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313 results for “locomotion”
Assessing Safe Locomotion with Exoskeletons in Realistic Scenarios (SALOEXO)
<p>This dataset includes data from various subjects walking over a treadmill (N-Mill from Motek) with a lower-limb exoskeleton (H3 from Technaid S.L.) with multiple sensors. A further description of the data will be uploaded. It is a result of the SALOEXO project, granted by the European project COVR (grant 779966).</p>
Neuromotor dynamics of human locomotion in challenging settings
<p><strong>Background</strong></p> <p>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.</p> <p><strong>How to use the data set</strong></p> <p>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.</p> <p>The file “participant_data.dat” is available in ASCII and RData (R Found. for Stat. Comp.) format and contains:</p> <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> <p>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”.</p> <p><strong>Old versions not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a></strong></p> <p>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.</p> <p>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”.</p> <p>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.</p> <p>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 R<sup>2</sup> 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 R<sup>2</sup> 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).</p> <p>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).</p> <p>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).</p>
"Planning Hybrid Driving-Stepping Locomotion for Ground Robots in Challenging Environments" video material
<p>"Planning Hybrid Driving-Stepping Locomotion for Ground Robots in Challenging Environments" video material</p>
Data from: Arm waving in stylophoran echinoderms: three-dimensional mobility analysis illuminates cornute locomotion
<p>The locomotion strategies of fossil invertebrates are typically interpreted on the basis of morphological descriptions. However, it has been shown that homologous structures with disparate morphologies in extant invertebrates do not necessarily correlate with differences in their locomotory capability. Here, we present a new methodology for analysing locomotion in fossil invertebrates with a rigid skeleton through an investigation of a cornute stylophoran, an extinct fossil echinoderm with enigmatic morphology that has made its mode of locomotion difficult to reconstruct. We determined the range of motion of a stylophoran arm based on digitized three-dimensional morphology of an early Ordovician form, <i>Phyllocystis crassimarginata</i>. Our analysis showed that efficient arm-forward epifaunal locomotion based on dorsoventral movements, as previously hypothesized for cornute stylophorans, was not possible for this taxon; locomotion driven primarily by lateral movement of the proximal aulacophore was more likely. 3D digital modelling provides an objective and rigorous methodology for illuminating the movement capabilities and locomotion strategies of fossil invertebrates.</p>
Kinematic data and mathematical modeling of sea star locomotion
<p>It is unclear how animals with radial symmetry control locomotion without a brain. Using a combination of experiments, mathematical modeling, and robotics, we tested the extent to which this control emerges in sea stars from the local control of their hundreds of feet and their mechanical interactions with the body. We discovered that these animals (<em>Protoreaster nodosus</em>) compensate for an experimental increase in their submerged weight by recruiting more feet that synchronize in the power stroke of the locomotor cycle. Mathematical modeling replicated this response to loading in the absence of nervous communication and demonstrated how the body weight serves as a regulator of recruitment. We built a robotic sea star with an array of independently-controlled actuators that were also recruited in greater numbers under higher loads due to their collective mechanics. These findings demonstrate that an array of actuators in biological and robotic systems are capable of cooperative transport with dynamic adjustments to loading. This form of distributed control contrasts the conventional view of animal locomotion as governed by the central nervous system and offers inspiration for the design of engineered devices with arrays of actuators.</p>
Cardiopulmonary excercise breath-by-breath data during locomotion at variable speed in 28 healthy young subject
