink that helps stabilize movement by dissipating energy through contact with the ground. We measured the activity of two intrinsic foot muscles, Flexor Digitorum Brevis (FDB) and Abductor Hallucis (AH), as well as the mechanical work performed by the foot as a whole and at a modelled plantar muscle tendon unit (MTU) to test whether these passive mechanics are actively controlled during stepping. We found that the underlying passive visco-elasticity of the foot is modulated by the muscles of the foot, facilitating both dissipation and generation of energy depending on the mechanical requirements at the center of mass (COM). Compared to level-ground stepping, the foot dissipated and generated an additional –0.2 J/kg and 0.10 J/kg (both P < 0.001) when stepping down and up a 26 cm step respectively, corresponding to 21 % and 10 % of the additional net work performed by the leg on the COM. Of this compensation at the foot, the plantar MTU performed 30 % and 89 % the work for step downs and step ups respectively. This work occurred early in stance and late in stance for stepping down respectively, when the activation levels of FDB and AH were increased between 69 % - 410 % compared to level steps (all P < 0.001). These findings suggest that the energetic function of the foot is actively modulated by the intrinsic foot muscles and may play a significant role in movements requiring large changes in net energy such as stepping on stairs or inclines, accelerating, decelerating, and jumping.

opencc-zeroDec 2018View details →

dryad32/100

Thoracic adaptations for ventilation during locomotion in humans and other mammals

Bipedal humans, like canids and some other cursorial mammals, are thought to have been selected for endurance running, which requires the ability to sustain aerobic metabolism over long distances by inspiring large volumes of air for prolonged periods of time. Here we test the general hypothesis that humans and other mammals selected for vigorous endurance activities evolved derived thoracic features to increase ventilatory capacity. To do so, we investigate whether humans and dogs rely on thoracic motion to increase tidal volume during running to a greater extent than goats, a species that was not selected for endurance locomotion. We found that while all three species use diaphragmatic breathing to increase tidal volume with increasing oxygen demand, humans also use both dorsoventral and mediolateral expansions of the thorax. Dogs use increased dorsoventral expansion of the thorax, representing an intermediate between humans and goats. 3D analyses of joint morphology of 10 species across four mammalian orders also show that endurance-adapted cursorial species independently evolved more concavo-convex costovertebral joint morphologies that allow for increased rib mobility for thoracic expansion. Evidence for similarly derived concavo-convex costovertebral joints in Homo erectus corresponds with other evidence for the evolution of endurance running in the genus Homo.

opencc-zeroOct 2019View details →

ClinicalTrials.gov32/100

Global Positioning Satellite and Accelerometry to Assess Human Locomotion (ACTI GPS)

ClinicalTrials.gov study NCT01805219. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

dryad32/100

Thoracic adaptations for ventilation during locomotion in humans and other mammals

Open the record for dataset details and reuse information.

publicOct 2019View details →

dryad32/100

Data from: The foot is more than a spring: human foot muscles perform work to adapt to the energetic requirements of locomotion

Open the record for dataset details and reuse information.

publicFeb 2019View details →

dryad28/100

Data from: Speed dependency in α-motoneuron activity and locomotor modules in human locomotion: indirect evidence for phylogenetically conserved spinal circuits

Coordinated locomotor muscle activity is generated by the spinal central pattern generators (CPGs). Vertebrate studies have demonstrated the following two characteristics of the speed control mechanisms of the spinal CPGs: (i) rostral segment activation is indispensable for achieving high-speed locomotion; and (ii) specific combinations between spinal interneuronal modules and motoneuron (MN) pools are sequentially activated with increasing speed. Here, to investigate whether similar control mechanisms exist in humans, we examined spinal neural activity during varied-speed locomotion by mapping the distribution of MN activity in the spinal cord and extracting locomotor modules, which generate basic MN activation patterns. The MN activation patterns and the locomotor modules were analysed from multi-muscle electromyographic recordings. The reconstructed MN activity patterns were divided into the following three patterns depending on the speed of locomotion: slow walking, fast walking and running. During these three activation patterns, the proportion of the activity in rostral segments to that in caudal segments increased as locomotion speed increased. Additionally, the different MN activation patterns were generated by distinct combinations of locomotor modules. These results are consistent with the speed control mechanisms observed in vertebrates, suggesting phylogenetically conserved spinal mechanisms of neural control of locomotion.

opencc-zeroDec 2016View details →

dryad28/100

Data from: Speed dependency in α-motoneuron activity and locomotor modules in human locomotion: indirect evidence for phylogenetically conserved spinal circuits

Open the record for dataset details and reuse information.

publicMar 2017View details →

ClinicalTrials.gov24/100

Supraspinal Contributions to the Control of Human Locomotion: Clinical and Fundamental Aspects

ClinicalTrials.gov study NCT02263560. IPD Sharing: Not stated. Countries: 1. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →

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