<p>Ergospyrometric evaluations are useful in physio-mechanics of locomotion.</p> <p>This dataset includes the raw ergospyrometric data of 28 young subjects during locomotion at variable speed, walk and run on a treadmill at different speeds and gaits.</p> <p>Characteristics of the experimental group:</p> <p>- gender: 28 males<br> - age: 32. 53 (10.99SD)<br> - height: 175.0 cm (0.008 SD)<br> - weight: 72.96 kg (9.51 SD)</p> <p>Equipments:<br> - Cosmed K5 wearable metabolic analyzer<br> - Software Cosmed Omnia v.1.6.5</p> <p>Experimental design:<br> -The Walking Run Transtition Speed(W-R Ts) were experimentally determined. Each subject was asked to perform 3 trials on a treadmill (GE T2100, General Electric, USA), with a staircase protocol of increasing speeds. The ramp was designed to start at a comfortable walking gait (3.0 km h-1), and to increase the speed by 0.5 km.h-1 each 15 s. When the subject started to run, the ramp was stopped and the speed marked down on a worksheet. The average or the modal transition speed was taken as the Ts of the subject. All the treadmill trials were performed in the Biomechanics Research and Movement Analyses Laboratory (LIBiAM) of the Universidad de la República in Paysandú (Uruguay), at a controlled temperature of 25ºC.</p> <p>The theoretical transition speed tTs was computed according to the Froude number equation (Alexander. 1976): v = (nFr g LL)0.5, where v is the theoretical speed, 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; Alexander, 2003; Bona et al., 2019).</p> <p>Experimental speed ramp:</p> <p>-A personalized ascending and descending speed ramp was designed, centered on the transition speed and ranged from (Ts= Transition speed) Ts-20% to Ts+20%, each step lasting 5 s. Each ramp cycle lasted 50 s, and was repeated 5 times, for a total trial time of 250 s. The trial was repeated twice.</p> <p>Note: not all the subject performed the entire protocol. In particular some data are lacking in track.</p> <p>Cost of Transport Analysis:</p> <p>- The resting O2 (RO2) consumption was computed as the averaged VO2 (ml/min/kg) of the 5' in orthostasis. - The trial O2 (TO2) consumption was computed as the averaged VO2 (ml/min/kg) of the last 2' of each speed trial - The exercise O2 (EO2) consumption was computed as TO2 - RO2 - The trial respiratory quotient (RQ) was computed as the averaged RQ (VCO2/VO2) of the last 2' of each speed trial - The RQ based Energetic Equivalent (EE) to transform mlO2 in Joules was derived from Di Prampero (2015). - The metabolic power (W/kg) was computed as (EO2 * EE) / 60 (remember that W = J/s) - The Cost of transport (J/kg/m) was computed by dividing the metabolic power for the speed (m/s) (Saibene and Minetti, 2003).</p> <p> All the participants signed an informed consent. The protocol was approved by the Ethical Committee of the University (#311170-000921-19).<br> </p> <p>Dataset legend.</p> <p><br> Filename:<br> - Subject ID (S1, S2...)<br> - Contents (Orthostasis, Walk, Run, Skip and speed)<br> - Mode (CPET Breath by breath)<br> - Date and time<br> <br> Columns A to I<br> - General info (sensitive data were deleted)<br> - Speed and gait of the trial<br> - When resting in orthostasis was included, it was marked in green<br> Columns of interest (for the other columns please refer to the Cosmed K5 / Cosmed Omnia manuals)<br> <br> J = Time in sec.<br> O = VO2 oxygen consumption in ml/min<br> P = CO2 carbon dioxide production in ml/min<br> Q = RQ respiratory quotient (VCO2/VO2)<br> V = VO2/kg oxygen consumption per kg (ml/min/kg)<br> AI = Marker: Begin and End of each trial and of the resting in orthostasis have been marked</p> <p>Marked row are in yellow or green</p> <p>AJ-AN = Environmental data<br> BA-BF = GPS data</p> <p><br> </p>
DeepLabCut network trained to track mouse body parts during open field locomotion (top-down view)
<p>DeepLabCut (https://github.com/DeepLabCut/) (Mathis et al., 2018; Nath et al., 2019) was used for tracking body parts of mice in an open field arena or in the rotarod. DeepLabCut 2.1.8.2 (local version on Windows with CPU, using the GUI) and 2.1.10.2 (google colab to train the network) were used using default parameters and the pretrained resnet50 network with imgaug augmentation. Frames were extracted with the k-means method and outlier frames with the jump method. <em>Open field: </em>20 images from 19 videos (10 or 30 fps) were extracted for a total of 380 labeled pictures. 8 body parts (snout, both ears, body center, both side laterals, tail base and tail end) and the 4 corners of the field arena were manually labeled and linked to each other using skeletons. A neural network was trained using these images for 170K iterations. 20 outlier frames were extracted from each video and relabeled. An additional 20 images from 19 videos with different recording conditions were labeled. The network was then refined for 210K iterations (from scratch), yielding a train error of 3.33 pixels and a test error of 8.83 pixels (with a likelihood p-cutoff of 0.6). This process was repeated a second time (using an additional 20 images from 15 new videos) to improve the pixel error; to a final 400 K iterations (train error: 2.65, test error: 3.71). 67 videos from 5 different experiments were analyzed on the final network.<em> </em></p> <p><em>Used to analyze videos for a publication (Labouesse et al., Nature Communications 2023)</em></p>
Sex-specific tuning of modular muscle activation patterns for locomotion in young and older adults
<p>There is increasing evidence that including sex as a biological variable is of crucial importance to promote rigorous, repeatable and reproducible science. In spite of this, the body of literature that accounts for the sex of participants in human locomotion studies is small and often produces controversial results. Here, we investigated the modular organization of muscle activation patterns for human locomotion using the concept of muscle synergies with a double purpose: i) uncover possible sex-specific characteristics of motor control and ii) assess whether these are maintained in older age. We recorded electromyographic activities from 13 ipsilateral muscles of the lower limb in young and older adults of both sexes walking (young and old) and running (young) on a treadmill. The data set obtained from the 215 participants was elaborated through non-negative matrix factorization to extract the time-independent (i.e., motor modules) and time-dependent (i.e., motor primitives) coefficients of muscle synergies. We found sparse sex-specific modulations of motor control. Motor modules showed a different contribution of hip extensors, knee extensors and foot dorsiflexors in various synergies. Motor primitives were wider (i.e., lasted longer) in males in the propulsion synergy for walking (but only in young and not in older adults) and in the weight acceptance synergy for running. Moreover, the complexity of motor primitives was similar in younger adults of both sexes, but lower in older females as compared to older males. In essence, our results revealed the existence of small but defined sex-specific differences in the way humans control locomotion and that these strategies are not entirely maintained in older age.</p> <p>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 code to process the data. In total, 520 trials from 215 participants are included in the supplementary data set.</p> <p>The file “metadata.dat” is available in ASCII format and contains:</p> <ul> <li>Code: the participant’s code</li> <li>Group: the participant's group (G1=young adults, walking; G2=old adults, walking; G3=young adults, running)</li> <li>Sex: the participant’s sex (M or F)</li> <li>Locomotion: the type of locomotion (walking or running)</li> <li>Speed: the speed at which the recordings were conducted in [m/s]</li> <li>Speed_type: the distinction between fixed (decided by the researchers) or preferred (selected by the participant) speed</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> <p>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. Trials are named like “ID0020_M_YOUNG_TW_01,” where the characters “ID0020” indicate the participant number (in this example the 20th), the character “M” indicates the sex, the characters “YOUNG” indicate the age group, the characters “TW” indicate the locomotion type and environment (T=treadmill, W=walking, R=running), and the numbers “01” indicate the trial number.</p> <p><strong>Old versions not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a></strong></p> <p>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 one row 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_ID0020_M_YOUNG_TW_01,” where the characters “CYCLE_TIMES” indicate that the trial contains the gait cycle breakdown times, the characters “ID0020” indicate the participant number (in this example the 20th), the character “M” indicates the sex, the characters “YOUNG” indicate the age group, the characters “TW” indicate the locomotion type and environment (T=treadmill, W=walking, R=running), and the numbers “01” indicate the trial number.</p> <p>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 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. Each trial is saved as an element of a single R list. Trials are named like “RAW_EMG_ID0003_F_OLD_TW_01”, where the characters “RAW_EMG” indicate that the trial contains raw emg data, the characters “ID0003” indicate the participant number (in this example the 3rd), the character “F” indicates the sex, the characters “OLD” indicate the age group, the characters “TW” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number.</p> <p>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”. The latest version of this code can be found at https://github.com/alesantuz/musclesyneRgies.</p>
Muscle activation patterns are more constrained and regular in treadmill than in overground human locomotion
<p>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.</p> <p> </p> <p>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.</p> <p>The file “metadata.dat” is available in ASCII and RData format and contains:</p> <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> <p>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 3<sup>rd</sup>), 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”.</p> <p><strong>Old versions not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a></strong></p> <p>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 20<sup>th</sup>), 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.</p> <p>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 3<sup>rd</sup>), 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”.</p> <p>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 12<sup>th</sup>), 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.</p> <p>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”.</p>
Lower complexity of motor primitives ensures robust control of high-speed human locomotion
<p>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.</p> <p> </p> <p>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.</p> <p>The file “metadata.dat” is available in ASCII and RData format and contains:</p> <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> <p>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”.</p> <p><strong>Old versions not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a></strong></p> <p>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).</p> <p>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”.</p> <p>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.</p> <p>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 <em>k</em> used for the calculations. HFDs are presented as one number (mean HFD of the primitives for that trial), as are the interval times <em>k</em>. 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).</p> <p>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”.</p>
Four videos showing surface-crawling locomotion and pedal surface collection in Lymnaea stagnalis (Lymnaeidae, Gastropoda)
<p> </p> <p>Many freshwater and marine gastropods can glide along the water surface with the sole of the foot facing upward. The force driving this surface-crawling locomotion is generated by the epithelial cilia on the sole, which push a ribbon of mucus produced on the anterior sole backwards into the water. If cilia on the posterior part of the sole stop beating, mucus accumulates on the posterior sole together with particles from the water surface. This so-called pedal surface collection enables gastropods to harvest edible materials from the surface. To perform pedal surface collection, the animals may either float or attach themselves with the posterior tip of the foot to solid substrates such as aquatic plants or aquarium walls.</p> <p>The four videos presented here show surface-crawling locomotion and pedal surface collection in the great pond snail, <em>Lymnaea stagnalis</em> (L., 1758), a common holarctic species. The animals came from a pond in a forest near Frankfurt am Main, west central Germany. Shell lengths of the snails shown range from 31 to 37 mm. Videos were captured with various inexpensive digital cameras without special equipment for illumination etc. Video material was processed with ImageJ (https://imagej.nih.gov/ij/) and QuickTimePro (https://support.apple.com/downloads/quicktime).</p> <p> </p> <p><strong>LYMNAEA-1 surface-crawling locomotion 1:</strong> <em>Lymnaea stagnalis</em> crawls onto the water surface from the wall of the tank it is kept in, glides along the surface, and returns to the tank wall. The animal can be seen breathing, and particles move at constant velocity along the entire sole of the crawling snail. The earliest description of this behavior I know of is found in LISTER (1694: p. 8).</p> <p> </p> <p><strong>LYMNAEA-2 pedal surface collection 1:</strong> (A) <em>Lymnaea stagnalis</em> crawls along the water surface. (B) The floating animal collects mucus and particles from the surface on its posterior sole and (C) eats the accumulated material. This is the behavior BROCKMEIER (1898) called 'Planktonfischen' (plankton fishing).</p> <p> </p> <p><strong>LYMNAEA-3 pedal surface collection 2:</strong> <em>Lymnaea stagnalis</em> attached to plants conducts pedal surface collection. Similar behavior was mentioned by KAISER (1960).</p> <p> </p> <p><strong>LYMNAEA-4 pedal surface collection 3:</strong> <em>Lymnaea stagnalis</em> conducts pedal surface collection in an upright position, being attached to a vertical solid substrate (a flower pot, in this case). Pedal surface collection in this posture has been studied previously in members of the families Ampullariidae (JOHNSON 1952) and Planorbidae (DELIAGINA & ORLOVSKY 1990).</p> <p> </p>
Neural coding in barrel cortex during whisker-guided locomotion
<p>Data accompanying publication at <a href="https://doi.org/10.7554/eLife.12559">https://doi.org/10.7554/eLife.12559</a> and code at <a href="https://doi.org/10.5281/zenodo.2949955">https://doi.org/10.5281/zenodo.2949955</a>. For example usage see the notebooks in the repository.</p> <p>The data is organized according to animal id, `00` - `18`.</p> <p>Animals `00` - `12` are electrophysiology data. Each electrophysiology animal data contains the timestamps of the extracted spikes and various processed tabular data. For usage see the `ephys-traces.ipynb` and `ephys-table.ipynb`. Raw voltage traces are not provided.</p> <p>Animals `13` - `18` are imaging data. Each imaging animal data contains timeseries of extracted calcium transients, pixel-wise regression maps of the field of view and various processed tabular data. For usage see the `imaging-raw.ipynb`, `imaging-traces.ipynb`, and `imaging-traces.ipynb`. Raw imaging movies are not provided.</p>
Dataset for study: Adaptive Hip Exoskeleton Control using Heart Rate Feedback Reduces Oxygen Cost during Ecological Locomotion
<p>This record contains the dataset for a manuscript currently under preparation and submission. See the description PDF file for more details. The information here will be updated according to the progress in the peer review and publication procedure.</p>
Рис. 1. ЭкоΛого-географическая характеристика зоопΛанктона гиΔротермаΛьной зоны оз. Кенон в июΛе 2019 г.: А — зоогеография, Б — местообитание, В — способ переΔвижения, Г — способ питания Fig. 1. Ecological and geographic characteristics of zooplankton in the hydrothermal zone of Lake Kenon in July 2019: А — zoogeography, Б — habitat, В — type of locomotion, Г — type of feeding in Zooplankton Structure And Distribution In The Hydrothermal Zone Of Cooling Reservoirs (Trans-Baikal Territory)
Рис. 1. ЭкоΛого-географическая характеристика зоопΛанктона гиΔротермаΛьной зоны оз. Кенон в июΛе 2019 г.: А — зоогеография, Б — местообитание, В — способ переΔвижения, Г — способ питания Fig. 1. Ecological and geographic characteristics of zooplankton in the hydrothermal zone of Lake Kenon in July 2019: А — zoogeography, Б — habitat, В — type of locomotion, Г — type of feeding
FIGURE 3 in Forelimb motion and orientation in the ornithischian dinosaurs Styracosaurus and Thescelosaurus, and its implications for locomotion and other behavior
FIGURE 3. Stratigraphic distribution of ornithopod and basal ornithischian ichnogenera (after Lockley et al., 2003, 2009; Stanford et al., 2004; Díaz-Martínez et al., 2015; Salisbury et al., 2016), with time-calibrated phylogeny of Ornithopoda (after McDonald, 2012; Dieudonné et al., 2020; Kobayashi et al., 2021). Blue parts of the cladogram and blue manus and pes prints indicate taxa and ichnotaxa with manus enclosed in a mitten-like sheath of soft tissue. Striped blue and black on the cladogram indicates uncertainty: known fossils don't include enough of the manus to determine whether the fingers were enclosed in a mitten-like sheath of soft tissue. The unnamed tracks from Spain are those described by Pérez-Lorente et al. (1997).
FIGURE 1 in Forelimb motion and orientation in the ornithischian dinosaurs Styracosaurus and Thescelosaurus, and its implications for locomotion and other behavior
FIGURE 1. Right pectoral girdle and forelimb bones of the holotype of Styracosaurus albertensis (CMN 344) and motion at the shoulder. A. Right scapula and coracoid in lateral view. B–D. Humerus in lateral (B), posterior (C), and anterior (D) views, with broken white line indicating edge of humeral head. E–F. Radius and ulna in proximal (E) and distal (F) views. G. Range of parasagittal motion at the shoulder in lateral view. H. Range of motion at the shoulder in dorsal view. I. Range of transverse motion at the shoulder in anterior view, with radius and ulna included; the broken line indicates the humerus in the approximate position of full elevation through the transverse plane, and the unbroken line indicates the humerus in the position that was used for photographing it in position 3. J. Range of parasagittal and transverse motion at the shoulder in lateral view, with radius and ulna included. K–M. Fleshed out reconstructions of S. albertensis in anterior view in habitual posture for standing and locomotion (K), in anterior view with forelimbs in sprawling posture (L), and in lateral view with forelimbs in habitual posture for standing and locomotion (M). 1 – 3, positions 1 – 3 (see Materials and Methods for description), c, coracoid; g, glenoid cavity; h, humerus; hh, humeral head; r, radius; s, scapula; u, ulna.
FIGURE 2 in Forelimb motion and orientation in the ornithischian dinosaurs Styracosaurus and Thescelosaurus, and its implications for locomotion and other behavior
FIGURE 2. Right pectoral girdle and forelimb bones of Thescelosaurus sp. (NCSM 15728) and motion at the shoulder. A. Right scapulocoracoid in lateral view. B–D. Humerus in lateral (B), posterior (C), and anterior (D) views, with broken white line indicating edge of humeral head. E–F. Radius and ulna in proximal (E) and medial (F) views. G. Motion at the shoulder in lateral view. H. Transverse motion at the shoulder in anterior view. I–J, Skeletons of Thescelosaurus sp. NCSM 15728 (I) and CMN 8537 (J), showing that the curvature of the anterior dorsal vertebrae positions the forelimb such that it can reach the ground when the sacrum is horizontal. K, Tracing of several of the bones of CMN 8537 (vertebral centra, femur, tibia + fibula + proximal tarsals, metatarsus + distal tarsal, scapulocoracoid, humerus, radius + ulna, and carpus + metacarpus), with corrections of the dislocations at the hip and posterior dorsum in the preserved skeleton, and with heavy lines representing the long axis of the sacrum and the surface of the ground, showing that the forelimb can reach the ground and can also be retracted to avoid the ground during bipedal locomotion. L, Fleshed-out reconstruction of Thescelosaurus sp. posed as in K. 1–3, positions 1–3 (see Materials and Methods for description), c, coracoid; ca, carpals; g, glenoid cavity; h, humerus; hh, humeral head; r, radius; s, scapula; u, ulna.
Dataset for the article: Robotic Feet Modeled After Ungulates Improve Locomotion on Soft Wet Grounds
<div> <div>This repository contains data for three different experiments presented in the paper:</div> <br> <div>(1) moose_feet (40 files): The moose leg experiments are labeled as ax_y.nc,</div> <div>where 'a' indicates attached digits and 'f' indicates free digits. The</div> <div>number 'x' is either 1 (front leg) or 2 (hind leg), and the number 'y'</div> <div>is an increment from 0 to 9 representing the 10 samples of each set.</div> <br> <div>(2) synthetic_feet (120 files): The synthetic feet experiments are labeled</div> <div>as lw_a_y.nc, where 'lw' (Low Water content) can be replaced by 'mw'</div> <div>(Medium Water content) or 'vw' (Vast Water content). The 'a' can be 'o'</div> <div>(Original Go1 foot), 'r' (Rigid extended foot), 'f' (Free digits anisotropic</div> <div>foot), or 'a' (Attached digits). Similar to (1), the last number is an increment from 0 to 9.</div> <br> <div>(3) Go1 (15 files): The locomotion experiments of the quadruped robot on the</div> <div>track are labeled as condition_y.nc, where 'condition' is either 'hard_ground'</div> <div>for experiments on hard ground, 'bioinspired_feet' for the locomotion of the</div> <div>quadruped on mud using bio-inspired anisotropic feet, or 'original_feet' for</div> <div>experiments where the robot used the original Go1 feet. The 'y' is an increment from 0 to 4.</div> <br> <div>The files for moose_feet and synthetic_feet contain timestamp (s), position (m), and force (N) data.</div> <div>The files for Go1 contain timestamp (s), position (rad), velocity (rad/s), torque (Nm) data for all 12 motors, and the distance traveled by the robot (m).</div> <br> <div>All files can be read using xarray datasets (https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html).</div> </div>
Fig. 3 in Tools and Methods to Assess Field Performance Locomotion activity meter for quality assessment of mass-reared sterile male moths (Lepidoptera)
Fig. 3. Mean activity counts for before and afer pheromone exposure (lef), and the mean afer/before activity ratio (right), for un-irradiated (0 Gy = −) and irradiated (300 Gy = +) Epiphyas postvittana males. Error bars are 95% confidence limits for each mean. An afer/before ratio of 1 (marked) indicates an equal level of activity before and afer pheromone exposure.
Fig. 2 in Tools and Methods to Assess Field Performance Locomotion activity meter for quality assessment of mass-reared sterile male moths (Lepidoptera)
Fig. 2. Measured activity of male Epiphyas postvittana as summed counts at 30 s intervals in a locomotor activity monitor, before and afer pheromone stimulus of irradiated and non-irradiated moths.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
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